UNIVERSITE LIBRE DE BRUXELLES
Faculty of Medicine
Laboratory of Cancer Epigenetics
Mining breast cancers with
novel epigenetic modifications
Evelyne COLLIGNON
A thesis submitted for the degree of Doctor of Philosophy
in Biomedical and Pharmaceutical Sciences
Ph.D Thesis Director: Dr. François Fuks
2017
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“Nothing in life is to be feared, it is only to be understood. Now is the time to understand
more, so that we may fear less.”
Marie Curie
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Résumé
Dans le domaine de l’épigénétique, la méthylation de l’ADN et les modifications
des histones ont longtemps focalisé l’attention de la recherche biologique et médicale.
Toutefois, notre vision de l’épigénétique s’est fortement élargie avec la découverte de
« nouvelles » modifications épigénétiques de l’ADN et de l’ARN. Dès lors, au cours de
cette thèse, nous avons voulu explorer trois de ces modifications, et leurs enzymes, dans
le cadre des cancers du sein.
Premièrement, nous avons étudié l’enzyme TET1, responsable de
l’hydroxyméthylation de l’ADN (5hmC). Nous avons découvert que le niveau
d’expression de TET1 dans les tumeurs mammaires basal-like corrélait avec les
changements de 5hmC par rapport au tissu sain. Nous avons aussi établi un lien inédit
entre la répression de TET1 et l’infiltration immune dans ces cancers. Nous avons ensuite
démontré que cette répression était liée à l’activation canonique de NF-κB, un régulateur
majeur de l’immunité et l’inflammation. Enfin, nous avons étendu ce nouveau mode de
régulation de TET1 par l’immunité à d’autres types de cancers, y compris le mélanome,
le cancer du poumon et le cancer de la thyroïde.
Dans la deuxième partie de cette thèse, nous avons effectué la première étude
transcriptomique d’une nouvelle modification de l’ARN, l’hydroxyméthylation de l’ARN
(5hmrC). Chez la drosophile, nous avons révélé la distribution de cette marque le long du
transcriptome, associé une fonction régulatrice de la traduction protéique à la marque et
révélé le rôle central de 5hmrC et dTet, l’enzyme responsable de sa formation, dans le
développement du système nerveux central. A la suite de cette étude pionnière, nous
avons investigué 5hmrC en lignées mammaires et nous avons découvert plus de 700
ARNs différentiellement hydroxyméthylés dans les cellules cancéreuses par rapport aux
cellules mammaires normales. Globalement, nos résultats indiquent que 5hmrC constitue
un nouveau niveau de dérégulation de la fonction des gènes dans les cancers du sein.
Dans la dernière partie, nous avons examiné le rôle de la méthylation de l’ARN
(m6A) dans les cancers mammaires. Nous avons cartographié la distribution de m6A en
lignées et en tissus humains et identifié près de 2000 ARNs différentiellement méthylés
dans les cellules cancéreuses. Ensuite, nous avons découvert qu’une déméthylase de
l’ARN, FTO, était sous-exprimée dans les cancers du sein, et nous avons démontré que
cette dérégulation s’accompagnait d’une augmentation globale de m6A et d’un mauvais
pronostic de survie chez les patients. In vitro, la sous-expression de FTO cause un
phénotype plus agressif en termes de migration, invasion et caractère souche des cellules
cancéreuses mammaires. Ainsi, nos résultats semblent assigner une fonction suppressive
de tumeur à FTO dans la glande mammaire.
En conclusion, nos résultats démontrent l’importance biologique des
« nouvelles » modifications de l’ADN et l’ARN et de leur dérégulation dans le
développement des tumeurs mammaires. Nos données mettent au jour de nouveaux
mécanismes par lesquels l’épigénétique contribue au processus de cancérogénèse et
indiquent que l’étude de ces modifications pourrait avoir une utilité clinique.
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Acknowledgements
First, I would like to thank my promoter, François Fuks, for giving me the
opportunity to pursue my PhD thesis in his team and work on exciting new themes of
research. Our many discussions, and sometimes disagreements, help me evolve
scientifically and develop a critical spirit. You also encouraged me and supported me in
my choices for the future, which I appreciated very much.
I would like also to thank all the members of the Laboratory of Cancer
Epigenetics, past and present, who shared this epic journey with me. First of all, Sarah,
who guided my first steps in the lab. Then, of course, Emilie, my “lab mom”, who was
always so helpful throughout the years (and who has the patience of a saint). Clémence,
my padawan, whom I could share my geekiness with (but not coffee). Jana, for all our
brainstorming and discussions on Schrödinger's Cat, among many topics. Martin, our dear
bioinformatician, who taught me some basic notions of R despite myself. Eric, our
resident star singer (and honorary member of the geek team). Pascale, whose knowledge
of good old classics awed us all. Princess Bouchra, who always stayed calm during the
storm. Iolanda, who listened to all of us and always supported us like a kindergarten
teacher. Rachel, who knew how to cheer us up with good music and cocktails. And all
the other members of the lab whom I had the chance to meet and share a good laugh with:
Elise, Laurence, Olivier, Christelle, Nick, Benjamin, Micha, Gordana, Jie, Matthieu,
Romy, Nitesh, Audrey, Thibaud, Andrea and Valentina.
I wish to thank all of our collaborators from the ULB and ULg, with a special
thought for Annalisa who worked by my side for four years, as well as the organisms
supporting our research: the FNRS, the l’Oréal Foundation, the Télévie, the IUAP and
the Walloon Region.
And finally, my family and my friends, who were ever present and supporting in
so many ways. Thank you.
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Table of contents
Abbreviations ......................................................................................................... 13
List of figures .......................................................................................................... 19
General introduction .............................................................................................. 21
1. Epigenetics ...................................................................................................... 23
1.1 Introduction to epigenetics ................................................................................. 23
1.2 Chromatin .......................................................................................................... 26
1.3 Epigenetic modifications and their machineries ................................................... 30 1.3.1 Covalent DNA modifications ................................................................................................. 30
1.3.1.1 Methylation of cytosine ............................................................................................... 31 1.3.1.1.1 5mC distribution ...................................................................................................... 31 1.3.1.1.2 The DNA methyltransferases ................................................................................... 33 1.3.1.1.3 Gene silencing mediated by DNA methylation ........................................................ 34 1.3.1.1.4 Biological and pathological relevance of 5mC ......................................................... 35 1.3.1.1.5 Mapping the methylome ......................................................................................... 38 1.3.1.2 Hydroxymethylation of cytosine .................................................................................. 39 1.3.1.2.1 5hmC distribution .................................................................................................... 39 1.3.1.2.2 The TET enzymes ..................................................................................................... 40 1.3.1.2.3 Active demethylation and DNA hydroxymethylation .............................................. 41 1.3.1.2.4 Biological and pathological relevance of 5hmC and TETs........................................ 43 1.3.1.2.5 Mapping the hydroxymethylome ............................................................................ 46 1.3.1.3 Other covalent DNA modifications .............................................................................. 47 1.3.1.3.1 Oxidative derivatives of 5hmC ................................................................................. 47 1.3.1.3.2 Methylation of adenine ........................................................................................... 48
1.3.2 Histone modifications ........................................................................................................... 48 1.3.2.1 Histone acetylation ...................................................................................................... 49 1.3.2.2 Histone methylation .................................................................................................... 50 1.3.2.3 Other histone modifications ........................................................................................ 52
1.3.3 Epigenetic regulation of the chromatin ................................................................................ 53 1.3.3.1 Crosstalk between epigenetic modifications ............................................................... 53 1.3.3.2 Chromatin remodeling ................................................................................................. 55
1.3.4 RNA modifications ................................................................................................................ 58 1.3.4.1 Methylation of adenosine ............................................................................................ 59 1.3.4.1.1 m6A distribution ...................................................................................................... 59 1.3.4.1.2 The m6A machinery ................................................................................................. 60 1.3.4.1.3 Biological relevance of m6A .................................................................................... 61 1.3.4.2 Methylation of cytosine ............................................................................................... 63 1.3.4.2.1 5mrC in non-coding RNAs ........................................................................................ 64 1.3.4.2.2 5mrC in mRNAs ........................................................................................................ 64 1.3.4.2.3 Oxidation of 5mrC into 5hmrC ................................................................................ 65 1.3.4.3 Mapping RNA modifications ........................................................................................ 65
2. Breast cancers ................................................................................................. 67
2.1 Introduction ....................................................................................................... 67
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2.2 Diversity of breast cancers .................................................................................. 69 2.2.1 Histopathological classifications ........................................................................................... 69 2.2.2 Molecular classifications ....................................................................................................... 70
2.3 The immune system: a double-edged sword ........................................................ 72 2.3.1 Immune infiltration and its clinical relevance ...................................................................... 73 2.3.2 The NF-κB signaling pathway ................................................................................................ 74
2.4 Management and treatments .............................................................................. 76 2.4.1 Breast cancer diagnosis ........................................................................................................ 76 2.4.2 General therapeutic options................................................................................................. 76 2.4.3 Systemic therapies ................................................................................................................ 77
2.5 Breast cancer and epigenetics ............................................................................. 79 2.5.1 Epigenetics alterations in breast cancers ............................................................................. 79
2.5.1.1 Alterations of DNA modifications in BC ....................................................................... 79 2.5.1.2 Alterations of histone modifications in BC .................................................................. 82 2.5.1.3 Alterations of chromatin remodeling in BC ................................................................. 83 2.5.1.4 Alterations of RNA modifications in BC ....................................................................... 83
2.5.2 Clinical relevance of epigenetics........................................................................................... 84 2.5.2.1 Epigenetics modifications as biomarkers for cancer ................................................... 84 2.5.2.2 Epigenetic therapy of cancer ....................................................................................... 87
3. Aims of the project .......................................................................................... 93
Results ................................................................................................................... 95
1. Immune activation of NF-κB drives TET1 dysregulation in cancer ...................... 97
1.1. Introduction ............................................................................................................ 97
1.2. Results .................................................................................................................... 99 1.2.1. TET1 expression is associated with 5hmC dysregulation in BLBC .................................... 99 1.2.2. Link between TET1 expression and immunity in BLBC ................................................... 102 1.2.3. Activation of NF-κB drives TET1 repression ................................................................... 104 1.2.4. TET1 is repressed through binding of NF-κB to its promoter ......................................... 108 1.2.5. TET1 is downregulated by NF-κB in other cancer types ................................................. 110
1.3. Key findings .......................................................................................................... 113
2. RNA hydroxymethylation: a new player in the game ...................................... 115
2.1. Introduction .......................................................................................................... 115
2.2. Results .................................................................................................................. 117 2.2.1. RNA hydroxymethylation by dTet in Drosophila S2 cells ............................................... 117 2.2.2. Transcriptome-wide mapping of 5hmrC in S2 cells ........................................................ 119 2.2.3. Hydroxymethylation can favor mRNA translation ......................................................... 121 2.2.4. In vivo relevance of 5hmrC in Drosophila ...................................................................... 123 2.2.5. Alterations of 5hmrC in breast cancer ........................................................................... 125
2.3. Key findings .......................................................................................................... 129
3. Dysregulations of m6A and its machinery support breast cancer .................... 131
3.1. Introduction .......................................................................................................... 131
3.2. Results .................................................................................................................. 133 3.2.1. Transcriptome-wide m6A landscape in breast cancer ................................................... 133
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3.2.2. Deregulation of the m6A machinery in BC ..................................................................... 137 3.2.3. Phenotypic effect of FTO in BC ....................................................................................... 139 3.2.4. FTO regulates the Wnt/β-catenin pathway in BC .......................................................... 141
3.3. Key findings .......................................................................................................... 146
Discussion ............................................................................................................ 149
1. Immune activation of NF-κB drives TET1 dysregulation in cancer .................... 151
2. RNA hydroxymethylation: a new player in the game ...................................... 159
3. Dysregulations of m6A and its machinery support breast cancer .................... 165
4. Concluding remarks ....................................................................................... 171
References ........................................................................................................... 175
Appendix .............................................................................................................. 205
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Abbreviations
2-OG 2-Oxoglutarate
5-Aza-CdR 5-Aza-2'-deoxycytidine
5-Aza-CR 5-Azacytidine
5caC DNA 5-carboxycytosine
5fC DNA 5-formylcytosine
5hmC DNA 5-hydroxymethylcytosine
5hmrC RNA 5-hydroxymethylcytosine
5hmU DNA 5-hydroxymethyluracile
5mC DNA 5-methylcytosine
5mrC RNA 5-methylcytosine
A Adenine
Ac Acetylation
ADAM19 A Disintegrin And Metalloproteinase Domain 19
ADP Adenosine Diphosphate
AID Activation-Induced Deaminase
ALKBH Alkylated DNA Repair Protein AlkB Homolog
APC Adenomatosis Polyposis Coli
APOBEC Apolipoprotein B mRNA editing enzyme catalytic polypeptide-like
ASB2 Ankyrin Repeat And SOCS Box Containing 2
ATP Adenosine Triphosphate
B2M Beta-2-Microglobulin
BC Breast Cancer
Bdnf Brain-derived neurotrophic factor (mouse)
BER Base Excision Repair
BLBC Basal-like Breast Cancer
BRCA1-2 Breast Cancer 1-2
BS Bisulfite
BS-Seq Bisulfite Sequencing
BTRC Beta-Transducin Repeat Containing E3 Ubiquitin Protein
C Cytosine
CBP Cyclic AMP response element-binding protein
CCL2 C-C Motif Chemokine Ligand 2
CD Cluster of Differentiation
CDO1 Cysteine Dioxygenase Type
CGI CpG island
ChIP-Seq Chromatin Immunoprecipitation Sequencing
CIBERSORT Cell type Identification By Estimating Relative Subsets Of known RNA
Transcripts
CLIP UV Cross-Linking and Immunoprecipitation
CM Conditioned Media
CMS Cytosine 5-Methylenesulphonate
COMPASS Complex Proteins Associated with Set1
CpG Cytosine-phosphate-Guanosine
CPM Counts Per Million
CTCF CCCTC-Binding Factor
CTL Control
CTLA-4 Cytotoxic T-Lymphocyte Associated Protein 4
Dam DNA adenine methylase
DAPK1 Death Associated Protein Kinase 1
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DAVID Database for Annotation, Visualization and Integrated Discovery
dhmR differentially hydroxymethylated regions
DMFS Distant Metastasis Free Survival
DNA Deoxyribonucleic Acid
DNMT DNA Methyl Transferase
DOX Doxocycline
DSBH Double-Stranded β-Helix
dTet drosophila Tet
eIF3 Eukaryotic Translation Initiation Factor 3
EMT Epithelial to Mesenchymal Transition
ER Estrogen Receptor
Erbb2 Erb-B2 Receptor Tyrosine Kinase 2
ES Embryonic Stem
ESMO European Society for Medical Oncology
EZH2 Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit
FBS Fetal Bovine Serum
FC Fold-Change
FDA Food and Drug Administration
FDR False Discovery Rate
Fgf1 Fibroblast Growth Factor 1
FK-228 Romidepsin
F-LUC Firefly Luciferase
FN1 Fibronectin 1
FOXM1 Forkhead Box M1
FTO Fat Mass And Obesity Associated
FZD Frizzled homolog Drosophila
G Guanine
GADD45 Growth Arrest And DNA Damage Inducible Alpha
GEO Gene Expression Omnibus
GFP Green Fluorescent Protein
GGI Genomic Grade Index
GlcNAcylation O-linked N-acetylglucosaminylation
GNAT GCN5-related N-acetyltransferases
GSK3 Glycogen Synthase Kinase 3
GSTP1 Glutathione S-Transferase Pi 1
GWAS Genome-Wide Association Studies
H1 Histone 1
H2A Histone 2A
H2B Histone 2B
H3 Histone 3
H3K27ac Acetylation of Histone 3 lysine 27
H3K27me3 Trimethylation of Histone 3 lysine 27
H3K4ac Acetylation of Histone 3 lysine 4
H3K4me3 Trimethylation of Histone 3 lysine 4
H3K9ac Acetylation of Histone 3 lysine 9
H3K9me3 Trimethylation of Histone 3 lysine 9
H4 Histone 4
H4K27ac Acetylation of Histone 4 lysine 27
HCF1 Host Cell Factor C1
HDAC Histone Deacetylase
HDM Histone Demethylases
HER2 Human Epidermal Growth factor Receptor 2
hMeDIP Hydroxymethylated DNA Immunoprecipitation
hMe-Seal 5hmC-selective chemical labeling
HMT Histone Methyl Transferase
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HNRNP Heterogeneous Nuclear Ribonucleoprotein
HOTAIR HOX Transcript Antisense RNA
HOX Homeobox
HP1 Heterochromatin Protein 1
HuR Hu Antigen R
ICM Inner Cell Mass
ICR1 Imprint Control Region 1
IDAX Inhibition Of The Dvl And Axin Complex
IDC-NOS Invasive Ductal Carcinoma Not Otherwise Specified
IDC-NST Invasive Ductal Carcinoma of No Special Type
IGF1R Insulin Like Growth Factor 1 Receptor
IGF2 Insulin Like Growth Factor 2
IgG Immunoglobulin G
IHC Immunohistochemistry
IKK IkB kinase
IL Interleukin
ILC Invasive Lobular Carcinoma
IP Immunoprecipitation
IPA Ingenuity Pathway Analysis
IκB Inhibitor of kappa B
JBP J-Binding Protein
LBH-589 Panobinostat
LINE-1 LINE retrotransposable element 1
LncRNAs Long non-coding RNA
LPS Lipopolysaccharide
LRP LDL Receptor Related Protein
LSD1 Lysine (K)-Specific Demethylase 1
LST1 Leukocyte Specific Transcript 1
LUAD LUng Adenocarcinoma
m6A N6-methyladenosine
MAGE Melanoma Antigen Family
MAPK Mitogen-Activated Protein Kinase
MASPIN Mammary Serine Protease Inhibitor
MBD Methyl-CpG-Binding domain
MDSCs Myeloid Derived Suppressor Cells
Me Methylation
MeCP2 Methyl-CpG Binding Protein 2
MeDIP-Seq Methylated DNA Immunoprecipitation Sequencing
MethylCap-Seq Methylated DNA Capture and Sequencing
MeTIL Methylation of TIL
METTL Methyltransferase Like
MGMT O-6-Methylguanine-DNA Methyltransferase
MHC Major Histocompatibility Complex
miCLIP m6A individual-nucleotide resolution using CLIP
miRNA microRNA
MLH1 MutL Homolog 1
MLL Mixed Lineage Leukemia
MMTV-PyMT Mouse Mammary Tumor Virus Promoter-polyoma Middle T-antigen
MRI Magnetic Resonance Imaging
mRNA Messenger RNA
MYST Moz, Ybf2/Sas3, Sas2, Tip60
NANOG Nanog homeobox
NCBI National Center for Biotechnology Information
NF-κB Nuclear factor kappa B
NK Natural Killer
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NSUN2 NOP2/Sun RNA Methyltransferase Family Member 2
OCT4 Octamer-binding Transcription factor 4
OGA O-GlcNAcase
O-GlcNAc O-linked β-N-acetylglucosamine
OGT O-GlcNAc transferase
OxBS-Seq Oxidative Bisulfite Sequencing
P Phosphorylation
p16 cyclin-dependent kinase inhibitor 2A
p38 P38 Mitogen Activated Protein Kinase
p50 Nuclear Factor Kappa B Subunit 1
p52 Nuclear Factor Kappa B Subunit 2
p65 RELA Proto-Oncogene, NF-KB Subunit
PAM50 Prediction Analysis of Microarray 50
PARP Poly(ADP-Ribose) Polymerase 1
PCDHGB7 Protocadherin Gamma Subfamily B, 7
PD-1/PD-L1 Programmed Cell Death 1 Ligand 1
PGC Primordial Germ Cells
pHEMA Poly(2-hydroxyethyl methacrylate)
PI3K Phosphatidylinositol-4,5-Bisphosphate 3-Kinase
PIK3CA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha
PITX2 Paired Like Homeodomain 2
Poly-A Polyadenylation
PPP2CB Protein Phosphatase 2 Catalytic Subunit Beta
PR Progesterone Receptor
PRMT Protein Arginine Methyltransferase
PTEN Phosphatase And Tensin Homolog
PXD-101 Belinostat
RAD51 RecA-Like Protein
RARA Retinoic Acid Receptor Alpha
RAR-b Retinoic Acid Receptor beta
RAS3 Ras-related protein R-Ras3
RASSF1A Ras Association Domain Family Member 1
Rb Retinoblastoma
Rel REL proto-oncogene
RFS Recurrence-Free Survival
RIP RNA Immunoprecipitaion
R-LUC Renilla luciferase
RNA Ribonucleic Acid
RNA-seq RNA sequencing
RPKM Reads Per Kilobase per Million mapped reads
R-Ras Related RAS Viral (R-Ras) Oncogene Homolog
rRNA Ribosomial RNA
RSEM RNA-Seq by Expectation Maximization
RTqPCR Reverse transcription polymerase chain reaction
RUNX1T1 RUNX1 Translocation Partner 1
SAHA Vorinostat
SAM S-adenosylmethionine
SATR-1 Satellite-like Repeat 1
SD Standard Deviation
SET1 SET Domain Containing 1A
SFN Stratifin
shRNA Short hairpin RNA
Sin3a SIN3 homolog A
siRNA Small Interfering RNA
SKCM SKin Cutaneous Melanoma (TCGA cohort)
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SMUG1 Single-Strand-Selective Monofunctional Uracil-DNA Glycosylase 1
snRNA small nuclear RNA
SOX Sex Determining Region Y-Box
SRSF2 Serine And Arginine Rich Splicing Factor 2
T Thymine
TAB-Seq Tet-Assisted Bisulfite Sequencing
TCGA The Cancer Genome Atlas
TDG Thymine DNA Glycosylase
TE Trophectoderm
TET Ten-Eleven Translocation
TF Transcription Factor
TFBIND Transcription Factor BINDing site
TH T Helper
THCA THyroid CArcinoma (TCGA cohort)
TIL Tumor-Infiltrating Lymphocyte
TIMP TIMP Metallopeptidase Inhibitor
TME Tumor Microenvironment
TN Triple Negative
TNF Tumor Necrosis Factor
TNM Tumor Node Metastasis
TP53 Tumor Protein P53
tRNA Transfer RNA
TSG Tumor Suppressor Gene
TSS Transcription Start Site
TYROBP TYRO Protein Tyrosine Kinase Binding Protein
Ub Ubiquitination
UCSC University of California Santa Cruz
UHRF1 Ubiquitin Like With PHD And Ring Finger Domains 1
UTR Untranslated regions
UV Ultraviolet
WHO World Health Organization
WNT Wingless-Type MMTV Integration Site Family
WTAP WT1 Associated Protein
XIST X Inactive Specific Transcript
YTHDC YTH Domain Containing
YTHDF YTH Domain Family
ZO1 Zona Occludens 1
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List of figures
Fig. 1: Genome versus epigenome.
Fig. 2: The nucleosome.
Fig. 3: Chromatin organization and packing.
Fig. 4: Euchromatin and heterochromatin.
Fig. 5: Epigenetic modifications regulate the flow of genetic information.
Fig. 6: The methylome.
Fig. 7: DNMT-mediated DNA methylation.
Fig. 8: De novo and maintenance methylation.
Fig. 9: Transcription silencing by DNA methylation.
Fig. 10: Dynamicity of 5mC during development.
Fig. 11: Aberrant DNA methylation in cancer.
Fig. 12: 5hmC distribution in the cerebellum.
Fig. 13: Activity and structure of human TET proteins.
Fig. 14: Pathways of cytosine demethylation mediated by TET proteins.
Fig. 15: View of DNA demethylation in the zygote.
Fig. 16: 5hmC changes in cancer.
Fig. 17: The main modifications of the four core histones.
Fig. 18: Histone acetylation.
Fig. 19: Major methylation events of histones H3 and H4.
Fig. 20: Crosstalk between DNA and histone modifications.
Fig. 21: Cooperative regulation and gene silencing.
Fig. 22: Connecting TETs and OGT.
Fig. 23: SWI/SNF complexes.
Fig. 24: Chemical modifications in eukaryotic mRNA.
Fig. 25: Metagene profiles of m6A.
Fig. 26: The writer, eraser and reader proteins of m6A.
Fig. 27: Mechanisms and functions of m6A.
Fig. 28: Functions of 5mrC.
Fig. 29: Ten leading cancer types.
Fig. 30: Anatomy of the breast.
Fig. 31: TNM stage is a predictor of overall survival.
Fig. 32: Expression subtypes are associated with clinical features.
Fig. 33: Balance of the immune TME.
Fig. 34: Canonical NF-κB pathway.
Fig. 35: Standard treatments for BC.
Fig. 36: DNA methylation changes are associated with ER status in BC.
Fig. 37: Loss of 5hmC in BC, measured by IHC.
Fig. 38: TET1 act as a TSG in BC.
Fig. 39: Example of epigenetic prognostic marker.
Fig. 40: DNA methylation as a tool to quantify immune infiltration.
Fig. 41: Activation of TSG by epigenetic drugs.
Fig. 42: Epigenetic drugs in cancer therapy.
Fig. 43: TET1 expression in BC subtypes.
Fig. 44: TET1 regulation is associated with distinct 5hmC changes in BLBC.
Fig. 45: Link between 5hmC, 5mC and gene expression in BLBC.
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Fig. 46: TET1 is anticorrelated with genes linked to immune pathways.
Fig. 47: High TET1 expression discriminates BLBC tumors with low immune infiltration.
Fig. 48: Leukocyte-conditioned media represses TET1 expression.
Fig. 49: TET1 expression and NF-κB in breast cancer.
Fig. 50: TET1 expression is repressed by NF-κB activation in vitro.
Fig. 51: TET1 expression is repressed by NF-κB activation in vivo.
Fig. 52: Schematic view of TET1 gene promoter.
Fig. 53: Binding of p65 to TET1 promoter.
Fig. 54: TET1 expression and immune markers in cancer.
Fig. 55: TET1 expression and NF-κB signature.
Fig. 56: NF-κB represses TET1 expression in cancer.
Fig. 57: Validation of 5hmrC-detecting dot blot method.
Fig. 58: 5hmrC is enriched in poly A RNA in S2 Drosophila cells.
Fig. 59: dTet knockdown leads to reduced 5hmrC levels.
Fig. 60: Transcriptome-wide distribution of 5hmrC in Drosophila cells.
Fig. 61: dTet mediates transcriptome-wide RNA hydroxymethylation in Drosophila.
Fig. 62: 5hmrC and gene expression upon depletion of dTet in S2 cells.
Fig. 63: Highly translated mRNAs display high levels of 5hmrC.
Fig. 64: 5hmrC favors mRNA translation.
Fig. 65: Levels of dTet and 5hmrC in early embryogenesis.
Fig. 66: dTet and 5hmrC levels in the brain.
Fig. 67: dTet-deficient fruit flies show impaired brain development, accompanied by
decreased 5hmrC.
Fig. 68: RNA methylation and hydroxymethylation in mammary cells.
Fig. 69: Distribution of 5hmrC peaks in breast cells.
Fig. 70: Changes in 5hmrC in BC.
Fig. 71: Examples of 5hmrC tracks in breast cells.
Fig. 72: Transcriptome-wide mapping of m6A in SKBR3 cells.
Fig. 73: m6A changes in cultured BC cells.
Fig. 74: Transcriptome-wide mapping of m6A in human BC biopsies.
Fig. 75: Expression of m6A enzymes in BC.
Fig. 76: Quantification of m6A in BC by mass spectrometry.
Fig. 77: FTO expression and survival in BC.
Fig. 78: FTO-knockdown in BC.
Fig. 79: FTO depletion enhances the mobility of cancer cells.
Fig. 80: FTO depletion enhances in vitro tumorsphere formation.
Fig. 81: Canonical Wnt pathway.
Fig. 82: FTO regulates β-catenin in BC.
Fig. 83: Loss of FTO enhances β-catenin signaling.
Fig. 84: Loss of FTO affects response to Wnt inhibitor in BC.
Fig. 85: Proposed model illustrating the immune regulation of TET1 in cancer.
Fig. 86: FTO, breast cancer and adipocytes.
Fig. 87: Proposed model illustrating effects of FTO depletion in BC.
Fig. 88: The molecular portrait of tumors is a multidimensional picture.
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General introduction
General introduction
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1. Epigenetics
1.1 Introduction to epigenetics
In 2001, the unravelling of the human genome generated considerable excitement
in the scientific community and provided an essential basis for the study of the inheritance
of phenotypical features, as described by Mendel over a century earlier (Lander et al.
2001). The genetic information, coded in our DNA, is written with 4 “letters” (A, T, C,
G) and inherited from our parents. The genes guide our development from a single cell to
a fully formed human being and outline our identity as a species. However, many
questions remain and research in the past decades indicates that we are “more than the
sums of our genes”. A striking illustration is the vast diversity in cell types within the
human body, from neurons to leukocytes or hepatocytes. All those cells share the same
genomic DNA, yet they display very different shapes and functions (Wade 2009).
Another example is the discrepancies in phenotypes observed between monozygotic
twins (Silva et al. 2011). To address those questions, one must look beyond the single
lecture of the DNA sequence to understand the fine regulation of gene expression.
The word “epigenetics”, which comes from the prefix epi- (in Greek επί: over,
above) and genetics, was broadly used to characterize events that could not be explained
by genetics alone, hence the need to go “beyond” genetics. It was defined in 1942 by
Conrad Waddington as “the branch of biology which studies the causal interactions
between genes and their products, which bring the phenotype into being” (Waddington
1942). Thus, in the original sense of the definition, any mechanism modulating the
apparition of a phenotype from a certain genotype could be considered as “epigenetic”.
However, the definition of epigenetics evolved over time, along with our knowledge of
genetics and gene regulation. It also narrowed over the years, focusing on “the study of
mitotically and/or meiotically heritable changes in gene function that cannot be explained
by changes in DNA sequence” (Russo et al. 1996; Riggs & Porter 1996; Bird 2007). The
field now comprises a range of biological mechanisms, including, among others, genomic
imprinting, X chromosome inactivation and retrotransposon regulation. Nevertheless, the
24
exact definition of epigenetics remains open to debate. In particular, the heritability trait
has yet to be clearly demonstrated for many events generally accepted as “epigenetic”
(Deans et al. 2015).
The field of epigenetics has attracted growing attention over the past decades, as
our understanding of its molecular basis has increased. The study of how epigenetic
mechanisms can influence the structure of the chromatin (the complex of DNA and its
intimately associated protein histones) and gene expression has improved our knowledge
of physiological development and homeostasis, as well as their deregulation in
pathologies (Berner et al. 2010; Portela & Esteller 2010). In the strict sense, two known
molecular mechanisms currently satisfy the definition of epigenetics: the first are the
covalent modifications of DNA, and the second are the post-translational modifications
of histones. Next to the classical epigenetic modifications, which focus on the regulation
of the genome and the chromatin, a third class of modifications has recently gained the
attention of the scientific community: the RNA modifications. A new field of study,
commonly referred to as “RNA epigenetics” or “epitranscriptomics”, has emerged as a
new regulatory level of gene function (Liu & Pan 2017).
Taken together, the epigenetic modifications that either activate or inactivate the
genes form a code that controls their expression. The epigenetic state of a given cell is
referred to as the “epigenome”, by analogy to the genome, for which the genetic
information is encoded in the DNA. A striking characteristic of epigenetics, which makes
its study so compelling, is its outstanding versatility: while all the cells of a given
organism essentially contain the same genome stocking the genetic information, many
distinct epigenomes are present within the same organism and organize this information
differently (Fig. 1) (Wade 2009). This versatility is both spatial and temporal. The
epigenomes vary vastly between cell types and tissues, and they evolve during the
development from a single-cell zygote to an adult, as well as during the aging events
occurring beyond (Lokk et al. 2014; Booth & Brunet 2016). It can also be influenced by
external factors, such as the nutritional status, thus offering a bridge between nature and
nurture (Boyce & Kobor 2015). In that regard, the study of epigenetics is both very
complex and powerful. Interestingly, the recent technical progresses in biomedical
sciences, particularly in the domain of micro-array and sequencing technologies, have
eased the study of the epigenomes by making them more accessible, both in terms of cost
and workload.
25
Fig. 1: Genome versus epigenome.
All the cells of the organism contain
the same genetic information in their
DNA. In contrast, the epigenetic
information contained in the
chromatin varies from one cell type
to another.
Because of this versatility, epigenetics has recently been integrated in the study of
many biological fields. It is now taken into consideration in research of early
development, reproduction, immunity, neurology, and evolution, among others (Portela
& Esteller 2010; Mirabella et al. 2016). This research is not restricted to humans and
mammals; it encompasses all types of species, from bacteria and yeast to worms, fish or
plants (Willbanks et al. 2016). In addition, epigenetic mechanisms and their deregulations
are also studied in the context of most pathologies, including all cancers,
neurodegenerative disorders (e.g. Parkinson, Huntington, and Alzheimer diseases),
inflammation, auto-immune diseases and cardio-vascular diseases (Portela & Esteller
2010; Brookes & Shi 2014). Thus, the scope of epigenetics has been broadening over the
years and is now interwoven with many other disciplines in biology.
In summary, epigenetics offers a complex and flexible reading of a cell’s state and
its study crosses-over disciplines such as physiology, medicine and evolution. The
epigenetic mechanisms are of vital importance for the proper regulation of many
biological processes. In the following sections, we will develop the main notions of
epigenetics. We will first present the concept of the chromatin, then we will detail the
molecular bases of the major epigenetic mechanisms.
DNA Chromatin
26
1.2 Chromatin
The diploid human genome consists of approximately 6 billion base pairs of DNA
which, in a loose state, represent a length of about 2 meters and contain about 20,000
genes (Ezkurdia et al. 2014). The genome is organized in 23 chromosome pairs that are
confined in the nucleus of the cells that measures about 10µm of diameter. Therefore, the
DNA must be carefully packed and organized in order to both fit in the nucleus and allow
the proper expression of the genes. This high compaction (of over 100,000-fold in
magnitude) is accomplished through the wrapping of the DNA around a core of histone
proteins – much like thread around a bobbin – and the nucleoprotein complex thus formed
is called the chromatin (Chakravarthy et al. 2005). This enfolding of the DNA is repeated
along the genome, each unit of this particular polymer being called a “nucleosome”. The
structure of the chromatin is extremely flexible, allowing various state of compaction and
modulating gene expression.
The nucleosome is the fundamental unit of the chromatin (Fig. 2). It is made of
approximatively 147bp of DNA wrapped around an octamer of proteins containing two
copies of the four core histones (H2A, H2B, H3 and H4) (Mirabella et al. 2016). The
octamer is formed by the association of two H2A-H2B dimers with one H3-H4 tetramer.
An additional histone protein, H1, binds to the “linker DNA” located between
nucleosomes. It is involved in the packing and compaction of the DNA. All histones
consist of a globular hydrophobic core and a basic N-terminal tail that projects from the
surface of the protein (Chakravarthy et al. 2005). This tail can be significantly altered by
a range of post-translational modifications. The role of these modifications in the
regulation of chromatin will be detailed in a further chapter (see 1.3.2 Histones
modifications).
27
Fig. 2: The nucleosome. Schematic
representation of DNA wrapped
around the histone octamer core.
Histone tails protrude from the core
and are subjected to chemical
modifications. (Adapted from
neuropsychotherapist.com)
The succession of nucleosomes forms a fiber of about 10nm diameter, commonly
referred to as the “beads on a string” structure (Fig. 3). It is the first level of compaction
of the DNA (Szerlong & Hansen 2011). The chromatin can be further compacted through
interactions between the nucleosomes, due to the incorporation of the H1 histone, leading
to a 30nm fiber. Further compaction of the chromatin fiber eventually produces the
formation of the metaphase chromosome structure (Fig. 3) (Mirabella et al. 2016). This
level constitutes the highest degree of packing of the chromatin and is clearly visible in
microscopy. The visualization of chromosomes through karyotyping methods has clinical
implications, as it allows the detection of dire chromosomal defects in fetus and newborn
children, such as the trisomy of chromosome 21 which causes Down syndrome (Ho &
Crabtree 2010).
Fig. 3: Chromatin organization and packing. Double-stranded DNA wraps around
histone proteins to form nucleosomes in the “beads on a string” structure. Further
compaction of the chromatin leads to 30-nm and 300-nm fibers of chromatin, and then to
the chromosome structure observed in metaphase. (Adapted from
www.geneticliteracyproject.org/?p=337515)
28
Chromatin is commonly classified in two categories, based on the degree of
compaction and the transcriptional activity: euchromatin and heterochromatin (Fig. 4).
The terms originally refer to differences in staining intensity observed in the nucleus,
which corresponded to distinct levels of compaction of the chromatin (Heitz 1928). This
difference is also visible with electron microscopy imaging of the nucleus:
heterochromatin appears as dark regions while euchromatin constitutes much brighter
areas (Wolf & Sumner 1996).
Heterochromatin corresponds to a highly-condensed state of chromatin, which
hinders gene transcription (Fig.4). It can be subdivided in two categories. On one hand,
the constitutive heterochromatin refers to chromatin regions that are always compact. It
comprises mainly gene-poor, noncoding and repetitive sequences. Of note, the presence
of constitutive heterochromatin in the telomeres and centromeres is important for genome
integrity (Almouzni & Probst 2011; Postepska-Igielska et al. 2013). On the other hand,
the facultative heterochromatin corresponds to regions that can alternate between a
closed, condensed state and a more open, transcriptionally active state (Trojer & Reinberg
2007). The regulation of such chromatin is notably linked to morphogenesis and cell
differentiation. A striking example of facultative heterochromatin is the X chromosome
in female, for which one copy is silenced by tight compaction of the chromatin while the
other remains open and allows gene transcription (Wutz 2011).
In contrast to heterochromatin, euchromatin refers to open, loose regions of
chromatin (Fig. 4). It is often called “active chromatin”, as it consists mainly of coding
regions of the genomes that are accessible to the transcription machinery (Tamaru 2010).
However, euchromatin is not always transcriptionally active. While some genes of
euchromatin are ubiquitously expressed (and thus commonly named “housekeeping
genes”), other genes require the involvement of additional transcription factors to
promote their expression.
Fig. 4: Euchromatin and
heterochromatin. Chromatin can
be divided in euchromatin (loosely
packed and accessible to the
transcription machinery) and
heterochromatin (highly condensed
and not transcribed). (Adapted from
Sha & Boyer 2008)
29
However, this view must be nuanced, as heterochromatin and euchromatin
represent two extreme situations on a wide spectrum that rarely reflect the reality of
chromatin state. For instance, the telomeric regions are considered as heterochromatic, as
previously mentioned, and yet they display some level of transcriptional activity.
Telomeric non-coding RNAs notably appear to regulate heterochromatin formation and
their dysregulation can lead to cellular senescence (Arnoult et al. 2012; Maicher et al.
2012). Likewise, centromeric regions contain large domains of heterochromatin,
nevertheless active transcription occurs and is a critical element to maintaining
centromere function (Hall et al. 2012). Thus, heterochromatin regions are not always
transcriptionally inert and their RNAs fulfill important functions.
In conclusion, DNA is not “naked” in the nucleus, but is associated with proteins
in a complex structure called the chromatin. The regulation of chromatin is key to the
establishment of a proper gene expression profile for any given cell. Several mechanisms
are involved in this regulation, including epigenetic modifications that are described
below.
30
1.3 Epigenetic modifications and their machineries
In this chapter, we explore the main epigenetic modifications and their regulation
(Fig. 5). First, we focus on the best-characterized modifications, namely DNA and histone
modifications. Then, we expand on an emerging theme in epigenetics, the field of RNA
modifications.
Fig. 5: Epigenetic modifications regulate the flow of genetic information. In the
central dogma of biology, genetic information is passed from DNA to RNA and then to
protein. Epigenetic DNA modifications and histone modifications are known to have
important roles in regulating gene transcription. RNA modifications add an additional
layer of dynamic regulation of biological processes. (Adapted from Y. Fu et al. 2014)
1.3.1 Covalent DNA modifications
The fine regulation of gene expression is essential to the establishment of various
cell types and the completion of their functions. One of the central mechanisms
underlying this regulation is the modification of the DNA through the addition of
chemical moieties. The most studied modification is, by far, the methylation of cytosine.
For decades, it was the only epigenetic modification of the DNA extensively investigated
31
in biomedical sciences. However, in recent years, light was shed on additional DNA
modifications, such as the hydroxymethylation of cytosine, thus emphasizing a system
more complex and dynamic than first thought. In this chapter, we will describe the main
covalent modifications of the DNA, their regulation and physiological relevance. We will
also describe the technologies available to profile their distributions.
1.3.1.1 Methylation of cytosine
The methylation of cytosine, commonly called “DNA methylation”, involves the
enzymatic transfer of a methyl group to the 5’ carbon of a cytosine residue to form the 5-
methylcytosine (5mC). This modified cytosine is sometimes referred to as the “fifth base”
of DNA. This modification is present on the genome of animals, plants and prokaryotes.
It is involved in a broad range of biological mechanisms, including bacterial defense
against bacteriophages, X chromosome inactivation, repression of transposons (Bergman
& Cedar 2013; Deans et al. 2015).
1.3.1.1.1 5mC distribution
In humans, cytosine methylation occurs nearly exclusively in a “CpG” context,
i.e. a cytosine-phosphate-guanine dinucleotide, and it is often associated with
transcriptional repression (Ziller et al. 2011). Importantly, CpG sites are not distributed
evenly in the genome, instead they tend to cluster together in regions called “CpG islands”
(CGIs) (Fan & Zhang 2009). These regions are short sequences of DNA with an
exceptionally high density of CpGs that are often located in the 5’ region of genes.
Interestingly, Alu sequences, the most abundant of the repetitive elements of the human
genome, are also CpG-rich regions and contain approximately one-third of all human
CpG dinucleotides (Y. Luo et al. 2014).
In mammals, almost 70% of all CpG sites are methylated (Lister et al. 2009), and
the patterns of methylation are not random. The highly-methylated regions include
repeated sequences, centromeric regions, satellite DNA and transposons (Almouzni &
Probst 2011; Postepska-Igielska et al. 2013). This methylation is essential for the
repression of endoparasitic elements, which could lead to genome instability through
translocation and gene disruption. In contrast, the CGIs located in the promoter of genes
32
are mostly unmethylated. Nevertheless, methylation of these CGIs can still be acquired
during development in a tissue-specific manner (Fan & Zhang 2009).
Traditionally, 5mC is considered in a gene promoter context and has long been
associated with gene silencing. However, this dogma has been extended in recent years
with the advances of technologies (Dedeurwaerder et al. 2013; Moran et al. 2015).
Genome-wide profiling has shown that 5mC is in fact far from restricted to promoter
regions. Methylation across the gene (often called intragenic or gene body methylation)
is now thought to be involved in transcriptional regulation and efficiency. And, strikingly,
whereas promoter methylation is a repressive mark, intragenic methylation tends to show
a positive correlation with gene expression (Kulis et al. 2013). Intragenic methylation
also appears to inhibit the transcription from alternative promoters, thus controlling
tissue-specific isoforms (Maunakea et al. 2010; Neri et al. 2017). Similarly, increasing
evidence suggests that methylation at intergenic regions can also regulate gene expression
(Shore et al. 2010; Moran et al. 2015). The noncoding regions contain regulatory
elements, including enhancers, silencers and noncoding RNAs, which can be affected by
5mC. Therefore, it is essential to consider DNA methylation across the entire genome
(also called “methylome”, see Fig. 6) rather than focusing solely on promoter regions
(Jeschke et al. 2015).
Fig. 6: The methylome. CGI promoters are usually protected from methylation and are
prone to active transcription. CpG-poor regions and repetitive elements are often
methylated. Enhancers, promoters, and intragenic regions can also be differentially
methylated in a cell type-specific fashion. (Adapted from Carrio & Suelves 2015)
33
1.3.1.1.2 The DNA methyltransferases
DNA methylation is mediated by a family of proteins called the DNA
methyltransferases (DNMTs). These enzymes transfer a methyl group (-CH3) from S-
adenosylmethionine (SAM) to the 5’ carbon of a cytosine (Fig. 7). There are five
members of the DNMT family in humans: DNMT1, DNMT2, DNMT3A, DNMT3B and
DNM3L. However, only three of them – DNMT1, 3A and 3B – are active DNA
methyltransferases.
Fig. 7: DNMT-mediated DNA methylation. DNMTs catalyze the transfer of a methyl
group from S-adenosylmethionine (SAM) to the 5-carbon position of cytosine (Adapted
from Gibney & Nolan 2010).
DNMT1 is called the “maintenance methyltransferase” because it is responsible
for the methylation of the newly synthetized strand during DNA replication (Fig. 8). In a
methylated CpG, the cytosines on both strands of the DNA are normally methylated.
However, after the genome duplication in S phase, the neo-synthetized strand is initially
unmodified, while the remaining parent strand is still methylated, thus generating a
“hemi-methylated site”. DNMT1 displays a high affinity for such hemi-methylated CpGs
and is able to methylate the new strand. Hence, it ensures the maintenance of the
methylation patterns following the genome duplication. Without DNMT1, the methylome
would not be maintained during cell divisions and would be progressively lost as the
consequence of a dilution effect (Li et al. 1992).
DNMT3A and 3B are called the “de novo methyltransferases” because they are
responsible for the establishment of the methylation patterns during early development
(Fig. 8). Loss of one or both enzymes in mice leads to embryonic or post-natal mortality,
highlighting the crucial role of DNA methylation in development (Kato et al. 2007). The
DNMT3 enzymes are also important for methylation pattern in adults. For instance,
DNMT3A was shown to be required for the maintenance of DNA methylation and
synaptic function in adult forebrain neurons (Feng et al. 2010). However, DNMT1 and
34
DNMT3 enzymes are not strictly restricted to their respective “maintenance” and “de
novo” methylation functions. Emerging evidence indicate that DNMT1 may also be
necessary for de novo methylation, while DNMT3 enzymes also contribute to
maintenance methylation (Fatemi et al. 2002; Walton et al. 2011).
Fig. 8: De novo and maintenance methylation. Unmethylated DNA is methylated de
novo by DNMT3A and 3B. Upon DNA replication, the newly synthesized DNA strand is
unmethylated at first. 5mC symmetry is then maintained by DNMT1. (Adapted from
http://atlasgeneticsoncology.org/Deep/DNAMethylationID20127.html)
In contrast, DNMT3L displays no catalytic activity. It is nevertheless required for
the establishment of genomic imprints, as the protein enhances the activity of DNMT3A
and 3B (Hata et al. 2002). The last member of the family, DNMT2, is active but the
enzyme but does not appear to methylate DNA in human cells. Instead, it is involved in
the methylation of the anticodon loop of aspartic acid transfer RNA (Tuorto et al. 2012).
1.3.1.1.3 Gene silencing mediated by DNA methylation
Repression of a gene by methylation is now a well-established event, particularly
in a promoter context. Two different models have been proposed for the underlying
mechanisms (Fig. 9). In the first model, the presence of 5mC prevents the binding of
transcription factors that are required to activate the expression of the gene. For instance,
at the well-known imprinted IGF2 gene cluster, binding of the CTCF factor to the imprint
control region 1 (ICR1) is prevented by methylation on the paternal allele (Hark et al.
2000). The second model relies on the recruitment of methyl-CpG-binding domain
(MBD) proteins which can specifically recognize and bind methylated CpGs. Those
35
MBDs can then in turn recruit repressing factors. For instance, MeCP2, the founding
member of the MBD family, is able to recruit histone deacetylases (HDAC) and other
chromatin-remodeling factors (Jones et al. 1998). This last example highlights how, most
often, epigenetic mechanisms are not acting alone, but instead they are part of
multifaceted regulatory networks. The role of histone modifications will be explored in a
subsequent chapter (see 1.3.2 Histone modifications).
Fig. 9: Transcription silencing by DNA methylation. Methylation of CpGs can prevent
the binding of transcription factors (TF) required for gene expression, as well as recruit
repressive complexes through binding of MBDs (e.g. MeCP2). The complexes can
comprise histone modifiers (e.g. HDAC1, HMT) or other a transcriptional regulatory
protein (e.g. Sin3a). (Adapted from Feng & Nestler 2010).
1.3.1.1.4 Biological and pathological relevance of 5mC
It is important to note that DNA methylation patterns are vastly modified during
the life of an organism. This is a dynamic process: 5mC can be added by its writers, the
DNMTs, but it can also be removed. DNA demethylation occurs either as a passive or an
active mechanism. In the first case, 5mC is progressively lost as the result of the absence
of DNMT1 across successive cycles of DNA replication. In the second case, an enzymatic
reaction is involved, independently of the cell cycle. The latter will be explained in detail
in a subsequent chapter (see section 1.3.1.2 Hydroxymethylation of cytosines).
In mammals, there are two massive waves of DNA methylation and demethylation
during development (Fig. 10) (Lee et al. 2014). The first wave of demethylation occurs
in early embryogenesis, starting in the zygote just after fecundation and continuing in the
first few embryonic replication cycles of morula and blastula. DNA is then remethylated
in a cell-type and tissue-specific manner. The second wave of 5mC remodeling occurs
during gametogenesis: all DNA patterns are erased in the germline and the genome is
specifically remethylated in the gametes.
36
Fig. 10: Dynamicity of 5mC during development. Two waves of DNA demethylation
and (re)methylation occur during development: in the zygote and early embryo; and in
the germline (male: blue line, female: red line). In somatic cells, DNA methylation varies
in a tissue-specific manner and remains globally stable. PGC: Primordial Germ Cells.
ICM: Inner Cell Mass. (Adapted from Lee et al. 2014)
DNA methylation patterns are crucial to proper development and cell
differentiation. In embryonic stem (ES) cells, maintenance of the pluripotency is
controlled by a set of transcription factors, including OCT4, NANOG, and SOX2, whose
expression is controlled by promoter methylation (Altun et al. 2010). In somatic adult
tissues, as well, emerging evidence has highlighted the role of 5mC in multipotency, cell-
fate commitment and reprogramming. For instance, similarly to ES cells, OCT4 appears
to be regulated by promoter methylation in adult stem cells (Lee et al. 2010). Additionally,
DNA methylation changes were observed during somatic differentiation, particularly in
enhancer and promoter regions. As such, in the intestinal tract, abnormal 5mC during
differentiation can lead to aberrant crypt foci and disrupt the absorptive barrier of the
intestinal epithelium (C.-Z. Huang et al. 2015).
37
DNA methylation aberrations have been observed in a wide range of pathologies,
yet in none so extensively as in cancer. Malignant cells are characterized by a global loss
of 5mC, concomitant with a local hypermethylation (Fig. 11) (Dawson & Kouzarides
2012). On one hand, the global hypomethylation of the genome is mainly linked to a
decrease of 5mC in repetitive and gene-poor regions. This event affects genome stability
and allows the reactivation of transposable elements that can randomly integrate in the
genome, thus potentially disrupting genes. DNA hypomethylation can also lead to the
aberrant activation of genes involved in proliferation and tumor growth (e.g. R-Ras,
MASPIN and MAGE). On the other hand, the hypermethylation observed locally can also
cause the repression of control genes, particularly tumor suppressor genes (TSG, e.g. Rb,
p16, BRCA1 and MLH1). Interestingly, these aberrant 5mC patterns arise even in the
absence of any mutation in the DNMT genes (Brookes & Shi 2014; Mirabella et al. 2016).
Fig. 11: Aberrant DNA methylation in cancer. Local hypermethylation of tumor
suppressor genes (left) and global hypomethylation, including repetitive elements (right),
are hallmarks of cancer cells. (Adapted from Lao & Grady 2011)
Thus, DNA methylation changes are very broad in cancer, and cancerology has
taken an interest in epigenetics. First, 5mC displays an intriguing potential as a prognostic
and predictive tool. For instance, repression of MGMT (O6-methylguanine-DNA
methyltransferase) gene by promoter hypermethylation is associated with a better
response to chemotherapy in glioma patients (Esteller et al. 2000). DNA methylation,
which is very specific of the cell-type, also offers a snapshot of the tissue composition,
with implications for survival and response to treatment, which can notably be predicted
38
by the immune infiltration of the tumor (Oble et al. 2009; Dedeurwaerder & Fuks 2012;
Melichar et al. 2014). Beyond the use of 5mC as a marker, DNA methylation also offers
opportunities in terms of therapeutics. Demethylating agents have shown promising
results in the treatment of myelodysplastic syndromes, demonstrating minimal side
effects of long-term treatment, and they can be used in combination with conventional
treatments, such as chemotherapy and immunotherapy (Dawson & Kouzarides 2012).
These topics are further detailed in the context of breast cancer in a subsequent chapter
(see 2.5.2 Clinical relevance of epigenetics).
1.3.1.1.5 Mapping the methylome
Recent advances in DNA methylation profiling technologies have widely
improved the coverage of the methylome queried. Such techniques have changed 5mC
profiling, from a candidate gene and promoter focus to genome-wide approaches
encompassing all genomic regions.
The gold standard for DNA methylation is the bisulfite sequencing (BS-Seq), a
method based on the chemical conversion of unmethylated cytosines into thymidines,
combined with high-throughput sequencing. The strength of bisulfite sequencing is the
single nucleotide resolution (Andrews et al. 2012). Another approach consists in the
enrichment of methylated DNA fragments, followed by sequencing. The enrichment can
be achieved by immunoprecipitation (MeDIP-Seq) or by binding to MBD domains
(MethylCap-Seq or MBD-Seq) (Nair et al. 2011). The coverage of the sequencing-based
methods is unrivaled. Yet, these technologies exhibit a number of technical issues,
including fragment size selection and sequence depth. Also, the costs remain relatively
high, often preventing the processing of large cohorts.
In contrast, the array-based Infinium technology (Illumina, San Diego, USA)
constitutes a compromise between coverage and costs (both in terms of labor and money),
a clear benefit when studying larger cohorts (Dedeurwaerder et al. 2013). Although the
first version of the array focused mainly on promoters, the latest version can investigate
over 850,000 CpGs targeting promoters, intragenic and intergenic regions (Moran et al.
2015). This improvement in the Infinium technology clearly reflects the growing demand
in research to assess DNA methylation across the entire genome.
39
1.3.1.2 Hydroxymethylation of cytosine
While 5mC has long focalized the attention of researchers in terms of DNA
modifications, additional epigenetic marks have since come to light. In 2009, two groups
reported the presence of 5-hydroxymethylcytosine (5hmC) in mammalian genome
(Kriaucionis & Heintz 2009; Tahiliani et al. 2009). This discovery, along with the
involvement of 5hmC in DNA demethylation, completely changed the perception of
DNA modifications. It revealed a remarkably dynamic system and raised a series of
questions regarding the roles of 5hmC in transcriptional regulation, development and
pathologies.
1.3.1.2.1 5hmC distribution
DNA hydroxymethylation occurs through the oxidation of pre-existent 5mC, thus
the mark is predictably also found at CpGs in the mammalian genome. Yet, studies
showed that the distributions of the two marks are dissimilar to an extent. In both ES and
neuronal cells, 5hmC is found enriched in euchromatin, whereas 5mC accumulates rather
in the heterochromatin (Ficz et al. 2011; Szulwach et al. 2011; Chen et al. 2014). Overall,
the level of 5hmC across genes shows a clear drop in the promoter, around transcription
start sites (TSS), and increased levels in gene bodies (Fig. 12) (Song et al. 2011). And
while 5mC abundance is stable in most tissues, 5hmC levels are lower than 5mC and vary
highly between tissues: up to 40% of 5mC levels in Purkinje neurons versus only 7% in
mouse ES cells, and even less in other organs and cultured cell lines (Globisch et al. 2010;
Ito et al. 2010; Szwagierczak et al. 2010).
Globally, DNA hydroxymethylation is considered a mark of active gene
expression (Fig. 12) (Song et al. 2011), yet the correlation between 5hmC and expression
is not always straightforward. For instance, in ES cells, 5hmC is mostly absent of CGI
promoters, which are transcriptionally active. Yet, the mark is enriched at the “bivalent”
CGI promoters, which are marked by both repressive and active histone modifications.
Such genes, while repressed in ES cells, are “poised” for transcription upon
differentiation signals (Butler & Dent 2013).
DNA hydroxymethylation accumulation is frequently observed in the gene body,
especially in exons, and it positively correlates with gene expression: high intragenic
5hmC is observed in genes highly expressed and vice versa (Pastor et al. 2011; Song et
40
al. 2011; Wu et al. 2011; Szulwach et al. 2011). Finally, 5hmC is also distributed at
intergenic cis-regulatory elements, such as active enhancers (Pastor et al. 2011; Szulwach
et al. 2011). The effect of 5hmC in such regions has yet to be elucidated.
Fig. 12: 5hmC distribution in the cerebellum.
Metagene profiles of 5hmC (divided into four
bins based on gene expression, see color
legend) and input genomic DNA in adult mouse
cerebellum. 5hmC drops at the promoter, is
enriched in the gene body, and correlates with
gene expression. (Adapted from Song et al.
2011)
1.3.1.2.2 The TET enzymes
Oxidation of 5mC is catalyzed by the Ten-Eleven Translocation (TET) enzymes
(Fig. 13). The founding member of the family, TET1, was originally described in myeloid
leukemia, in which the gene is often translocated from chromosome 10 to chromosome
11, hence its name (Ono et al. 2002). The TET enzymes are members of the TET/J-
binding protein (JBP) family of 2-oxoglutarate (2-OG) and iron (II)-dependent
dioxygenases. In both human and mice, all three TET proteins are active, and their
catalytic domain is composed of a cysteine-rich region and a double-stranded β-helix
(DSBH) domain. TET1 and TET3 both possess a CXXC DNA binding domain, while
TET2 does not. Instead, TET2 interacts with a separate CXXC protein, encoded by a gene
called IDAX or CXXC4.
41
Fig. 13: Activity and structure of human TET proteins. TET1–3 can convert 5mC into
5hmC. The enzymes contain a cysteine (Cys)-rich region followed by the double-stranded
β-helix (DSBH) fold characteristic of the 2-oxoglutarate-Fe(II) dioxygenases and
required for catalytic activity. TET1 and TET3 also contain a CXXC domain. (Adapted
from Williams et al. 2012)
1.3.1.2.3 Active demethylation and DNA hydroxymethylation
In 2009, Tahiliani et al. showed in a seminal article that overexpression of
wildtype TET1, but not its catalytic mutant counterpart, led to a decrease of 5mC
(Tahiliani et al. 2009). This “demethylase” activity was soon extended to TET2 and TET3
(Ito et al. 2011). The intriguing possibility that TET-mediated DNA hydroxymethylation
might act as an intermediate in the demethylation process generated considerable interest
in the scientific community. While the existence of active demethylation had been long
postulated, the underlying mechanisms had remained mostly elusive. In this section, we
will explain what is known of TET-mediated demethylation.
Because the methyl group of 5mC is thermodynamically very stable, the direct
removal of the moieties from the cytosine is unlikely, as it would require huge energy
expenditure (Wu & Zhang 2010). Instead, one of the more realistic mechanisms for active
demethylation is the cleavage of the glycosyl bond between the ribose and the base by a
DNA glycosylase. This event results in an abasic site that can be removed and replaced
with an unmodified cytosine by the base excision repair (BER) machinery of the cell.
This mechanism would necessitate the selective targeting of glycosylases to the CpGs to
be demethylated. Studies have highlighted two main mechanisms by which 5hmC might
be targeted for such demethylation (Fig. 14). The first pathway involves the deamination
of 5hmC into 5-hydroxymethyluracile (5hmU) by AID/APOBEC proteins (Guo et al.
2011). The second possibility requires the iterative oxidation of 5hmC into 5-
formylcytosine (5fC) and 5-carboxycytosine (5caC), which is mediated by the TET
42
enzymes (Shen et al. 2013). In all cases, removal of the 5hmC derivatives is achieved
through a glycosylase, such as TDG or SMUG1, followed by BER-mediated “repair”.
Fig. 14: Pathways of cytosine demethylation mediated by TET proteins. 5hmC might
facilitate passive and active demethylation pathway. (Adapted from Williams et al. 2012)
A key regulator of active demethylation events is the GADD45 family of proteins.
Despite a lack of any enzymatic activity, GADD45 acts as a link between demethylation
targets and the DNA repair machinery (Niehrs & Schäfer 2012). It was notably reported
that GADD45 directly binds to TET1 and increases its oxidation activity (Kienhöfer et
al. 2015). Furthermore, GADD45 enhances TDG-mediated removal of 5fC and 5caC.
Accordingly, knockout of both Gadd45a and Gadd45b from mouse ES cells leads to
hypermethylation of many genes that are targeted by TDG (Z. Li et al. 2015).
Other mechanisms for TET-mediated demethylation have also been proposed. Of
note, 5hmC might simply promote passive demethylation (Fig. 14), as DNMT1 appears
to methylate 5hmC‐containing DNA less efficiently than 5mC hemi‐methylated DNA
(Valinluck & Sowers 2007). Another intriguing hypothesis that has been suggested is the
oxidative demethylation, which consists in the decarboxylation of 5caC residues.
Carboxylases are widespread in protein signaling, which raises the question of a putative
“DNA carboxylase”. In support to this hypothesis, 5caC-decarboxylating activity was
reported in mouse ES cells and DNA methyltransferases were found to catalyze the direct
decarboxylation of 5caC in vitro (Schiesser et al. 2012; Liutkevičiutè et al. 2014).
However, the existence of an endogenous 5caC decarboxylase remains uncertain.
In summary, TET-mediated DNA demethylation is initiated by the oxidation of
5mC into 5hmC and can be achieved though several ways: passive demethylation,
43
glycosylase/BER machinery, and possibly 5caC decarboxylation. These mechanisms are
not necessarily exclusive and might occur in a context-dependent manner.
1.3.1.2.4 Biological and pathological relevance of 5hmC and TETs
The role of 5hmC as an intermediate in DNA demethylation is critical, yet the
high abundance of the mark in some tissues suggests that 5hmC is more than a transient
residue (Kriaucionis & Heintz 2009). Interestingly, increasing evidence points out that
proteins can bind 5hmC with various affinities and specificities (compared to 5mC),
including some MBDs and UHRF1, and some of these proteins are known to be involved
in gene regulation (Jin et al. 2010; Mellén et al. 2012; Otani et al. 2013; Spruijt et al.
2013). Hence, 5hmC has extensive potential, both for its demethylating role and as an
epigenetic mark of its own. And, although the three human TET genes display a high
similarity in sequence, they are not fully redundant and, to an extent, exert different
functions. Accordingly, both TET expression and 5hmC patterns vary specifically
between tissues (Szwagierczak et al. 2010; Ponnaluri et al. 2017).
The brain and the nervous system constitute a particularly relevant setting to study
the function of TETs and 5hmC because (1) DNA hydroxymethylation levels are the
highest in those tissues, (2) 5mC plays an essential role in neurogenesis, and (3) gene
regulation by active DNA demethylation is particularly pertinent in post-mitotic neurons
(where passive demethylation cannot occur by lack of cell division). Interestingly, several
studies concluded that 5hmC patterns acquired during development are required for
normal neurodevelopment and neurological functions in the adult brain. Notably, Tet1-
mediated 5hmC causes promoter demethylation and activation of growth factor genes
Bdnf and Fgf1 in the adult mouse brain and provides protection against oxidative stress
and neuronal cell death (Guo et al. 2011; Xin et al. 2015; Hsieh et al. 2016). Accordingly,
alterations of 5hmC may contribute to neurodevelopmental and neurodegenerative
diseases, including Rett syndrome, schizophrenia, and Alzheimer’s disease, among others
(Cheng et al. 2015).
Another system in which 5hmC and TETs have been extensively studied is
embryonic stem cells, and initial reports indicated that TET1, in particular, might be
implicated in pluripotency maintenance. Both Tet1 and Tet2 are elevated in mouse ES
cells and in the inner cell mass of the blastocyte; and Tet1-knockdown led to spontaneous
44
differentiation of ES cells (Ito et al. 2010). Another study reported that knockdown of
Tet1 and Tet2 during differentiation of ES cells was also associated with decreased 5hmC
levels at the promoters of pluripotency genes, leading to hypermethylation and gene
silencing (Ficz et al. 2011). However, this notion is in debate, as both Tet1-knockout and
Tet1/Tet2-double knockout mice are able to reproduce, despite some moderate perinatal
lethality and slightly reduced size and weight at birth. Thus, ES stemness could be
maintained in vivo without TET1 and TET2 (Dawlaty et al. 2011; Dawlaty et al. 2013).
As mentioned previously, a massive wave of demethylation of both maternal and
paternal genomes occurs in the zygote and during preimplantation development. Studies
have suggested that 5hmC and passive demethylation both contribute to paternal DNA
demethylation whereas the maternal genome appears to be demethylated mainly through
replication-dependent dilution effect (Fig. 15). Briefly, the paternal pronucleus appears
to be “demethylated” through maternal TET3-mediated oxidations of 5mC (into 5hmC,
5fC and 5caC), and those residues are then diluted in a replication-dependent manner
(Inoue & Zhang 2011). The maternal genome, however, is protected from demethylation
by the exclusion of TET3 by PGC7, a protein highly enriched in the maternal chromatin
(Kang et al. 2013). Interestingly, Tet3-knockout zygotes display a lack of 5mC
conversion into 5hmC in the paternal genome, which delays the subsequent activation of
paternal Oct4 and Nanog genes in early embryos. Loss of maternal TET3 led to reduced
fecundity and neonatal lethality of the offspring (Gu et al. 2011). Therefore, Tet3-
mediated epigenetic reprogramming of the zygotic paternal DNA is essential to early
development.
Fig. 15: View of DNA
demethylation in the
zygote. The maternal
genome is passively
demethylated while the
paternal pronucleus is
oxidized by TET3.
5hmC is then diluted
through successive
rounds of replications.
TE= trophectoderm,
ICM=inner cell mass.
(Adapted from Wu &
Zhang 2011)
45
Similarly to 5mC, DNA hydroxymethylation is vastly affected in cancer. Global
loss of 5hmC (Fig. 16) and impaired TET function have become a new hallmark of
cancers (Haffner et al. 2011; Lian et al. 2012). In hematopoietic malignancies, decreased
5hmC levels have frequently been associated to genetic aberrations in the TET2 gene
(microdeletions, loss of heterozygosity or mutations targeting the catalytic domain). The
high frequency of mutations in leukemia (up to 27% of cases in myelodysplasia) is
thought to be linked to the essential role of TET2 in hematopoietic development and
transformation, which was characterized in several Tet2-deficient mice. In solid cancers,
loss of 5hmC is also observed, despite the lack of any frequent TET mutation. Instead,
impairment of TET activity may be achieved though promoter hypermethylation,
microRNA (miRNA) interference, post-translational regulation or alterations of cofactors
and interactors. These events result in low gene expression, altered protein stability and
localization, or reduced enzymatic activity (Jeschke et al. 2016).
Fig. 16: 5hmC changes in
cancer. The majority of cancers
display a global reduction in
5hmC compared to normal
tissue, which is reflected in
various regions of the genome.
(Adapted from Jeschke et al.
2016)
Genome-wide profiling of 5hmC in various tumor types revealed that loss of
5hmC affects all genomic regions: promoters, gene bodies, intergenic regulatory regions,
and even repetitive elements. Unlike DNA methylation, loss of 5hmC in cancer is not
focused on gene-poor regions and occurs across the entire genome. Accordingly, several
genes were reported to be hypo-hydroxymethylated, e.g. RAC3, IGF1R, and TIMP2 genes
in melanoma (Lian et al. 2012). However, despite the global loss, genes can be affected
by both gain and loss of 5hmC. Importantly, these changes correlate positively with gene
expression. In leukemia, intergenic regions, and, in particular, enhancers, were reported
to be extensively affected by 5hmC changes (Rampal et al. 2014; Rasmussen et al. 2015).
In conclusion, genome-wide studies of DNA hydroxymethylation remain rare, however
they highlight broad changes in the hydroxymethylome. Given that 5hmC is highly tissue-
specific in normal cells, further studies will be required to investigate the diversity of
5hmC changes among tumor types.
46
1.3.1.2.5 Mapping the hydroxymethylome
Several techniques have been recently developed to map 5hmC patterns across the
genome. In this section, we will briefly explain the main existing methods.
First, it is important to note that the bisulfite sequencing (BS-Seq), which is the
gold standard to profile the methylome, cannot in fact distinguish 5mC from 5hmC: both
modifications protect DNA from C to T bisulfite-based conversion. Nevertheless, given
the much higher abundance of 5mC (about 100-fold compared to 5hmC in most tissues),
BS-Seq results are still relevant for the study of the DNA methylome.
Interestingly, 5hmC can be rendered sensitive to bisulfite conversion, by adding a
preceding step of selective chemical oxidation of 5hmC (into 5fC). Hence, in the
oxidative bisulfite sequencing (oxBS-Seq), unmodified C and 5hmC are both converted
to T, whereas in the classical BS-seq only unmodified C will be converted (Booth et al.
2013). Comparison of both profiles provides a quantitative measurement of 5mC and
5hmC in parallel and at single-base resolution. Another adaptation of the BS-Seq is the
Tet-assisted bisulfite sequencing (TAB-Seq). In this case, it is 5mC that is made sensitive
to bisulfite-conversion by TET-mediated oxidation (into 5caC), whereas 5hmC is
protected beforehand from oxidation by a specific glycosylation step (Yu et al. 2012).
Hence, in TAB-Seq, both unmodified C and 5mC are converted to T. Again, comparison
with classical BS-Seq (where only unmodified C are converted) provides 5hmC mapping
at single nucleotide resolution.
Other approaches rely on the enrichment of hyroxymethylated DNA fragments,
followed by high-throughput sequencing. The capture can be achieved by several
methods: (1) by direct 5hmC-targeting immunoprecipitation (hMeDIP), (2) by tagging
5hmC for a biotin pulldown through a specific glycosylation step, which is mediated by
the β-glucosyltransferase enzyme (hMe-Seal), (3) or by bisulfite conversion of 5hmC to
cytosine 5-methylenesulphonate (CMS), followed by immunoprecipitation of CMS by a
specific antibody (Song et al. 2011; Pastor et al. 2012; Nestor & Meehan 2014). While
the resolution of these affinity-based methods is lower than for oxBS and TAB-Seq, the
lower cost and availability of commercial, ready-to-use kits make them very attractive,
particularly for large cohorts of samples.
47
1.3.1.3 Other covalent DNA modifications
Other covalent modifications of the DNA have also been investigated in recent
years. We already mentioned 5-formylcytosine (5fC) and 5-carboxycytosine (5caC), two
residues derived from the further oxidation of 5hmC. In addition, next to cytosine
modifications, methylation of adenine (m6A) has been recently described. In this chapter,
we will summarize emerging data on these “new” DNA modifications.
1.3.1.3.1 Oxidative derivatives of 5hmC
TET enzymes mediate the iterative oxidation of 5mC into 5hmC, 5fC and 5caC.
Although much less abundant than 5hmC, both 5fC and 5caC are detectable in ES cells,
and 5fC is also found in various tissues, including brain, spleen, or liver (Ito et al. 2011).
In mouse ES cells, 5fC is found enriched at CGI promoters and exons of
transcriptionally active genes. Loss of TDG leads to increased 5fC in CGIs and correlates
with increased 5mC in these regions during differentiation of ES cells (Raiber et al. 2012;
Shen et al. 2013). Therefore, 5fC appear to play a role in epigenetic reprogramming of
specific loci, specifically related to DNA methylation regulation. These results were
confirmed in vivo, as TDG also appears to shape 5fC distribution at CGI in mouse
embryos. The mark was also enriched at active enhancers and intragenic regions, with
highly tissue-specific patterns, which suggests a role in embryonic development (Iurlaro
et al. 2016).
Unlike other cytosine modifications, 5caC remains undetectable in most somatic
tissues, which suggests that its role might be predominantly that of a transient mark and
makes the study of this modification very difficult. It is however found in ES cells and
early mouse embryo. Like 5fC, 5caC accumulates upon loss of TDG at intra- and
intergenic regulatory elements (Shen et al. 2013). It was also reported that 5caC
transiently increases during lineage specification of neural stem cells and hepatic
differentiation, before dropping in differentiated cells (Wheldon et al. 2014; Lewis et al.
2017). Therefore, active DNA demethylation seem to occur extensively in the
mammalian genome during early development.
In conclusion, it is still very early day for the characterization of oxidative
derivatives of 5hmC and much effort has yet to be provided in order to fully comprehend
48
their respective roles in the epigenome. Future studies will likely explore the distribution
of these epigenetic modifications in additional tissues. Also, identification of potential
specific binders might expand our understanding of their roles.
1.3.1.3.2 Methylation of adenine
In 2015, it was reported that adenosine can be methylated into N6-
methyladenosine (m6A) in mouse genomic DNA (Koziol et al. 2016). This modification
is extremely infrequent (0.00009% of A), although it is enriched in gene-poor regions. In
mouse ES cells, m6A enrichment correlates with epigenetic silencing of LINE-1
transposons (Wu et al. 2016). The Alkbh1 protein was identified as a demethylase for
m6A, although its writer enzyme is still unknown. In any case, early results indicate that
m6A might constitute an important component of the epigenetic regulation in mammalian
genomes.
Interestingly, m6A has been known to exist in bacterial DNA for a long time, but
its existence in mammalian genome is only a recent discovery. In bacteria, methylation
of adenine by the Dam methylases is well-characterized and is associated with protection
from bacteriophage restriction enzymes, DNA replication and bacterial virulence
regulation (Ratel et al. 2006). The roles of m6A in mammalians and bacteria might be
very different.
1.3.2 Histone modifications
The second category of epigenetic modifications is the post-translational
modification of histone tails (Fig. 17). It is a major type of epigenetic modification with
wide implications in terms of chromatin modulation and gene transcription. Over a
hundred histone modifications are known. Among them, the best-characterized are
acetylation, methylation, phosphorylation, and ubiquitination, all of which can affect
various residues of the histone tails with various consequences. Together, these
modifications form the “histone code” that regulates gene transcription (Bernstein et al.
2007). In this chapter, we will explain the main chemical modifications of histones, their
machineries, and their functions. Since the topic of histone modifications was not the
49
focus of our research, we will restrict our description to the essential facts related to the
matter.
Fig. 17: The main modifications of the four core histones. Many residues of the histone
tails can be chemically modified. Ac, acetylation; Me, methylation; P, phosphorylation;
Ub, ubiquitination. (Adapted from Rodríguez-Paredes & Esteller 2011)
1.3.2.1 Histone acetylation
Histone acetylation corresponds to the transfer of an acetyl moiety from acetyl-
coenzyme A to a lysine residue of the N-terminal histone tail. This reaction is catalyzed
by a family of enzymes called histone acetyltransferases (HATs). The acetyl moiety can
also be removed by enzymes called histone deacetylases (HDACs), thus providing a
dynamic regulation of the mark (Dawson & Kouzarides 2012). HATs can be classified
into 2 main categories: the type A HATs (including the GNAT, MYST and CBP/p300
families) are localized in the nucleus and involved in chromatin regulation whereas the
type B HATs are localized in the cytoplasm and acetylate newly synthesized histones
before nucleosome formation. In contrast, there are 4 classes of HDACs, based on
sequence homology to the enzymes originally identified in the yeast and domain
organization (Witt et al. 2009).
50
Acetylation is a histone mark associated with active transcription. Histone tails
are globally positively charged because of the presence of many lysine and arginine
residues and their positive amine group. These positive charges allow a proper interaction
with the DNA molecule, which is negatively charged due to the phosphate groups of the
molecule’s backbone. Acetylation neutralizes the positive amine charges of the lysine
residues, and thus decreases the binding affinity of histones to DNA, which in turn favor
chromatin opening (Fig. 18). Conversely, removal of the acetyl moiety by HDACs
increases binding of histones to the DNA, and thus promotes chromatin compaction and
silencing of transcription. In addition, acetylation of histone tails also allows the specific
recruitment of transcription factors through their bromodomains.
Fig. 18: Histone acetylation.
Acetylation neutralizes the
positive charge of the lysine
and promotes chromatin
opening. (Adapted from Korzus
2010)
Genome-wide mapping of histones marks is traditionally performed by
chromatin-immunoprecipitation, followed by high-throughput sequencing (ChIP-Seq).
Histone acetylation occurs at many residues, but the main sites are located on histones
H3 and H4 (Bannister & Kouzarides, 2011). Among them, acetylation of histone H3
lysine 4 (H3K4ac), lysine 9 (H3K9ac), and lysine 27 (H3K27ac) are enriched at
promoters of active genes. But histone acetylation is not restricted to promoters and is
also found at cis-regulatory regions. For instance, H3K27ac is also found at active
enhancers.
1.3.2.2 Histone methylation
Histone methylation consists in the addition of a methyl group to a lysine or an
arginine residue of an N-terminal histone tail. There are different levels of methylation,
51
as the residues can be mono-, di-, or trimethylated. Methylation of arginine is catalyzed
by protein arginine methyltransferases (PRMTs), while methylation of lysine is catalyzed
by histone methyltransferases (HMTs). The methyl group can also be removed by histone
demethylases (HDMs), which once again allows a dynamic regulation of the mark
(Dawson & Kouzarides 2012).
The effect of the modification on gene expression depends on the specific residue
targeted and the level of methylation (Fig. 19). Unlike acetylation, methylation does not
neutralize the positive charge of these amino acids. Therefore, the effects of the
modifications are due to the recruitment of specific factors and vary widely. For instance,
trimethylation of histone H3 lysine 4 (H3K4me3) is typically associated with active
promoters, whereas trimethylation of histone H3 lysine 9 (H3K9me3) and lysine 27
(H3K27me3) are associated with gene repression (Mirabella et al. 2016). Other
methylation marks are involved in DNA replication, enhancer activity, transcription
elongation and DNA repair (Mosammaparast & Shi 2010).
Fig. 19: Major methylation events of histones H3 and H4. The effect of histone
methylation depends on the targeted amino acid and the level of methylation. The function
of each mono-, di-, and tri-methylation state is detailed by the color code, as explained
in the figure legend (Adapted from Mosammaparast & Shi 2010).
In embryonic stem cells, certain promoters of developmental regulatory genes,
referred to as “bivalent promoters”, bear both active H3K4 and repressive H3K27
trimethylation marks (Butler & Dent 2013). These promoters are not active as such, but
are poised for transcription. During differentiation, loss of H3K27 methylation marks can
52
lead to a rapid activation of the gene. In contrast, bivalent genes that lose H3K4 methyl
marks will be silenced and targeted by heterochromatin.
1.3.2.3 Other histone modifications
Acetylation and methylation are the two major post-translational modifications of
histones, but other modifications have been characterized in recent years, broadening the
panel of histone marks. In this section, we will briefly explore additional histone marks.
Phosphorylation of histone is a highly dynamic mark that occurs on several amino
acids: serine, threonine, tyrosine, arginine, histidine and lysine. The addition of the
phosphate group is mediated by kinases, with ATP as a cofactor, while the removal of the
phosphate from the histone is mediated by phosphatases (Sawicka & Seiser 2012).
Similarly to acetylation, phosphorylation reduces the positive charge of histones and
influences chromatin structure. The modification can also be bound by specific factors
and has been implicated in a variety of functions, including gene activation, chromosome
condensation and segregation during mitosis and meiosis, DNA repair or apoptosis.
Mono-ubiquitination of histone is a large modification, with the addition of the
76-amino acid peptide that is ubiquitin. It occurs on histone H2A and H2B, where it is
associated with gene repression and transcription initiation, respectively (Bannister &
Kouzarides 2011).
Glycosylation has also been described in the context of histones. The OGT
enzyme can catalyze the addition of the O-linked N-acetylglucosamine (O-GlcNAc)
moiety on serine and threonine residues of histone H2B (Fujiki et al. 2011). This reaction,
referred to as “GlcNAcylation”, can antagonize histone phosphorylation because it occurs
on the same residues. The removal of O-GlcNAc is mediated by the O-GlcNAcase (also
called OGA) enzyme. This modification appears to act as a recruitment signal for the
H3K4me3 machinery, therefore it ultimately promotes gene expression. The O-GlcNAc
mark is closely related to the regulation of metabolic pathways and provides a link
between nutrition and epigenetics (Aquino-Gil et al. 2017).
53
1.3.3 Epigenetic regulation of the chromatin
1.3.3.1 Crosstalk between epigenetic modifications
In previous chapters, we have described the main epigenetic modifications.
However, these modifications do not act as isolated marks, but rather as parts of a
complex system. In fact, various modifications decorate histones at the same time, and
together they form the “histone code”. In addition, the histone modifications interact
closely with DNA modifications. It is the integration of the various epigenetic signals that
ultimately leads to the regulation of chromatin (Fig. 20).
Fig. 20: Crosstalk between DNA and histone modifications. Shades of green indicate
active histone marks, whereas shades of pink represent repressive histone marks. The
orange and yellow colors mark regions of 5mC and 5hmC, respectively. (A) The
distribution of histone marks is illustrated across the promoter region, TSS
(transcriptional start site), and gene body of an active gene. (B) Histone H3 methylation
and DNA methylation are found in the promoter region and TSS in repressed genes. (C)
Bivalent chromatin domains display histone H3K4me3, H3K27me3, and 5hmC. (Adapted
from Butler & Dent 2013).
We have already mentioned the case of bivalent promoters in ES cells, which are
inactive, but poised, promoters of developmental genes that bear 5hmC, as well as active
H3K4me3 and repressive H3K27me3. We will now provide additional examples of how
54
DNA modifications and the histone code can work hand in hand to regulate chromatin
and gene expression.
Histone marks have the capacity to recruit DNMTs and guide DNA methylation.
This occurs, for instance, during the formation of heterochromatin (Fig. 21). Regions of
repressed chromatin are typically marked by H3K9me3. The Heterochromatin Protein 1
(HP1) binds to H3K9me3 through its specific chromodomain and, in turn, recruits
DNMTs in order to methylate DNA. Therefore, post-translational modifications of
histones contribute to shaping the 5mC landscape. The presence of 5mC is not strictly
required for the formation of heterochromatin, however this double regulation allows to
lock tightly the heterochromatin. Conversely, DNA methylation can also influence
histone marks. For instance, 5mC found in heterochromatin can be bound by MBDs
which subsequently recruit HDACs. The deacetylation of histone is an additional
mechanism that represses gene expression, as the newly deacetylated H3K9 is then
available for trimethylation and further spreading of the heterochromatin.
Fig. 21: Cooperative regulation and
gene silencing. Gene repression in
heterochromatin occurs through a
multi-protein complex. The HP1 protein
recognizes H3K9me3. This eventually
leads to the recruitment of DNMTs for
DNA methylation, HDACs for histone
deacetylation and HMTs for the
spreading of heterochromatin through
additional H3K9 methylation. (Adapted
from Feinberg & Tycko 2004).
Another functional link has been established recently by our host laboratory, and
others, between histone glycosylation and DNA hydroxymethylation machineries in the
context of gene activation (Fig. 22) (Deplus et al. 2013; Chen et al. 2013; Vella et al.
2013). The enzyme responsible for histone GlcNAcylation, OGT, can interact with the
TET proteins, independently of their oxidation activity. Through this association, TETs
recruit OGT on CGI promoters and enhances its enzymatic activity. Subsequently, OGT
glycosylates its target histone H2B, as well as other target proteins. Among them is HCF1,
a key subunit of the H3K4 methyltransferase SET1/COMPASS complex. Glycosylation
of HCF1 stabilizes the complex, which favors H3K4me3 and gene activation. In
55
conclusion, TETs act as scaffolding proteins by recruiting and activating OGT, which in
turn promotes the H3K4me3 machinery and transcription.
Fig. 22: Connecting TETs and
OGT. (1) TET–OGT interaction
promotes OGT GlcNAcylation on
numerous proteins, including HCF1.
(2) In a TET‐dependent manner, a
GlcNAcylated HCF1 stabilizes the
SET1/COMPASS complex. (3) Both
TET proteins and OGT activity favors
histone H3K4me3 and subsequent
transcriptional activation. (Adapted
from Deplus et al. 2013).
In conclusion, epigenetics brings together several machineries in order to regulate
gene expression. The relations between these different machineries give a more complex
picture, in which different modifications can either reinforce each other (e.g. H3K9me3
and 5mC in heterochromatin) or, on the contrary, balance each other (e.g. H3K4me3 and
H3K27me3 in bivalent promoters of ES cells). As of now, little is known about these
connections, yet they are key to understanding the intricacies of gene regulation. And
considering that new epigenetic modifications are found every year, there is still much
work to be done in that regard.
1.3.3.2 Chromatin remodeling
In addition to the previously described modifications of DNA and histone, a third
epigenetic mechanism can be involved in the regulation of the chromatin, which we will
briefly describe in this section.
Chromatin remodeling is an ATP-dependent mechanism mediated by the
SWI/SNF complexes. In human cells, these are large, multi-protein complexes containing
a single ATPase protein (either BRM or BRG1) and numerous core and accessory
subunits. The genes encoding these subunits belong mostly to the SMARC, ARID and
BCL families (Masliah-Planchon et al. 2015). SWI/SNF complexes are traditionally
divided in two categories, the “BAF complexes” and the “PBAF complexes”, based on
the presence of certain core subunits (Fig. 23, left panel). However, the composition of
56
these complexes is highly variable, with multiple possible paralogues, and changes
widely depending on the cellular context. Thus, they should not be viewed as stable
complexes with a restricted set of well-defined subunits, but rather as a collection of
highly variable complexes (Wang et al. 1996; Mohrmann & Verrijzer 2005).
The chromatin remodeling complexes can affect histone-DNA interactions, using
the energy released by ATP consumption, and thus deeply change the organization of the
chromatin. This process involves nucleosome sliding, dissociation or replacement, and
these changes in the chromatin organization can either activate or repress transcription of
genes. Interestingly, SWI/SNF subunits contain domains interacting with both histones
and DNA, suggesting once again that different levels of epigenetic regulation can
influence one another. More broadly, members of the SWI/SNF complexes can recruit
transcription factors, as well as modulators of transcriptional activity (either coactivators
or repressors) (Helming et al. 2014). Hence, SWI/SNF complexes can influence
chromatin organization through several mechanisms.
Fig. 23: SWI/SNF complexes. SWI/SNF complexes are found in two major subtypes,
BAF and PBAF, and comprise multiple subunits (left). SWI/SNF complexes contribute to
transcription modulation by mobilizing nucleosomes and by interacting with
transcription factors, coactivators, and corepressors on DNA. Subunits found mutated in
cancer are denoted by a red star and are described in the table (right). (Adapted from
Helming et al. 2014)
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SWI/SNF complexes have been shown to play critical roles in physiology, notably
in the regulation of the balance between stemness and differentiation. For instance, there
is a shift in the composition of the SWI/SNF complex during the transition from neural
stem cells and progenitors to post-mitotic neurons, as BAF45a and BAF53a subunits are
replaced by BAF45b, BAF45c and BAF53b subunits (Lessard et al. 2007). SWI/SNF
subunits have also displayed critical roles in adipogenesis, osteogenesis and
hematopoiesis (Wilson & Roberts 2011).
Furthermore, SWI/SNF complexes are involved in the regulation of many genes
related to proliferation and/or cell motility, such as cell cycle regulator p16INK4A, oncogene
c-MYC, or genes of the Rho GTPase family. In direct link with this, alterations of
SWI/SNF chromatin remodeling have been implicated in multiple cancers (Fig. 23, right
panel). For instance, SMARCB1, which is now considered as a bona fide tumor suppressor
gene, is repressed in cancer, either by loss-of-function mutation (both somatic and
germline) or downregulation (Margol & Judkins 2014). In particular, repression of this
SWI/SNF subunit is involved in the development of the aggressive malignant rhabdoid
tumors. Numerous other SWI/SNF subunits are also known to be dysregulated in cancer,
including ARID1A, ARID2, SMARCA2, SMARCC1, and SMARCC2 (Masliah-Planchon
et al. 2015). The involvement of chromatin remodeling complexes in breast cancers will
be mentioned in a subsequent chapter (see section 2.5.1 Epigenetic alterations in breast
cancers).
58
1.3.4 RNA modifications
Next to classical modifications of the chromatin, i.e. histone and DNA
modifications, a new field of research is emerging: RNA epigenetics. This domain, also
referred to as epitranscriptomics, investigates the post-transcriptional modifications of
RNA.
Over 100 distinct chemical modifications have been identified so far, and,
together, they constitute the “epitranscriptome” of our cells, by analogy to the epigenome
(Liu & Pan 2017). Already, the field of RNA epigenetics is changing our view on the
central dogma of biology, as it is clear that RNA transcripts are not merely transient
copies of the DNA. Instead, just like the chromatin, they constitute a true level of
regulation with the potential to fine-tune the functions of the genes. And these
modifications affect the various classes of RNA, from messenger RNAs (mRNAs) (Fig.
24), to ribosomal RNAs (rRNAs) or transfer RNAs (tRNAs). Since RNA epigenetics is
still in its early days, most of these modifications have not been well characterized yet.
Nevertheless, they represent an intriguing new field of research that will undoubtedly
expand in the years to come.
In this chapter, we will explore the main modifications of the mammalian
transcriptome, their machineries and biological relevance.
Fig. 24: Chemical modifications in eukaryotic mRNA. (Adapted from X. Li et al.
2017).
59
1.3.4.1 Methylation of adenosine
The methylation of adenosine is, by far, the most abundant modification of mRNA
to be identified. However, the low expression of mRNAs compared to rRNAs and tRNAs
limited for many years the possibilities of research on this modification. Recently, m6A
has regained the interest of the scientific community, due to advances in biochemical
approaches and sequencing technologies, as well as a better understanding of the players
involved in the regulation of m6A (X. Li et al. 2017).
1.3.4.1.1 m6A distribution
As previously mentioned, m6A is a frequent modification in mRNA, with about
1% to 2% of all adenosines being methylated and approximately 2 methylated sites per
transcript (Pan 2013; Yue et al. 2015). The exact abundance of the mark varies depending
on the biological context. In 2012, two independent studies reported the first mappings of
m6A in RNA by immunoprecipitation, followed by high-throughput sequencing (MeRIP-
Seq), and identified thousands of targets. Profiling of m6A across the mammalian
transcriptome revealed that the mark was preferentially found enriched near the
transcription start site (TSS), around the stop codons, and in the 3’ untranslated regions
(3’ UTR) (Fig. 25) (Dominissini et al. 2012; Meyer et al. 2012). Methylated sites
correlated with evolutionary conserved regions between human and mice, suggesting that
the function of m6A might be conserved between species.
Fig. 25: Metagene profiles of m6A. Metagene analysis of MeRIP–Seq data shows that
m6A is enriched around the TSS and the stop codon of mRNA transcripts. (Adapted from
Meyer & Jaffrey 2014)
How specific regions of RNAs are targeted for methylation is still unknown,
however consensus motifs of m6A have been identified, with the most predominant one
60
being GGm6ACU. Nevertheless, the majority of the regions corresponding to the m6A
consensus motifs were in fact not methylated, suggesting that m6A mRNA modification
is regulated by more factors than simple sequence recognition.
1.3.4.1.2 The m6A machinery
The m6A methyltransferase machinery is a multi-protein complex, consisting of
two active methyltransferases, METTL3 and METTL14, in complex with the
catalytically inactive protein WTAP (Fig. 26) (Maity & Das 2016). METTL3 was the first
m6A methyltransferase identified and displays a SAM-binding domain. METTL14 is a
close homologue, with similar activity, that was shown to interact with METTL3.
Depletion of these enzymes leads to a decrease of m6A, both in vitro and in vivo (Y.
Wang et al. 2014). WTAP, a protein previously known to be involved in mRNA splicing,
was also reported to interact with the core complex of METTL3 and METTL14.
Strikingly, knockdown of WTAP reduced m6A levels more significantly than knockdown
of METTL3 or METTL14, despite the lack of enzymatic activity of the protein (Liu et al.
2014). This suggest that WTAP is required for the proper activity of the METTL3-
METTL14 complex.
Just like DNA and histone modifications, m6A is a dynamic mark that can be
removed from the RNA (Fig. 26). The first m6A demethylase to be identified was the fat
mass and obesity-associated protein (FTO) (Jia et al. 2011). Reports indicated that
knockdown of FTO increased m6A levels, while overexpression decreased them, both in
vitro and in vivo. Recent results indicate that FTO oxidizes m6A into 6-
hydroxymethyladenosine and 6-formyladenosine, suggesting an oxidative demethylation
process similar to TETs and 5mC in the DNA (Fu et al. 2013). A second m6A
demethylase was later identified: ALKBH5 (Zheng et al. 2013). This enzyme, unlike
FTO, catalyzes the direct removal of the methyl group from the adenosine.
Several proteins were suggested to preferentially bind (or be excluded from) m6A-
containing RNAs (Fig. 26). Of note, m6A appears to destabilize the potential binding to
the opposing U base within a hairpin RNA structure, thus forming a single-strand region.
The accessibility of RNA, in turn, allows a better binding of HNRNPC, an abundant
nuclear protein involved in pre-mRNA processing (Liu et al. 2015). In contrast, m6A-
dependent alteration of the RNA structure blocks the binding of HuR, a mediator of post-
transcriptional regulation (Dominissini et al. 2012; Y. Wang et al. 2014). Beyond
61
structure alteration, several proteins were also identified as specific m6A binders,
including the YTHDF1-3 and YTHDC 1-2 proteins (Zhang et al. 2010). In particular,
YTHDF2 was shown to bind the methylated 3’UTRs and to be involved in mRNA
degradation regulation (X. Wang et al. 2014). In conclusion, m6A affects the interaction
of proteins to RNA, and this binding is, at least in part, responsible for the function of
m6A.
Fig. 26: The writer, eraser and
reader proteins of m6A.
Mammalian m6A writers function as
a protein complex with three
components: METTL3, METTL14,
and WTAP. Two m6A erasers have
been reported: FTO and ALKBH5.
The function of m6A is mediated
partly by reader proteins, which
have been identified in members of
the YTH domain-containing protein
and the heterogeneous nuclear
ribonucleoprotein (HNRNP) protein
families (Adapted from Zhao et al.
2016).
1.3.4.1.3 Biological relevance of m6A
A real breakthrough was achieved in recent years with the identification of key
functions of m6A in various levels of regulation of gene expression, and a link with
biological processes such as stemness and metabolism. This is carried out, at least in part,
by the interaction with specific m6A binder proteins (Fig. 27a).
Emerging evidence points towards a mechanistic relationship between m6A and
splicing regulation (Fig. 27c). All the m6A writers (METTL3, METTL14 and WTAP)
and erasers (ALKBH5 and FTO) have been localized, at least partially, in nuclear
speckles, which are important structures for pre-mRNA processing (Maity & Das 2016).
Knockdowns of METTL3, WTAP, ALKBH5 or FTO were all shown to affect splicing in
various biological contexts. This is achieved by m6A-mediated regulation of the binding
of various factors involved in splicing, including HNRNPC, YTHDC1 and SRSF2 (Zhao
et al. 2014; Liu et al. 2015; Xiao et al. 2016).
62
Several recent studies also suggest that m6A might play a role in translation,
generally promoting its efficiency (Fig. 27d). Two m6A binding proteins were suggested
to enhance translation: YTHDF1, which is known to interact with translation initiation
factors; and eIF3, which is a key translation initiation factor (X. Wang et al. 2015; Meyer
et al. 2015). Identification of additional m6A binding proteins might improve our
understanding of the mark’s association with translation.
Furthermore, m6A has been described to regulate RNA stability (Fig. 27e).
Studies in cells depleted for METTL3, METTL14 and FTO have in turn associated m6A
with increased or decreased levels of transcripts. Enhanced stability was rather observed
for mRNAs with methylated introns, for which loss of m6A might lead to improper
splicing and subsequent degradation of the transcript. In contrast, m6A-mediated binding
of YTHDF2 (a promoter of RNA degradation) and blocking of HuR binding (a known
mRNA stabilizer), was associated with decreased half-life and mRNA decay (X. Wang
et al. 2014).
Fig. 27: Mechanisms
and functions of m6A.
Several mechanisms
have been attributed to
m6A in relation with
protein binding, RNA
base pairing, splicing,
translation, stability and
degradation. (Adapted
from Meyer & Jaffrey
2014)
63
In mammalian ES cells, m6A appears to regulate stemness and cell fate transition
by promoting the transition from naïve pluripotency towards differentiation. Studies have
suggested that loss of m6A led to an increase in pluripotent markers and difficulties to
differentiate (Batista et al. 2014; Geula et al. 2015). Accordingly, Mettl3-knockout
blastocysts displayed an inability to repress pluripotent genes and ES cells formed poorly
differentiated teratomas in vivo (Batista et al. 2014).
Regulation of m6A has also been linked to obesity and adipogenesis. Variants of
FTO have been associated with childhood obesity in genome-wide association studies,
hence the name of the protein (fat mass and obesity-associated protein) (Farooqi 2011).
Also, FTO was suggested to promote obesity in mice by promoting food intake and
adiposity while decreasing energy expenditure (Fischer et al. 2009; Church et al. 2010).
In line with this, FTO-mediated demethylation of the RUNX1T1 mRNA was associated
with splicing regulation of this adipogenic regulatory factor (Merkestein et al. 2015).
The role of m6A in diseases remains unclear to date. Nevertheless, the mark has
recently been implicated in cancer. In MLL-rearranged leukemia, FTO appears to
demethylate a set of transcripts, including key regulators ASB2 and RARA, decreasing
their stability and thus promoting leukemogenesis (Z. Li et al. 2017). Similarly, in
glioblastoma, reduced m6A levels promoted tumorigenesis. Overexpression of METTL3
or chemical inhibition of FTO suppressed the progression of stem cell-mediated tumor
through the regulation of key transcripts, including ADAM19 and FOXM1 (Cui et al.
2017; Zhang et al. 2017). Thus, while it is still early days, m6A-mediated regulation
appears to play an important role in cancer. Given the complexity of the disease, and the
variety of tissues in which it occurs, additional studies are required to better understand
its function in malignancies.
1.3.4.2 Methylation of cytosine
Just like DNA, RNA can be methylated on the 5’ carbon of a cytosine residue to
form 5-methylcytosine (here abbreviated as 5mrC, to avoid confusion with the DNA
modification 5mC). This modification was first studied in the context of tRNAs, however
emerging evidence also points out towards a role in the regulation of mRNAs.
64
1.3.4.2.1 5mrC in non-coding RNAs
DNMT2, a member of the DNA methyltransferase family, has been shown to
methylate several tRNAs, including those mediating the addition of aspartate, valine and
glycine (Fig. 28) (Schaefer et al. 2009). This methylation event has been linked to
increased tRNA stability and protein synthesis. Another enzyme, NSUN2, was also
implicated in the methylation of tRNAs. In line with this idea, loss of both Dnmt2 and
Nsun2 in mice led to a drastic decrease in 5mrC, as well as a global decrease in protein
synthesis (Tuorto et al. 2012).
Aside from tRNAs, methylation also occurs on other non-coding RNAs (Fig. 28).
Notably, loss of Nsun2-mediated 5mrC was reported to cause aberrant processing of
small RNA fragments that can function as miRNAs (Hussain, Sajini, et al. 2013). It was
also reported that two well-known long non-coding RNAs (lncRNAs), HOTAIR and
XIST, display 5mrC around regulatory regions mediating interaction with protein
complexes (Amort et al. 2013). And in rRNA, 5mrC could be involved in translation
regulation and tRNA recognition (Chow et al. 2007).
1.3.4.2.2 5mrC in mRNAs
The existence of 5mrC in mRNA was known for decades (Dubin & Taylor 1975),
yet the focus of research remained on tRNA methylation for many years. However, the
study of mRNA methylation recently gained attention with the first mappings of
transcriptome-wide 5mrC distribution (Squires et al. 2012; Khoddami & Cairns 2013;
Edelheit et al. 2013). The mark was found relatively enriched in both 5’ and 3’UTR, and
slightly depleted in coding regions. Of note, the enrichment in the 3’ UTR and near
Argonaute binding sites suggested that 5mrC in mRNA could be associated with miRNA
degradation pathway, although this remained to be demonstrated (Fig. 28). The exact
function of 5mrC in mRNA is not yet clarified.
The identification of the enzymes responsible for mRNA methylation remains in
debate, as there are at least six potential RNA cytosine methyltransferases in mammals,
in addition to Dnmt2 and Nsun2. Based on sequence homology in the catalytic domain,
Nsun1 and Nsun3-7 are all predicted to methylate RNA, although there might be some
substrate specificity. In addition, Nsun1, 2 and 5 have been identified as mRNA-binding
proteins (Hussain, Aleksic, et al. 2013).
65
Fig. 28: Functions of 5mrC. Cytosine methylation on tRNAs and non-coding RNAs have
been associated with cleavage, processing and stability of the RNA. The function of
methylation in mRNAs is still unclear but could be linked to miRNA-mediated interference
(Adapted from Blanco & Frye 2014)
1.3.4.2.3 Oxidation of 5mrC into 5hmrC
It was recently discovered that, similar to what occurs in DNA, TET enzymes can
mediate the formation of 5-hydroxymethylcytosine in RNA (here abbreviated as 5hmrC,
to avoid confusion with the DNA modification 5hmC) (L. Fu et al. 2014; Huber et al.
2015). The modification was significantly enriched in poly-adenylated RNA (polyA
RNA), which contains mostly mRNAs and lncRNAs. In terms of abundance, 5hmrC was
about 1000-fold less abundant than 5mrC in total RNA, although this ratio dropped to 25-
fold in polyA RNA. The presence of the mark specifically in polyA RNA suggest that
5hmrC could play a role in the regulation of mRNAs and/or lncRNAs.
1.3.4.3 Mapping RNA modifications
Several techniques have been recently developed to map RNA modifications
across the transcriptome. Most of these methods are adapted from DNA or histone
modification mapping techniques. They are based on chemical modifications of the
nucleotides, immunoprecipitation, or a combination of both.
The principle of the bisulfite (BS) treatment, which is the gold standard to identify
5mC in DNA, can also be used to map 5mrC in RNA: unmethylated cytosines are
chemically converted into thymidines while 5mrC are unchanged. This technique
displays a single-nucleotide resolution; however, the treatment is rather aggressive to
RNA and can lead to degradation. It has nevertheless been used in previous studies, in
combination with high-throughput sequencing (Squires et al. 2012; Amort et al. 2017).
66
Perhaps the most common technique used to study the distribution of RNA
modifications is the immunoprecipitation of RNA (RIP) followed by high-throughput
sequencing. It can be used to detect both RNA modifications and RNA-protein
interactions. In the case of methylation (e.g. 5mrC or m6A), this method is called MeRIP,
and has been successfully used in a number of studies (Dominissini et al. 2012; Meyer et
al. 2012; Amort et al. 2017). As all immunoprecipitation-based methods, RIP techniques
rely on the availability of a specific antibody targeting the modification and its resolution
is limited to about 100-200bp.
Another fundamental approach to map RNA modification was derived from the
RIP method. This technique relies on the use of ultraviolet (UV) light to irreversibly
crosslink RNA to binding proteins (similar to the use of formaldehyde in ChIP), hence its
name, crosslinking immunoprecipitation (CLIP). The main advantage of the crosslinking
step is that it allows more stringent washes during the IP, thus CLIP is theoretically more
specific than the traditional RIP. The CLIP principle was further expanded into several
adapted methods, one of which, called m6A individual-nucleotide resolution using CLIP
(miCLIP), was specifically designed to map m6A. The principle is to induce a mutational
signature with an anti-m6A antibody and UV-induced antibody-RNA crosslinking,
followed by retro-transcription. Analyzing the specific mutational signature subsequently
allows to map m6A at the single nucleotide resolution (Linder et al. 2015).
67
2. Breast cancers
2.1 Introduction
Breast cancer (BC) is a malignant disease that originates in the mammary gland.
It is a major health burden worldwide with about 1.7 million diagnoses in 2012 (WHO
2012). In women, it is the most common cancer (Fig. 29), and the second deadliest, just
after lung cancer (Siegel et al. 2016). Most BCs are diagnosed in patients above 50 years
of age. Approximately 1 out of 3 women will develop a breast cancer throughout their
life and 1 out of 8 will die overall. Progress in the comprehension of the disease and in
treatment opportunities have improved the survival rate in the past decades: the 10-year
age-standardized net survival for BC in women has increased from 40% from 1971 to
78% in 2011. Nevertheless, with around 450 000 deaths every year, BC remains a major
issue worldwide, and the survival rate varies greatly from one country to another. In
Europe and North America, up to 80% of women survive their breast cancer, whereas the
numbers drop below 40% in low-income countries (WHO 2012).
Fig. 29: Ten leading cancer types. Estimated new cancer cases by sex in the United
States, 2016 (Adapted from Siegel et al, 2016)
The etiology of BC is complex and not fully understood, with many potential risk
factors (Dumalaon-Canaria et al. 2014). Familial antecedents, particularly in young, first-
68
degree relatives, increase significantly the risk of developing BC. Such cases have been
associated with hereditary mutations in tumor suppressor genes, such as the DNA repair
genes BRCA1 and BRCA2. However, so-called familial BC only accounts for less than
10% of all BC, whereas sporadic (non-familial) cancers represent the vast majority of
BCs. Factors associated with hormonal status have also been linked to higher risk of BC
(e.g. late first pregnancy, no pregnancy, no breast-feeding, late menopause, hormone
replacement therapy). Other factors could be attributed to lifestyle (e.g. obesity, alcohol
consumption, diet) or environment (e.g. exposure to pesticides or heavy metal cadmium).
Importantly, BC constitutes a very heterogeneous disease that could be seen as a
set of several malignant conditions whose common feature is essentially the tissue of
origin. In adult women, the breast contains glandular tissue, fibrous stroma, and fat tissue
(Fig. 30, left panel) (Jesinger 2014). The mammary gland, which is the functional part of
the breast, is constituted of 10 to 20 lobules, responsible for milk production, and a series
of ducts that collect and drain the milk towards the nipple. Within breast lobules, the
epithelium is formed of a single layer of luminal cells, surrounded by an underlying layer
of basal myoepithelial cells (Fig. 30, right panel). During breastfeeding, prolactin induces
luminal cells to secrete milk in the lumen, and oxytocin elicits the contraction of the
myoepithelial cells to eject the milk from the lobule.
Fig. 30: Anatomy of the breast. Schematic representation of the lobular and ductal
systems. (Adapted from http://humanbiologylab.pbworks.com/w/page/104941359
/Histology%20of%20the%20Mammary%20Gland)
69
2.2 Diversity of breast cancers
The heterogeneity of BC is a huge hurdle in terms of patient care-taking, as they
can display very different biological features, as well as different clinical evolutions.
Hence, clinicians and biologists have long sought to classify BC into different subtypes,
in order to improve our understanding of the disease and therapy management. In this
section, we will present the main classifications used to categorize breast cancers.
2.2.1 Histopathological classifications
The first classification of BC, into the so-called histological types, relies on the
detection by pathologists of specific morphological and cytological features of the
tumors. BCs are first divided based on the extent of the disease: malignancies that are
contained within the tissue of origin, without any breach of the basal membrane, are called
in situ carcinoma, whereas those that extend beyond are called infiltrating or invasive
carcinoma. BCs are also distinguished by their origin, i.e. ductal versus lobular.
Histopathological observations further refine the classification into many histological
types (up to 21, depending on the taxonomy) (Dieci et al. 2014). Based on these criteria,
the most common type of BC is the invasive ductal carcinoma “not otherwise specified”
(IDC-NOS) or of “no special type” (IDC-NST), with up to 75% of all BC. In brief, this
IDC type represents the adenocarcinoma that do not display any particular feature that
would allow to distinguish them. Taken together, all the other “special” histological types
represent the remaining 25% of BC. Among them, the most common is the invasive
lobular carcinoma (ILC), with approximately 10% of BC.
Another classification commonly used by pathologists is the histological grade,
which is an assessment of the aggressiveness of the tumor, based on three morphological
features: tubule formation, mitotic count, and nuclear pleomorphism (Filho et al. 2011).
Each one is graded, and the combined score divides the tumors from grade 1 (poorly
proliferating and highly differentiated) to grade 3 (highly proliferative and poorly
differentiated). Unfortunately, almost half of the patients are classified into the
intermediate grade 2, which is not very informative for clinicians as patients of this group
display mixed phenotypes and survivals. However, an improved version of the
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histological grade, called the genomic grade index (GGI) allows to further distinguish
grade 2 patients into low and high risk categories, with outcomes similar to grade 1 and
3, respectively (Sotiriou et al. 2006). The GGI classification is based on a 97-gene
signature, focused on proliferation and cell cycle markers.
In routine, invasive BCs are often sorted based on the “tumor node metastasis”
(TNM) classification. This stratification is based on three criteria: the size of the tumor
(T), the infiltration of tumor cells in the armpit lymph nodes (N), and the metastasis status
(M). Based on the combined score, BCs are sorted into tumor stages I to IV, in increasing
order of aggressiveness (Fig. 31).
Fig. 31: TNM stage is a predictor of overall
survival. Patients with stage III and stage IV
BC have significantly worse overall survival
than patients with stage I and stage II breast
carcinoma (Adapted from Orucevic et al.
2015).
2.2.2 Molecular classifications
Extensive research has been pursued in order to identify molecular biomarkers
that might appropriately reflect the intrinsic diversity of BC, as well as to predict the
response to certain therapies. Nowadays, 4 standard biomarkers are evaluated by
immunohistochemistry (IHC) in routine. Two of them, the estrogen receptor (ER) and the
progesterone receptor (PR), identify a group of patients that could benefit from endocrine
therapies. Approximately 65% of all BCs fall in this category, and they are considered of
better prognosis overall. The third biomarker, the HER2 growth factor receptor, defines
a group that can be treated with the recently-developed HER2-targeted therapies. The
Erbb2 gene, coding for HER2, is amplified in about 10-20% of all BC and leads to an
oncogenic activation of the phosphatidylinositol-3-kinase (PI3K) signaling pathway.
Tumors that are negative for ER, PR and HER2 are commonly referred to as “triple
71
negative” and do not benefit, as of today, of any specific therapy. The fourth biomarker,
Ki67, is expressed in all proliferative cells and thus reflects the rate of proliferation within
a tumor. Taken together, these 4 biomarkers help, to some extent, the clinicians in the
therapy decision-making.
With the recent progresses in transcriptome-wide technologies, many research
groups have turned to transcriptome-wide expression analyses in order to find an unbiased
approach to the topic of BC diversity. According to the “intrinsic” classification described
by Perou et al., BC can be classified into at least four subtypes: luminal A and B, HER2-
like (or “HER2-enriched”), and basal-like (Perou et al. 2000). Similar results were found
by numerous studies in the following years (Sorlie et al. 2001; Sorlie et al. 2003; Sotiriou
et al. 2003; Hu et al. 2006). Interestingly, there is a striking correspondence between these
four subtypes and the biomarkers routinely used by pathologists (Fig. 32). Luminal A
cancers (approximately 40-50% of all BC) are mainly ER-positive and/or PR-positive, of
low grade and low Ki67. These correspond to the subtype with the best overall survival.
Luminal B cancers (about 20-25% of all BC) are also ER-positive and/or PR-positive, but
of high grade and high Ki67. They are more aggressive than their luminal A counterpart.
HER2-like cancers (10-15% of all BC) are predominantly HER2-positive, hence the name
of this group. They are also often of high grade. Basal-like cancers (10-20% of all BC)
are mostly “triple negative” and of high grade. These last two subtypes correspond to
aggressive subtypes. A signature, commonly referred to as
“PAM50”, was developed based on the expression of 50
genes in order to classify BC into these 4 subtypes (Parker
et al. 2009; Wallden et al. 2015).
Fig. 32: Expression subtypes are associated with clinical
features. Tumor samples are grouped by mRNA subtype:
luminal A (n=225), luminal B (n=126), HER2-enriched
(n=57) and basal-like (n=93). Clinical data are shown as
follows: ER/PR/HER2 IHC status (dark grey: positive;
white: negative; light grey: N/A or equivocal); “T” tumor
size (dark grey: T2–4; white: T1; light grey: N/A or
equivocal) and “N” node status; (dark grey: positive;
white: negative; light grey: N/A or equivocal). (Adapted
from Koboldt et al. 2012).
72
Additional gene expression-based subtypes have been described, although they
are rare and less clearly defined than the main four subtypes. The normal-like subtype (3-
6% of all BC) is, as its name suggests, the closest to normal breast in terms of gene
expression patterns. This group is also characterized by intermediate or good survival
(Weigelt et al. 2010). The claudin-low subtype (about 5% of all BC) represents a very
heterogeneous group, enriched in both triple negative and ER-positive tumors and it is
characterized by intermediate survival rates (Herschkowitz et al. 2007; Sabatier et al.
2014). Based on gene expression, the claudin-low subtype constitutes the most
undifferentiated tumors.
Further research has been performed in recent years in order to refine the
molecular classification of BC (Dedeurwaerder et al. 2011; Lehmann et al. 2011;
Netanely et al. 2016). Worth mentioning, a study performed on an extensive cohort
(nearly 2000 BC patients) suggested the existence of up to 10 BC subtypes with distinct
clinical outcomes, based on the integrative analysis of copy number and gene expression
data (Curtis et al. 2012). Some of the identified clusters could be associated with the
known expression subtypes. However, many clusters were a mix of different subtypes,
which indicates an even higher degree of complexity in BC. The Cancer Genome Atlas
(TCGA) consortium went one step further by comparing multiple levels of regulations in
a pan-cancer study. In the context of BC, they highlighted the existence of four main
subtypes by combining data from microRNAs, DNA methylation, copy number, PAM50
mRNA expression, and protein expression in a cohort of over 500 patients (Koboldt et al.
2012). Each level of gene regulation showed significant heterogeneity. Interestingly,
protein expression highlighted two potential novel subtypes that might be related to the
microenvironment of the tumor, thus illustrating that BC displays heterogeneity at many
levels and that genetics and RNA expression alone might not be sufficient to fully
understand the complexity of the disease.
2.3 The immune system: a double-edged sword
Aside from malignant cells, breast and other tumors contain various cell types,
which, together, are referred to as the tumor microenvironment (TME). The TME is a
major contributor to BC heterogeneity and its composition varies between tumor types,
73
and stages of tumor development (Becht et al. 2016). The TME is recruited by cancer
cell-derived factors and includes immune cells, fibroblasts, adipocytes and endothelial
cells. In turn, TME cells influence the behavior of cancer cells with different outcomes in
terms of survival and response to therapy.
In this section, we will focus on the role of immune cells of the TME and their
clinical relevance in BC. We will then focus on a major regulator of immunity and
inflammation, the NF-κB family.
2.3.1 Immune infiltration and its clinical relevance
The role of the immune system is dual in cancer. On one hand, proper immune
reactions, such as immune surveillance, are required both for preventing tumor
development and for efficient anti-tumor response during treatment. On the other hand,
inadequate immune responses, such as chronic inflammation and immune escape
mechanisms, support the development and progression of cancer.
Immune cells are one of the major cell types found in the TME and they carry
important diagnostic information in cancer. But the immune reaction in cancer is very
complex, and different subtypes of immune cells have been associated with differential
prognostic and predictive values. Overall, the abundance of tumor-infiltrating
lymphocytes (TILs) is associated with a good clinical outcome in breast and other cancers
and is predictive of good response to chemotherapy (Aaltomaa et al. 1992; Dieci et al.
2015; Melichar et al. 2014). Nevertheless, distinct subtypes of lymphocytes can display
very different roles in BC. For instance, cytotoxic T cells (or CD8+ lymphocytes), which
are effector cells that can recognize and destroy cancer cells, have been associated with a
good outcome and response to therapy in many studies in BC (Mahmoud et al. 2011; S.
Liu et al. 2012; Seo et al. 2013). In contrast, high frequency of regulatory T cells , which
suppress immune surveillance, correlates with tumor grade and reduced patient survival
(Lança & Silva-Santos 2012). T-helper cells (or CD4+ lymphocytes) can act as both anti-
or pro-cancer agents, depending on their cytokine profile (e.g. TH1, TH2; TH17 profiles)
and their clinical relevance vary accordingly (Becht et al. 2016). The role of B
lymphocytes, which are mediators of humoral immunity, is in debate, studies having
shown contrasting results and attributing to B lymphocytes both supportive and
suppressive effects on tumor progression (Nelson 2010). Macrophages, like CD4+
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lymphocytes, display distinct effect based on their polarization: classically activated M1
macrophages and alternatively activated M2 macrophages are thought be anti- and pro-
tumorigenic in nature, respectively (Biswas & Mantovani 2010).
With such contrasting influences, the overall prognostic effect of the immune
microenvironment seems to be the balance between the different immune cell subtypes
(Fig. 33). For example, breast cancer patients with tumors characterized by a high
macrophage, high CD4+, but low CD8+ T-cell signature had a significantly decreased
recurrence-free survival than patients with tumors that showed a low macrophage, low
CD4+ and high CD8+ T-cell signature (DeNardo et al. 2011).
Fig. 33: Balance of the immune TME. The elimination of tumor cells involves CD4+
T-helper (TH1, TH2 and TH17) cells, CD8+ cytotoxic T cells, γδT cells, natural killer
(NK) cells, natural killer T (NKT) cells; M1 macrophages and dendritic cells. Escape
from the immune response include myeloid derived suppressor cells (MDSCs), regulatory
T (TReg) cells, TH17 cells and M2 macrophages. (Adapted from Kansara et al. 2014)
2.3.2 The NF-κB signaling pathway
Beyond tumor infiltration by immune cells, activation of immune and
inflammatory signaling pathways are also common events in cancer cells. Among them,
the Nuclear factor kappa B (NF-κB) is a major regulator, originally identified as a critical
transcription factor for the development, survival, and activation of leukocytes, including
B and T lymphocytes, as well as macrophages (Gerondakis & Siebenlist 2010). In
mammalian, NF-κB family is composed of five members, p65 (RelA), RelB, c-Rel, p50
(NF-κB1), and p52 (NF-κB2), which form dimers that can act as transcriptional activators
or repressors, directly binding to their target DNA sequence. In the canonical pathway
(Fig. 34), the p50-p65 dimer is kept inactivated in the cytoplasm by an inhibitor called
IκB (inhibitor of kappa B). Following activation of the pathway (e.g. upon binding of
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various cytokines to their receptor), IκB is targeted for
proteasomal degradation by IκB kinase (IKK)-mediated
phosphorylation. Once IκB is degraded, the NF-κB dimer
is free to translocate to the nucleus where it can bind to
DNA and recruit effectors to act on gene expression. In
cancer cells, activation of the NF-κB can occur upon
mutations in the pathway genes or be mediated by cytokine
release from various cells of the TME, including immune
cells (Lakshmi Narendra et al. 2013).
Fig. 34: Canonical NF-κB pathway. The IKK complex
targets the inhibitor of NF-κB (IκB) for proteasomal
degradation by phosphorylation. IκB degradation allows
the translocation of the NF-κB dimer p50-p65 into the
nucleus where it can bind to DNA and act on gene
expression. (Adapted from Gerondakis et al. 2014)
The role of NF-κB in cancer is complex. Generally considered as a pro-
tumorigenic factor, it is involved in cell survival, invasion, angiogenesis, metastasis and
chemoresistance. These effects are widely associated with the pro-inflammatory role of
NF-κB. This notion is supported by the observation that patients with chronic
inflammation have higher risks to develop cancer, including BC (Bhatelia et al. 2014).
Furthermore, NF-κB induces the release of cytokines (e.g. TNF, IL1, IL6 and IL8) which
lead to the recruitment of leukocytes to the TME. The following immune response, which
includes events such as the release reactive oxygen species by neutrophils, might cause
DNA-damage and mutations as a side effects (Schumacker 2015). Additionally, NF-κB
signaling was shown to enhance epithelial to mesenchymal transition (EMT) as wells as
vascularization of tumors in BC (Shibata et al. 2002; Huber et al. 2004). In contrast, NF-
κB activation is part of the immune response targeting malignant cells, and, notably, full
activation of NF-κB is accompanied by a high cytotoxic activity of immune cells against
cancer cells (Hoesel & Schmid 2013). Also, several reports have indicated that NF-κB
could also oppose cancer development by promoting the survival of non-cancerous cells
(Zhang et al. 2005; Maeda et al. 2005; Shibata et al. 2010). Overall, the diverse effects of
the NF-κB pathway are determined both by the mechanisms sustaining tumor induction
and the type of immune response involved.
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2.4 Management and treatments
In this section, we will summarize the current strategies in terms of breast cancer
management, from diagnosis to treatment. We will mention the main therapeutic options
and explain how they relate to the biology of BC.
2.4.1 Breast cancer diagnosis
BC can be discovered in a patient through several circumstances: screening by
mammography, palpation of the breast, changes in the appearance of the breast, or
unexpected leaking from a single nipple. The formal diagnosis is usually based on three
procedures: (i) the clinical examination of the breast and the neighboring lymph nodes by
a doctor; (ii) the radiological examination, such as X-ray mammography, ultrasound
examination or magnetic resonance imaging (MRI) of the breast and neighboring lymph
nodes; and (iii) the histopathological examination following a biopsy of the tumor. The
latter, which is performed with a needle, provides the first clues concerning the
characteristics of the cancer and help guide the clinicians (ESMO 2016).
2.4.2 General therapeutic options
Deciding the therapeutic plan requires an inter-disciplinary team of medical
professionals. The final decision takes into account the size of the tumor, its location, the
lymph node status, the stage of the tumor and its molecular features.
The surgical removal of the tumor is chosen in most cases, for non-invasive as
well as invasive cancers. Depending on the progression of the disease, the surgery might
remain local (lumpectomy) or require the full removal of the breast (mastectomy). Of
note, breast-conserving surgery and breast-reconstruction are now a valid option for many
patients and should be discussed with their doctor (ESMO 2016).
Unfortunately, BC can lead to the development of metastases, even after the
removal of the tumor. At the point of diagnosis, micrometastatic sites may already exist,
and BC should in fact be viewed and treated as a systemic disease rather than a localized
pathology (Ignatiadis et al. 2008). Thus, clinicians will often advise the use of additional
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therapies to prevent the development of metastases (Fig. 35). Such treatments may occur
before the surgery (in which case they are referred to as “neo-adjuvant” or “pre-
operative”) and/or after the surgery (in which case they are called “adjuvant”). These
therapies, which have helped reduce the mortality of BC over the past decades, include
radiotherapy, chemotherapy, endocrine (or hormone) therapy and antibody-based
targeted therapies. Radiotherapy relies on the use of radiation in order to destroy rapidly-
proliferative cancer cells. Importantly, it is recommended as an adjuvant therapy for the
vast majority of invasive BC. The recommended radiation dose is 45-50 Grays, divided
in about 25 sessions in order to lower the damage to surrounding tissues and to better
control the tumor over a long period of time. Aside from the local treatments (i.e. surgery
and radiation), clinicians choose the most appropriate options in terms of systemic
treatment, based on the patient’s clinical features. These options are explained in the next
section.
Fig. 35: Standard treatments for BC. The most common combination for BC treatment
is the combination of surgery and radiation to remove and control locally the tumor.
Systemic treatments (hormone therapy, chemotherapy or targeted therapy) are often used
as additional (neo)adjuvant therapies to prevent the transition into a metastatic disease.
(Adapted from http://www.puhuahospital.com/treatments/cancer/breast)
2.4.3 Systemic therapies
Nowadays, chemotherapy generally consists in the combination of several anti-
cancer agents. Given the toxicity of the treatment, chemotherapy is given in 4-8 cycles,
with a resting period in between cycles. Several combinations of drugs can be used, but
generally at least one drug of the anthracycline family is advised (e.g. doxorubicin or
epirubicin). The anthracyclines prevent DNA replication and are among the most efficient
anti-cancer drugs available, however they can cause cardiotoxicity. Other drugs
commonly used for chemotherapy include cyclophosphamides (alkylant agents blocking
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DNA replication), taxanes (blockers of microtubule formation, which is essential for cell
division) and carboplatines (platinium-based blockers of DNA replication). Despite the
inevitable side effects, chemotherapy remains a relevant therapeutic option both as a
(neo)adjuvant therapy (when local surgery is performed) and in the case of advanced BC
(when the disease has already spread beyond the breast area).
Endocrine or hormone therapy is a first-line treatment for ER and/or PR-positive
tumors (den Hollander et al. 2013). A combination of several treatments can be used:
tamoxifen, aromatase inhibitors, gonadotropin-releasing hormone analogues, or the
removal of the ovaries (ovariectomy). The choice of treatment is notably based on the
menopausal status of the patient. Although endocrine therapy is often used in combination
with chemotherapy, patient at low risk of recurrence might benefit from endocrine therapy
alone, and thus avoid chemotherapy.
HER2-targeted therapy has become an efficient treatment for HER2-positive
tumors. The most common drug, trastuzumab (also called Herceptin), relies on an anti-
HER2 antibody to kill the tumor cells (Li & Li 2013). This adjuvant treatment is used in
combination with chemotherapy and has helped improve the survival of HER2-positive
patients, who previously suffered from poor survival. The effects of the treatment appear
to be due to the blockade of the HER2 signaling pathway, as well as an increase in HER2
antigen presentation, which promotes the recognition of tumor cells by cytotoxic T
lymphocytes.
Recently, we have witnessed the emergence of a major therapeutic innovation in
oncology: the immune therapies based on checkpoint blockade. Briefly, antibodies target
the immune checkpoints responsible for preventing the activation of the immune system,
including CTLA-4 or the PD-1/PD-L1 pathway (McArthur 2016). These strategies allow
a tumor-specific immune response to be elicited and, taking into account immune
memory, durable antitumor response and cure may be achieved. The checkpoint
inhibitors have shown exciting promising results in the context of metastatic melanoma
first, then other types of solid tumors (Yang et al. 2007; Hodi et al. 2010; Robert et al.
2011; Dany et al. 2016). Preliminary results from clinical trials indicated that some BC
might respond to immunotherapy, particularly triple negative and basal-like tumors which
are heavily infiltrated by immune cells (McArthur 2016). Several clinical trials are
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ongoing to evaluate how to best benefit from immune therapies, which have generated
tremendous hope in the field of oncology.
With many possible combinations, the choice of the best strategy often remains
difficult, even when taking into consideration all the clinical data available. Much
research on BC is currently focused on optimizing therapeutic strategies based on the
combination of various agents, the doses and/or the timing. And given the heterogeneity
of BC, the main issue in the management of the disease, beyond the development of new
drugs, might be identifying which patients will benefit the most from which combination
of treatments.
2.5 Breast cancer and epigenetics
As all cancers, BC notoriously involves numerous genetic alterations.
Interestingly, a link can be established with the intrinsic heterogeneity of the tumors: the
luminal A subtype is enriched in PIK3CA mutations (about 45% of cases) whereas the
basal-like subtype is highly associated with TP53 mutations (about 80% of cases)
(Koboldt et al. 2012). Nevertheless, cancer is not just a genetic disease, but also an
epigenetic disease. Many epigenetic machineries are altered during carcinogenesis and
these alterations actively contribute to the progression of the cancer. In this section, we
will review the main epigenetic alterations observed in BC, their implication in the
development of the disease and their clinical relevance.
2.5.1 Epigenetics alterations in breast cancers
The first part of this chapter describes the epigenetic alterations commonly
observed in BC, with a particular focus on DNA modifications.
2.5.1.1 Alterations of DNA modifications in BC
DNA methylation is a hallmark of all cancers, including BC. The cancer genome
displays global hypomethylation concomitantly to local hypermethylation (see also
section 1.3.1.1.4 Biological and pathological relevance of 5mC). The observed global
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hypomethylation is, at least in part, caused by a loss of methylation in pericentromeric
regions and repeated sequences. This leads to the activation of previously silenced
retrotransposon-like regions such as LINE-1 and the satellite DNA sequence SATR-1,
and consequently to genomic recombination and chromosomal instability (Costa et al.
2006; Van Hoesel et al. 2012). Loss of 5mC occurs mainly in gene-poor regions, and thus
only a limited number of genes have been reported as hypomethylated (Paredes et al.
2005; Pakneshan et al. 2005; Ito et al. 2008; Sharma et al. 2010). By contrast, many gene
promoters have been reported as hypermethylated in BC. These genes are classically
involved in the regulation of proliferation, apoptosis, DNA repair, metastasis or
angiogenesis, and their silencing through promoter methylation furthers tumorigenesis.
Examples include the cell-cycle regulator p16ink4A, the DNA repair genes MGMT and
MLH1, and the tumor suppressor genes (TSG) RAR-b, RASSF1A and PTEN (Murata et
al. 2005; Park et al. 2011; Fumagalli et al. 2012; Wang et al. 2012). Intra- and intergenic
regions have also been reported as aberrantly methylated in breast cancer, however, their
numbers are small because of the promoter-centric approach that was used for many
years, and they are expected to increase in future as genome-wide technology is ever more
utilized (Shann et al. 2008; Lu et al. 2012; Shetty et al. 2011). Intriguingly, these
alterations in 5mC occurs even without any changes of the DNMTs.
DNA methylation has been extensively studied on a genome-wide level in the
context of BC subtypes. Striking differences in 5mC profiles have been observed between
ER-positive and ER-negative tumors (Fig. 36) (Li et al. 2010). Beyond the ER status,
DNA methylation profiling seems to segregate the different molecular BC subtypes. In
particular, basal-like BC tend to display a differential methylation profile, in both
promoters and other genomic regions (Holm et al. 2010). These results suggest that
distinct subtypes of BC, as defined by gene expression, may display specific alterations
of the methylome. Nevertheless, the two levels of analysis do not overlap completely and
DNA methylation brings additional information to transcriptome data. For example,
genome-wide DNA methylation analyses of 802 breast tumors by the TCGA network
revealed five distinct subgroups, only two of which coincided with known expression
subtypes (luminal B and basal-like); the other three were unknown (Koboldt et al. 2012).
Importantly, the robustness of results obtained by various groups was recently evaluated
in a systematic review of 22 genome-wide methylation studies, and many genes and
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pathways were found to be commonly affected across all studies (Day & Bianco-Miotto
2013).
Fig. 36: DNA methylation changes are associated with ER status in BC. BC samples
were stratified through hierarchical cluster analysis. ER status is a main discriminator
of DNA methylation-based clusters in BC. (Adapted from Dedeurwaerder et al. 2011)
As previously mentioned (section 1.3.1.2.4 Biological and pathological relevance
of 5hmC), DNA hydroxymethylation is also widely affected in cancer. As most
malignancies, BC displays a global loss of 5hmC (Fig. 37) (Haffner et al. 2011). No
genome-wide 5hmC mapping has been reported in human biopsies so far, therefore the
local changes of 5hmC in BC are yet unknown. Given (i) the vast changes of 5mC in BC,
and (ii) the close link between 5mC and 5hmC, it is expected that many 5hmC alterations
could be found in BC as well. How those changes relate to anomalies in 5mC and gene
expression also remains to be explored. The question was partially answered in a study
in which 5hmC changes were mapped in the MMTV-PyMT transgenic mice (which
spontaneously develop breast tumors) with or without exogenous expression of sFlk1, a
protein that enhances tumor vessel pruning and hypoxia. The authors observed a global
loss of 5hmC in hypoxic tumors occurring mainly in gene-rich regions. Promoter
hypermethylation of a subset of tumor suppressor genes (TSG) was also found under
hypoxic conditions, suggesting a link between loss of 5hmC loss and gain of 5mC in this
model (Thienpont et al. 2016). Whether this relation stands true in human breast tissues
remains to be demonstrated.
Fig. 37: Loss of 5hmC
in BC, measured by
IHC. (Adapted from
Haffner et al. 2011)
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Unlike hematological malignancies, no recurrent mutation was observed for TET
genes in the breast. Nevertheless, all TETs display decreased expression, particularly
TET1. (L. Yang et al. 2015). Loss of TET1 has been linked to hypermethylation of its
promoter and has also been reported as a marker for metastasis in BC (Sang et al. 2015).
Worth mentioning, although TET enzymes are almost never found mutated in solid
cancers, including BC, 5hmC readers show higher mutational frequencies (Scourzic et al.
2015). Importantly, TET1 was mostly described as a tumor suppressor and its reduced
expression appears to promote migration, proliferation, tumor growth and metastasis.
Downstream targets of TET1 include tissue inhibitors of metalloproteinase (TIMP) and
HOX genes (Fig. 38) (Hsu et al. 2012; Sun et al. 2013).
Fig. 38: TET1 act as a TSG in
BC. Activation of Tet1 expression,
or its downstream target HOXA9,
by doxocycline (+DOX) in an
inducible breast cancer model
decreases tumor growth and
tumorigenesis. (Adapted from Sun
et al. 2013)
Worth mentioning, two studies have explored the effect of hypoxia, a condition
commonly associated with cancer, on TETs in the breast with similar results. In the first,
hypoxia was found to induce TET1 and TET3 upregulation, leading to activation of the
TNF-p38-MAPK signaling pathway and tumor promotion (Wu et al. 2015). In the second
study, a modest increase in TET expression was also observed under hypoxic conditions,
contrasting with a global loss of 5hmC in 2 out of 3 breast cell lines. The authors
demonstrated that decreased oxygen levels directly affected the oxidative activity of TET
enzymes and they suggested that increased TET expression might be an attempt of
compensation mechanism (Thienpont et al. 2016). Therefore, beyond TET expression,
loss of activity is another mechanism that can affect 5hmC levels in BC.
2.5.1.2 Alterations of histone modifications in BC
Aberrant histone modification patterns, as well as changes in their respective
machinery, have been linked to genomic instability and repression of tumor suppressor
genes in BC. For instance, H3K4 demethylase protein LSD1 is overexpressed in ER-
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negative tumors and is considered as a predictive marker for aggressive BC (Nagasawa
et al. 2015). Another example is the Polycomb2 complex member and H3K27
methyltransferase EZH2, which is overexpressed in HER2-like and basal-like tumors
(Holm et al. 2012). While the effects of aberrant histone modifications vary widely based
on the mark, it is now well-established that these changes contribute to BC development
and progression (Pitta & Constantinou 2015).
2.5.1.3 Alterations of chromatin remodeling in BC
As most cancer types, genes related to chromatin remodeling are found
dysregulated in BC. Loss-of-function mutations are associated with several members of
the SWI/SNF complexes, including PBRM1, ARID1A1, SMARCA2 and BRD7 (Helming
et al. 2014). In addition, post-translational modification of SWI/SNF subunits resulting
in mistargeting of the complex is another mechanism of chromatin remodeling
dysregulation in BC. In that regard it was demonstrated that BAF155 protein (encoded
by the SMARCC1 gene) can be aberrantly methylated by the arginine methyltransferases
CARM1. This event, which promotes the expression of genes involved in glycogen
metabolism, enhances breast tumor progression and metastasis (Stefansson & Esteller
2014).
2.5.1.4 Alterations of RNA modifications in BC
Research on RNA modifications is still in its early days, particularly in the context
of breast cancer. In 2016, a study reported that BC cells upregulate m6A demethylase
ALKBH5 in hypoxic conditions, subsequently leading to m6A-dependent regulation of
NANOG and an enrichment in cancer stem cells (Zhang et al. 2016). Of note, genetic
variants of FTO, another m6A demethylase, have been associated with increased risk of
BC in genome-wide association studies (GWAS) (Kaklamani et al. 2011). Given that
obesity is a known risk factor for BC and that FTO was originally identified for its role
in obesity, it was previously thought that the association between FTO variants and BC
was mostly due to obesity. However, the recent identification of FTO as an m6A
demethylase has challenged this notion (Jia et al. 2011). Importantly, no transcriptome-
wide study of any RNA modification in BC has yet been reported, but given the recent
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interest in this topic, it is expected that many groups will tackle this issue in the years to
come.
2.5.2 Clinical relevance of epigenetics
In this section, we will explore the relevance of epigenetics to clinical oncology
regarding BC, following two main topics: (i) the potential of epigenetic marks as new
diagnostic, prognostic and predictive biomarkers, and (ii) the development of drugs
targeting epigenetic modifications as new tools for cancer therapy.
2.5.2.1 Epigenetics modifications as biomarkers for cancer
As explained in previous chapters, epigenetic alterations are massively involved
in cancer development. Next to genetic mutations, which have long been used as
biomarkers in cancer, the so-called “epimutations” (i.e. alterations in genes involving
epigenetic mechanisms) are increasingly recognized as relevant markers as well. DNA
modifications, in particular, display several key features that make them useful as cancer
biomarkers, although other epigenetic modifications remain relevant. First, DNA,
whether unmodified or bearing epigenetic modifications, is a stable molecule, particularly
in comparison to RNA, allowing its detection in liquid biopsies (Warton et al. 2016).
Secondly, epigenetic alterations are common in cancer and are thought to occur early in
the disease (Dawson & Kouzarides 2012). Therefore, they could be used for the early
detection of malignancies. Finally, epigenetic biomarkers allow to detect, not only
changes in the cancer cells themselves, but also changes in the tissue composition
(Dedeurwaerder et al. 2011; Sehouli et al. 2011). This is of great interest, because cells
of the tumor microenvironment, such as immune cells, fibroblasts and adipocytes,
contribute to the cancer phenotype and bear a predictive and prognostic value (see further
below) (Oble et al. 2009; Dieci et al. 2015; Wolfson et al. 2015; Kuzet & Gaggioli 2016).
Detecting specific DNA methylation marks in liquid biopsies could in fact be
useful for cancer diagnostics, either for early detection or post-treatment monitoring. In
the context of BC, relevant target genes from blood biopsy include APC, GSTP1, RAR-β,
RASSF1A, SFN, P16, MLH1, HOXD13, PCDHGB7 and DAPK1 (Hoque et al. 2006;
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Ahmed et al. 2010; Shan et al. 2016). Overall, quantification of DNA methylation in
bodily fluids is a very attractive method of cancer diagnosis in clinics, given its
noninvasive nature.
Given the heterogeneity of BC, much attention has been directed to the
identification of prognostic markers, i.e. biomarkers that can categorize patients based on
their survival probability, in order to shepherd therapy strategy and reach the best clinical
risk-benefit ratio. Currently, risk evaluation is mainly based on immunohistological
features, mutation signatures and gene-expression analysis. Epigenetic biomarkers
represent an additional tool that can complement the classical methods used in routine.
DNA methylation signatures have been widely showed to refine our ability to predict
patient outcome, both in tumor and blood biopsy (Müller et al. 2003; Müller et al. 2004;
Dedeurwaerder et al. 2011; Severi et al. 2014). Overall, hypermethylation within
functional promoters was associated with an increased risk, whereas hypermethylation of
genomic regions outside promoters was associated with decreased risk (Severi et al.
2014). Methylated genes associated high-risk included APC and RASSF1A (Fig. 39)
(Müller et al. 2004). Likewise, changes in histone modifications could also serve as
prognostic biomarker. For instance, high global levels of histone acetylation and
methylation (H3K4me2 and H4K20me3) were associated with good outcome and were
found almost exclusively in luminal-like breast tumors.
Fig. 39: Example of epigenetic prognostic
marker. Methylation of the APC gene is
associated with poor survival in BC patients
(Adapted from Müller et al. 2003)
A major challenge of modern oncology lies in the selection of the optimal
treatment, as the effectiveness of a given therapy can vary greatly from one patient to
another. Hence, clinicians have started to rely on the use of predictive markers, i.e.
biomarkers that can predict a patient’s response to treatment. Once more, epigenetic
markers are of interest in this context. For instance, hypermethylation of the DNA-repair
genes BRCA1 and BRCA2 predicts sensitivity to adjuvant chemotherapy and/or
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poly(ADP-ribose) polymerase (PARP) inhibitors (Veeck et al. 2010; Xu et al. 2013).
Other hypermethylated genes identified as predictive markers for adjuvant therapies
include TP53, PITX2 an CDO1 (Harbeck et al. 2008; Dietrich et al. 2010; Foedermayr et
al. 2014).
Furthermore, DNA methylation is increasingly recognized as a tool to investigate
the tissue composition of a tumor. Above all, tumor-infiltrating lymphocytes (TILs) are
associated with better survival and better response to treatment in breast and other cancers
(Oble et al. 2009; Melichar et al. 2014; Dieci et al. 2015). Interestingly, several studies
have highlighted the ability of DNA methylation to discriminate and accurately quantify
the various immune populations (Fig. 40) (Sehouli et al. 2011; Accomando et al. 2014;
Dedeurwaerder & Fuks 2012). This is notably due to the more linear relation between
DNA molecules and the numbers of cells (i.e. 2 copies of DNA per cell in diploid
organisms) as compared to RNA, as well as the high tissue-specificity of 5mC profiles
(Hackl et al. 2016). Recent work from our host laboratory has demonstrated the
prognostic value of a DNA methylation-based signature (called “MeTIL”) by measuring
TIL infiltration in breast and other cancers. Moreover, the MeTIL signature also predicted
response to chemotherapy independently of other clinical and pathological variables.
Fig. 40: DNA methylation as a tool to quantify immune infiltration. The
immunophenotype, in particular the composition of TILs in the tumor tissue, can be
estimated based on DNA methylation profiles using computational tools, such as
establishing signature scores and/or deconvolution of signal. (Adapted from Hackl et al.
2016)
In conclusion, DNA methylation is by far the epigenetic modification that is the
closest to a “bench to bedside” transition, while histone modifications have also shown
some potential in terms of biomarkers. Recent studies have suggested that other
epigenetic modifications (e.g. DNA hydroxymethylation and RNA methylation) could
87
also be detected in liquid biopsies and have potential as new biomarkers (Godderis et al.
2015; Huang et al. 2016; Gilat et al. 2017). Unlike DNA methylation, the use of such
epigenetic modifications in routine oncology practice is still limited by the greater amount
of material required for their analysis. However, it is expected that technologies will
improve in the future and make them accessible to biomarker investigation.
2.5.2.2 Epigenetic therapy of cancer
Beyond their role as biomarkers, epigenetic modifications also represent an
appealing target for cancer therapy because their alterations are involved in the
progression of the disease and because they are reversible. Thus, in the past two decades,
much effort has been aimed at the development of drugs targeting epigenetic enzymes.
These drugs can either inhibit broadly the establishment of an epigenetic mark and lead
to a global loss of the modification, or they can target a specific enzyme or isoform for
more selective effects.
Among epigenetic drugs, DNA methylation inhibitors were the first to be used in
oncology (Fig. 41). These inhibitors, e.g. 5-Azacytidine (5-Aza-CR; azacytidine) and 5-
Aza-2’-deoxycytidine (5-Aza-CdR; decitabine), consist in nucleoside analogues that are
incorporated in the DNA but cannot be methylated, thus leading to 5mC depletion during
DNA replication. At low dose, DNA methylation inhibitors block proliferation in tumor
cells by reactivating previously hypermethylated tumor suppressor genes (TSG) (Phan et
al. 2016). At higher doses, they also induce cytotoxicity, due to the covalent binding of
DNMTs to DNA (Oka et al. 2005). Although these drugs do not specifically target tumor
cells, they appear to affect more efficiently rapidly-growing cells, which might explain
their relatively low side effects to healthy tissues. Following promising results in the
treatment of leukemia (Silverman et al. 2002), 5-Aza-CR and 5-Aza-CdR were approved
by the U.S. Food and Drug Administration (FDA) in 2004 and 2006, respectively, for
treatment of myelodysplastic syndromes.
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Fig. 41: Activation of TSG by
epigenetic drugs. In cancer
cells, DNA hypermethylation
and histone hypoacetylation act
to promote silencing of TSGs.
Treatment with DNMT and
HDAC inhibitors (DNMTi and
HDACi, respectively) reverse
these epigenetic alterations,
open chromatin structure and
increase TSG gene expression.
Restoration of normal TSG
functions promotes anti-cancer
properties of epigenetic drugs.
(Adapted from Seidel et al.
2012)
The second main category of epigenetic drugs in oncology consists of HDAC
inhibitors (Fig. 41). Increasing histone acetylation with HDAC inhibitors has indeed
shown promising results by blocking cell growth and inducing apoptosis. As for DNA
methylation inhibitors, their action was shown to be linked to the reactivation of TSG.
Many HDAC inhibitors have been developed over the years, with the first-generation
inhibitors showing a broad specificity for HDACs and the second-generation inhibitors a
greater intrinsic selectivity for their molecular targets (Valdespino & Valdespino 2015).
Currently, four drugs have been approved by the FDA for cancer therapy: Vorinostat
(SAHA), Romidepsin (FK-228), Belinostat (PXD-101) and Panobinostat (LBH-589).
Aside from directly targeting enzymes, epigenetic drugs can also target readers of
specific modifications. For instance, the “BET inhibitors” (iBETs) bind the
bromodomains of BET proteins BRD2, BRD3, BRD4, and BRDT. In doing so they block
the interaction between BET proteins and acetylated histones, and thus affect the
recruitment transcription factors and other chromatin modulators (Shi & Vakoc 2014).
These drugs have shown promising results as anti-cancer therapies in preclinical models.
For instance, BRD4 was implicated in the development of epithelial cancers, which might
explain the therapeutic effects of iBETs observed in xenograft models of breast cancer
(Shi et al. 2014). Studies are currently being conducted in order to evaluate the clinical
benefit associated with such therapies. Worth mentioning, iBETs are also known as potent
anti-inflammatory agents, which has been linked to the critical role of Brd2, Brd3, and
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Brd4 in the induction of inflammatory gene transcription, notably in macrophages and T
lymphocytes (Biswas & Mantovani 2010; Bandukwala et al. 2012).
Until recently, SWI/SNF complexes were thought to be inadequate targets for
inhibition in cancer therapy, because they mostly display tumor suppressive functions.
However, new potential therapeutic strategies have been developed on the basis of
synthetic lethality (Schiaffino-Ortega et al. 2014). Briefly, this concept relies on the
observation that mutations in SWI/SNF subunits, while potentially promoting cancer,
tend to also render the cancer cells more sensitive to repression of additional SWI/SNF
subunits. For example, studies suggested that the inhibition of SMARCA2 might have a
therapeutic benefit in SMARCA4 mutant tumors (Oike et al. 2013; Hoffman et al. 2014).
Therefore, targeting SWI/SNF proteins could be a new field for anti-cancer drug
discovery. Worth mentioning, the SWI/SNF complexes include several proteins with
bromodomains, such as SMARCA4, SMARCA2, BRD9, and PBRM1, that are targetable
with specific iBETs (Schiaffino-Ortega et al. 2014).
In the context of BC, epigenetic drugs, especially demethylating agents and
HDAC inhibitors, are frequently used in combination with other drugs because they can
enhance the susceptibility to other anti-cancer agents such as chemotherapeutic drugs (S.
Y. Li et al. 2015; Phan et al. 2016; Manal et al. 2016). Synergistic interaction can be of
particular interest in cancer therapy, as it allows lowering of doses and thus reduction of
side effects. In BC cells, epigenetic drugs have been shown to increase the anti-tumor
effects of several chemotherapeutic agents (including paclitaxel, doxorubicin, and 5-
fluorouracil), as well as tamoxifen and trastuzumab (i.e. the most common hormone and
HER2-targeting agents, respectively) (Sharma et al. 2006; Mirza et al. 2010; Huang et al.
2011). Phase I-III trials are ongoing to evaluate the clinical benefit of epigenetic drugs
used in combination with conventional treatments (C. et al. 2015; Jones et al. 2016;
Connolly et al. 2017).
Another potential benefit of epigenetic drugs is their ability to reverse cancer
immune evasion (Fig. 42) through increased expression of tumor surface antigens and
major histocompatibility complex (MHC) molecules (Roulois et al. 2015; Chiappinelli et
al. 2015). In particular, 5-Azacytidine has been identified as an immunomodulator in
cancer. Although clinical benefit has yet to be proven, this effect could potentially be
exploited as strategy to enhance the efficiency of immune therapies in breast and other
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cancers. In addition, iBETs appear to reduce the inflammatory pro-tumor effects of the
TME by suppressing the production of nitric oxide and a variety of inflammatory
cytokines (Leal et al. 2017). It is likely that the immunomodulatory effects of epigenetic
drugs will progressively be integrated in cancer management, in combination of either
conventional or immune-targeted therapies.
Fig. 42: Epigenetic drugs in cancer therapy. Normal cells can acquire both genetic and
epigenetic alterations during the tumorigenesis development. The interplay between these
two processes gives rise to alterations in the chromatin. Further selection can take place
following host immune surveillance or chemotherapy and, once again, the availability of
both genetic and epigenetic pathways can rapidly speed up the emergence of resistance.
Epigenetic therapy has the potential to reverse epigenetic abnormalities, thus restoring
sensitivity to treatment (Adapted from Jones et al. 2016).
The extension of the epigenetic repertoire in recent years has also broadened the
possibilities in terms of epigenetic drugs. In particular, given that TET enzymes have been
described as TSG in many cancers, including BC, it has been suggested that reactivation
of TETs in cancer might also be beneficial in cancer therapy. As of today, there is no
specific activator of TETs/5hmC available, however vitamin C has been identified as a
positive cofactor of TET enzymes that is involved in DNA demethylation (Blaschke et
al. 2013; Minor et al. 2013; Sasidharan Nair et al. 2016). This is of particular interest,
because meta-analysis suggests that vitamin C supplementation and/or dietary intake
could be associated with better survival in BC (Harris et al. 2014). Vitamin C is a broad
factor that act on many enzymes besides TETs and has far-reaching effects. Nevertheless,
it seems likely that some of the beneficial effects could be mediated by increased 5hmC
levels in cancer cells.
In regard to RNA modifications, regulators of related enzymes are just starting to
appear on the market (Y. Huang et al. 2015; T. Wang et al. 2015). A recent study reported
that inhibition of the m6A demethylase FTO with meclofenamic acid suppressed glioma
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progression and prolonged lifespan of xenografted mice, opening new applications for
RNA epigenetics (Cui et al. 2017). Furthermore, RNA-based therapeutics have recently
been gaining attention, as utilizing siRNA and mRNA in a therapeutic context is now
possible. In that regard, understanding how chemical modifications of RNA can either
improve or hinder the potency of RNAs is essential (Kaczmarek et al. 2017). However,
the field of epitranscriptomics is still in its early days, and much further research will be
required to determinate the potential of RNA modification in terms of clinical oncology.
In conclusion, epigenetic drugs have shown promising results as anti-cancer
agents. In particular, DNMT and HDAC inhibitors have already displayed benefits for
cancer patients, notably when acting in synergy with other agents. This notion is
supported by ongoing clinical trials, and seems, at least in part, associated with their
immunomodulator effects. Likewise, iBETs have recently shown promising results in
cancer therapy, with potent anti-inflammatory properties. And given the identification of
so-called “new” epigenetic modifications, the field of cancer epigenetics will probably
increase further in the next few years.
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3. Aims of the project
As described in the introduction, the improvement in sequencing technologies and
the characterization of new enzymes has deeply changed our view of epigenetics by
unraveling the importance of the growing epigenetic repertoire in many biological
processes, as well as their implication in diseases such as cancer. This scientific revolution
allows a better understanding of the molecular mechanisms regulating the cells of an
organism. In the long run, exploring the “new” epigenetic modifications should improve
cancer management by providing potential novel biomarkers and therapeutic targets. Yet,
the study of these modifications is still in its early days and has not yet reached its full
potential. Given the high incidence of breast cancer, we have decided to mainly focus on
this disease as a model to explore the role of such epigenetic modifications in human
pathologies.
More specifically, our project was divided in three parts, each centered around a
different epigenetic modification. First, we sought to explore the regulation of TET1, an
enzyme responsible for DNA hydroxymethylation (5hmC) and already known to be
downregulated in many cancers, including breast. Secondly, we wanted to provide the
first mapping of RNA hydroxymethylation (5hmrC), a modification just recently
identified. We aimed to conduct a first fundamental study in Drosophila, then to extend
our data by exploring potential 5hmrC dysregulations in breast cancer. Finally, we wanted
to investigate the potential implication of RNA methylation (m6A), the most frequent
modification of mRNAs, in breast cancer.
The general goal of this project was to improve our understanding of how
recently-discovered epigenetic modifications are altered in cancer and provide evidence
that they constitute an important and novel level of dysregulation implicated in
carcinogenesis. Overall, we anticipated that our results could contribute to a better
knowledge of the role of epigenetics in health and disease.
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95
Results
Results
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1. Immune activation of NF-κB drives TET1
dysregulation in cancer
1.1. Introduction
In the past decades, changes in the epigenome have been increasingly recognized
as intrinsically linked with tumorigenesis and cancer progression (Dawson & Kouzarides
2012). It is now well-accepted that both DNA methylation and histone modifications, the
two major epigenetic modifications, are vastly affected in cancer and involved in the
disease. Yet, the recent expansion of the epigenetic repertoire has brought new questions
and challenges to the field of cancer epigenetics. Alterations of TET enzymes and DNA
hydroxymethylation (5hmC) have been identified as a hallmark of cancers and have
gained much attention in recent years. Loss of TETs and 5hmC have been associated with
cancer progression and metastases in many cancers, including breast cancer (Haffner et
al. 2011; Hsu et al. 2012). Hence, understanding TET dysregulation represents a key
challenge for the future in cancer epigenetics.
In our quest for mechanisms of TET dysregulation in breast cancer, we came upon
an unprecedented link with the immune system and, more specifically, the NF-κB
pathway. This was of tremendous interest to us, because the immune system has emerged
as a major feature of cancer in recent years with a dual effect: on one hand, secretion of
pro-inflammatory factors by immune cells are involved in tumor progression and
resistance to treatment; on the other hand, the immune system is heavily involved in the
anti-tumor response. The latter notion is supported by the fact that tumor immune
response, and in particular tumor-infiltrating lymphocytes (TILs), are increasingly
recognized to be associated with better clinical outcome. In addition, the recent
emergence of immune checkpoint inhibitors as a promising method to treat cancer has
brought a new hope in terms of cancer treatment, as demonstrated by ongoing clinical
trials. In regard to that, our findings are of great interest because epigenetic drugs have
been shown to modulate the antitumor immune response and dissecting the epigenetic
mechanisms underlying the cross-talk between the immune system and cancer could help
optimize therapeutic strategies.
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In this study, we started by exploring epigenetic dysregulations in breast cancer
and we demonstrated for the first time that TET1 repression, and its related alterations of
5hmC, are associated with activation of immune pathways and immune infiltration of the
tumor. We show that TET1 is repressed in breast cancer, both in vitro and in mice,
following activation of the major immune regulator NF-κB, through binding to the TET1
promoter. Finally, we extend our findings to other cancer types, including melanoma,
lung and thyroid cancer, suggesting that immunity-driven repression of TET1 could be a
characteristic shared by many cancer types.
Personal contribution to this study includes:
- The general coordination of this project through interactions with the other
contributors;
- The design of experiments and interpretation of the data (along with Annalisa
Canale from the University of Liège);
- RNA-seq analyses, gene ontology analyses, cell culture and cell treatments,
RT-qPCR, Western blot, luciferase assays, agarose-streptavidin binding
assays, ChIP-qPCR, statistical analyses;
- Preparation of the figures;
- Preparation of the manuscript (along with Annalisa Canale).
This chapter summarizes the major points of our study. For further details, please see the
related manuscript provided in Appendix (Collignon et al., “Immune activation of NF-κB
drives TET1 dysregulation in cancer”, currently under submission).
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1.2. Results
1.2.1. TET1 expression is associated with 5hmC dysregulation in BLBC
TET1 has been described as a tumor suppressor in breast cancer (BC). Studies
have indicated that the gene was downregulated, which was associated with increased
tumor growth in mice, as well as poor survival in patients (Hsu et al. 2012; Sun et al.
2013; Yang et al. 2015). However, none of these studies took into account the
heterogeneity of BC. Hence, we decided to look more in depth at the regulation of TET1,
particularly in regard to the various BC subtypes.
To assess TET1 expression in breast tumors, we used publicly available RNA-seq
data from The Cancer Genome Atlas (TCGA) consortium (Koboldt et al. 2012). We
subdivided the samples into the four main BC subtypes and compared TET1 expression
to normal breast (see Fig. 43). As reported in publications for BC, TET1 expression was
found decreased in three of the molecular subtypes – luminal A (n=213), luminal B
(n=116) and HER2-like (n=54) tumors – compared to normal tissues (n=101). In sharp
contrast, BLBC (n=90), which composed the fourth subtype, displayed a different pattern
of expression. The range of expression was much wider – approximately 4-fold wider
than other BC subtypes – with some tumors displaying low expression and other high
expression of the TET1 gene.
Fig. 43: TET1 expression in BC subtypes. Expression was assessed in breast tissue by
meta-analysis of public RNA-Seq data from the TCGA cohort. Normal: normal breast
(n=101); BLBC: basal-like breast cancer (n=90); HER2: HER2-like breast cancers
(n=54); LumA: luminal A breast cancers (n=213); LumB: luminal B (n=116).
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Given the wide range of TET1 expression in BLBC tumors, we took advantage of
this subtype to investigate whether 5hmC dysregulations were associated with TET1
levels. We obtained DNA from 4 pairs of matched breast tissues (i.e. tumor and normal
samples coming from the same patients). We used the previously described hMe-seal
method to specifically select hydroxymethylated fragments, followed by deep sequencing
(referred here as “5hmC-seq”) (Delatte et al. 2015).
We first clustered the breast sample pairs based on TET1 expression in the
respective tumor (2 pairs with high expression and 2 pairs with low expression). In the
group characterized by low tumor TET1 expression, we identified 256 differentially
hydroxymethylated regions (dhmRs) in tumors compared to their matched normal tissues.
Most dhmRs were hypohydroxymethylated (58%). In contrast, in the group characterized
by high TET1 expression, we identified 160 dhmRs that were almost exclusively
hyperhydroxymethylated (98%). All dhmRs are displayed in a heat map in Fig. 44. The
overlap between the dhmRs of the 2 groups was extremely low with only 2 gene bodies
and 1 intergenic region in common. These results indicate that BLBC tumors with distinct
TET1 expression levels display different patterns of 5hmC alterations, with high TET1
expression being associated with 5hmC gain and vice versa.
Fig. 44: TET1 regulation is associated with distinct 5hmC changes in BLBC.
Sequencing of 5hmC was performed in 4 pairs of BLBC and matched normal breast.
Paired samples were clustered based on TET1 expression in the respective tumor: 2 pairs
with low TET1 expression (left), 2 pairs with high TET1 expression (right). Based on
selection criteria (log FC>3 and FDR<0.05), 256 and 160 differentially
hydroxymethylated regions (dhmRs) were identified in BLBC of each group, respectively.
The heat maps illustrate 5hmC levels (in CPM, counts per million) of the dhmRs identified
in BLBC with low TET1 expression (left) and high TET1 expression (right), compared to
matched normal breast.
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Next, we investigated the potential link between 5hmC, 5mC and gene expression
in each BLBC groups. Specifically, we looked at the differential regulation of all coding
gene-associated dhmRs between cancers and normal breast (Fig. 45). In the TET1-low
group, loss of 5hmC was mostly associated with a gain of 5mC. Consistently, in the TET1-
high group, gain of 5hmC was mostly associated with loss of 5mC. Hence, there was a
negative relationship between 5hmC and 5mC changes in BLBC. Furthermore, in both
groups, genes displaying 5hmC changes were also found deregulated at the expression
levels, albeit with no specific direction (either up- or downregulated, regardless of the
hyper- or hypohydroxymethylated status). These results suggest that there is a link
between DNA hydroxymethylation, DNA methylation, and gene expression in BLBC
tumors.
Fig. 45: Link between 5hmC, 5mC and gene expression in BLBC. Heat maps
illustrating 5hmC, 5mC and expression changes in BLBC with low TET1 expression (left)
and high TET1 expression (right), compared to normal breast. Only coding genes
associated with dhmRs are represented for each tumor group. 5mC changes were
measured using Illumina 450K Infinium in the same matched samples. The most variant
probe of the corresponding region (promoter or gene body) was represented. Expression
(mRNA) changes were obtained from TCGA by comparing RPKM values of the 25 BLBC
tumors with the lowest and highest TET1 expression, respectively, and normal breast.
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1.2.2. Link between TET1 expression and immunity in BLBC
In order to unravel the mechanisms responsible for TET1 dysregulation in breast
cancer, we next investigated the relationship between TET1 expression and signaling
pathways. Given that BLBC tumors display both high and low expression of TET1, we
focused on that subtype in particular.
From the TCGA RNA-Seq data, we selected all genes presenting a Pearson
correlation coefficient r superior to 0.25 (either positive or negative) with TET1
expression and we performed a gene ontology analysis with DAVID. Strikingly, the top
pathways enriched in the genes negatively correlated with TET1 were all related to
immunity and defense (Fig. 46A). To illustrate this result, we computed a heat map of the
top 20 genes from the “immune response” category. The score of this “immune response”
signature presented an r correlation coefficient of -0.49 with TET1 expression
(p<0.00001) (Fig. 46B). In contrast, the same signature of genes displayed a much weaker
correlation with TET1 in other BC subtypes, with r coefficients for the signature score of
-0.18 (p=0.009) for luminal A tumors, -0.13 (p=0.16) for luminal B tumors, and -0.15
(p=0.29) for HER2-like tumors. Hence, TET1 expression displayed a negative correlation
with expression of many immune markers, specifically in BLBC.
Fig. 46: TET1 is anticorrelated with genes linked to immune pathways. (A)
Functional enrichment analysis was performed with DAVID on all genes presenting a
correlation coefficient r > 0.25 or r < -0.25 with TET1, based on gene expression (RPKM)
of TCGA BLBC samples (n=90). The top 5 of immune and defense categories are
represented. (B) Heat map illustrates gene expression (RSEM z score) of the top 20 genes
from the “immune response” category from panel A, based on correlation coefficient r.
TCGA BLBC samples were ordered by TET1 expression.
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The immune and defense pathways identified included genes related to the
myeloid/macrophage compartment, such as TYROBP and CD14, as well as genes related
to the lymphoid compartment, such as CD3D, CD4, CD8A, and LST1. Importantly, genes
of key regulatory factors involved in defense pathways, such as the NF-κB member
RELA, the major histocompatibility complex (MHC) class I partner B2M and the
chemokine CCL2, were also found inversely correlated with TET1 expression. Examples
are provided in Fig. 2 and supplementary Fig. S1 of the manuscript presented in appendix
I.
Next, we further characterized the immune status of BLBC tumors by performing
immunohistochemistry (IHC) of classical immune markers to quantify tumor infiltration
by immune cells. This was performed in collaboration with the team of K. Willard-Gallo
(Bordet Institute). As shown in Fig. 47A, the CD45 antigen, commonly used to score
global leukocyte infiltration, was negatively correlated with TET1 expression. BLBC
tumors with high TET1 expression displayed significantly lower infiltration by leukocytes
by IHC than BLBC tumors with low TET1 expression (p=0.04). To further explore the
infiltration in respect to the various immune populations, we scored the infiltration of T
and B lymphocytes by staining CD3 and CD20 antigens, respectively. Consistent with
our findings that TET1 is negatively associated with global leukocyte infiltration, we
observed a negative relation between TET1 expression and the infiltration of the tumors
by T and B lymphocytes (p=0.029 and p=0.005, respectively).
In order to confirm and extend our IHC results, we used CIBERSORT, a method
for characterizing cell composition of complex tissues from their gene expression profiles
(Fig. 47B). Consistently with IHC results, tumors with high TET1 expression displayed
lower infiltration of several immune populations, including CD4+ and CD8+ T
lymphocytes (with p-values of 0.008 and 1.10-5, respectively) and M1 macrophages (with
p=5.10-5).
Taken together, our results indicated that the global immune state of tumors w
negatively correlated with TET1 expression. BLBC with a low expression of many
immunity-related genes, as well as a low infiltration by the major types of leukocytes,
were characterized by a higher expression of TET1 overall, and vice versa.
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Fig. 47: High TET1 expression discriminates BLBC tumors with low immune
infiltration. (A) High expression of TET1 was associated with low leukocyte infiltration
in BLBC tumors. Tumor infiltration was measured by IHC. Staining of CD45, CD3, and
CD20 was performed to quantify leukocytes, T lymphocytes, and B lymphocytes,
respectively (n=18). (B) Infiltration by major immune sub-populations was further
analyzed by using CIBERSORT, a method for characterizing cell composition of complex
tissues from their gene expression profiles in BLBC (n=90). Gene expression data
(RPKM) were obtained from TCGA.
1.2.3. Activation of NF-κB drives TET1 repression
Given the strong link between immune markers and TET1 repression in BLBC,
we wondered whether activation of immune pathways could be involved in the
dysregulation of TET1. Because both immune cells and immune markers were found
negatively correlated with TET1 in BLBC tissues, we hypothesized that cytokine release
from immune cells of the tumor microenvironment could lead to TET1 repression in
breast cancer cells.
In order to test this hypothesis, we first treated triple negative breast cancer MDA-
MB-231 cells with media conditioned by myeloid U937 cells (Fig. 48). This method,
previously established by Mohamed (2012), allowed us to test the effects of U937-derived
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secreted cytokines, chemokines and growth factors on immune cells. In these
experimental conditions, a significant decrease of TET1 expression (2.6-fold, p=0.02),
but not TET2 or TET3, was detected by RT-qPCR. Furthermore, the decrease of TET1
was confirmed at the protein level by Western blot. This result suggested that mediators
secreted by leukocytes could indeed lead to TET1 repression in breast cancer.
Fig. 48: Leukocyte-conditioned media
represses TET1 expression. MDA-MB-231
cells were treated with media previously
conditioned by myeloid U937 cells. TET
expression was measured by RT-qPCR (left
panel, n=3, data expressed as mean ± SD,
relative to control) and nuclear TET1
protein and p65 levels were assessed in
control (CTL) and conditioned media (CM)
conditions by Western blot (right panel,
representative of three independent
experiments).
Next, we aimed to unravel the specific mechanisms related to immune pathways
that could drive TET1 regulation. Because the genes previously identified as anti-
correlated with TET1 covered a panel of miscellaneous immune genes, encompassing
innate and adaptive immunity, as well as inflammatory markers, it seemed likely that a
central immune regulator might affect TET1 expression, rather than a highly specific
factor. In regard to that, several clues pointed towards a potential involvement of the NF-
κB family, which affects many immune and inflammatory functions, as hereafter
explained.
First, the NF-κB member RELA, which codes for the protein p65, was among the
genes identified as negatively correlated with TET1 in BLBC tumors of the TCGA cohort
(see Fig. 2 of the manuscript presented in appendix I). Secondly, when MDA-MB-231
cells were treated with U937-conditioned media, there was an increase in nuclear p65, as
observed by Western blot, which revealed the activation of canonical NF-κB pathway in
these experimental conditions (Fig. 48). Thirdly, based on RNA-seq data of the TCGA
cohort, we scored a NF-κB signature, and we observed that high TET1 expression was
associated with low signature score in BLBC tumors (Fig 49A) (p=2.10-5). Finally, in a
publicly available dataset (GSE52707), TET1 was reduced when NF-κB member p65 was
overexpressed in breast cancer cells (Fig. 49B). Taken together, these data suggested that
NF-κB activation could contribute to TET1 repression.
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Fig. 49: TET1 expression and NF-κB in
breast cancer. (A) High expression of
TET1 was associated with a low NF-κB
signature in BLBC tumors (n=90). Gene
expression data (RSEM z scores) were
obtained from TCGA. (B) Breast cancer
cells stably expressing an active form of
NF-κB p65 (RELA) have a lower expression
of TET1. Data were obtained from
GSE52707 on NCBI GEO database.
Given the association between TET1 expression and NF-κB activation, we next
aimed to test the causality of the relationship by activating NF-κB in vitro through
different approaches. For each condition, we verified that NF-κB was properly activated
by assessing nuclear translocation of p65 (Fig. 50) by Western blot, and the induction of
target genes IL6 and IL8 by RT-qPCR (see supplementary figure S4 of the manuscript
presented in appendix I).
First, p65 was overexpressed in MDA-MB-231 cells, and we observed a decrease
in TET1 expression (1.7-fold; p=0.04) (Fig. 50A). Of note, this result was consistent with
public data on p65 overexpression in BC cells (Fig. 49B). However, in cancer tissues,
elevated NF-κB activity is often achieved through cytokine release from cells of the tumor
microenvironment (Ben-Neriah & Karin 2011). Hence, in our next experiment, we treated
MDA-MB-231 cells with TNF or LPS, two soluble factors that are well-known to induce
the activation of the NF-κB pathway through their signaling cascade (Hellweg et al.
2006). This led to a 1.8-fold (p=0.01) and 2.6-fold (p=0.001) decrease of TET1
expression, respectively (Fig. 50B and 50C). Taken together, our results from these three
modes of NF-κB activation (p65 overexpression, LPS, TNF) all suggested that NF-κB
could specifically downregulate TET1, with no effect on TET2 expression and no effect
or an increase of TET3 expression. The effect of TNF on TET1 expression was also
confirmed in two other triple negative breast cell lines, i.e. Hs 578T and BT549 (see
supplementary figure S5 of the manuscript presented in appendix I). However, a careful
interpretation of the data was required because the cell signaling effects of cytokines, such
as TNF, are broad and not restricted to NF-κB activation. Therefore, in our next
experiment MDA-MB-231 cells were pretreated with MG-132, a known blocker of NF-
κB activation before TNF treatment. In these conditions, repression of TET1 was
compromised (1.2-fold reduction, p=0.15) (Fig. 50D), which suggested that TNF-
mediated repression of TET1 was actually due to NF-κB activation.
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Fig. 50: TET1 expression is repressed by NF-κB activation in vitro. (A) NF-κB member
p65 was overexpressed in MDA-MB-231 cells and TET expression was measured by RT-
qPCR (left panel, n=3, data expressed as mean ± SD, relative to control). Nuclear p65
levels were assessed by Western blot (right panel, representative of three independent
experiments). (B,C) NF-κB was activated by LPS and TNF treatment in MDA-MB-231
cells for 4h and TET expression was measured by RT-qPCR (left panel, n=3, data
expressed as mean ± SD relative to control). Nuclear p65 levels were assessed by Western
blot (right panel, representative of three independent experiments). (D) TNF-dependent
activation of NF-κB was blocked by pre-treatment of MDA-MB-231 with MG-132 and
TET expression was measured by RT-qPCR (left panel, n=3, data expressed as mean ±
SD relative to control). Nuclear p65 levels were assessed by Western blot (right panel,
representative of three independent experiments).
Finally, in order to test whether NF-κB-dependent dysregulation of TET1 was
conserved in vivo, we took advantage of a transgenic mice system called IKMV. In this
model, aberrant NF-κB activation leads to a pre-cancerous state in mammary epithelium
(Barham et al. 2015). The description of the transgenic model can be found in the study
by Barham et al. and in the Fig. 4 of the manuscript presented in appendix I. Briefly,
mammary glands were collected after 3 days with or without activation of NF-κB, and a
significant reduction of Tet1 expression was detected in those pre-cancerous epithelia,
both by RT-qPCR (4.5-fold; p=0.001) and Western blot (Fig. 51).
This last result further confirmed previous findings: in vitro and in vivo data both
reinforce the concept that NF-κB activation negatively regulates TET1 expression in BC.
Taken together with previous observations on immune markers and immune infiltration
in breast tissues, our data support a model in which cytokine release by cells of the micro-
environment and subsequent action of NF-κB mediate TET1 dysregulation in BLBC.
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Fig. 51: TET1 expression is repressed by NF-κB activation in vivo. Effect of NF-κB
activation was assessed in vivo in the breast using IKMV transgenic mouse model,
previously described (Barham et al., 2015). Tet expression was measured by RT-qPCR
(left panel, n=3, data expressed as mean ± SD relative to control) and Tet1 protein level
was assessed by Western blot (right panel, n=2 for controls and n=3 for IKMV samples).
1.2.4. TET1 is repressed through binding of NF-κB to its promoter
Next, we investigated the molecular mechanisms involved in TET1 regulation.
Given that NF-κB can bind DNA and act as a transcription factor, we started by searching
for the known consensus sequence of NF-κB binding sites in the vicinity of the TET1
promoter with in silico analyses. We used three different prediction algorithms (JASPAR,
AliBaba and TFBIND), and we identified two putative p65 binding sites, thereafter
named sites A and B. Both sites were close to the transcription start site (TSS) of the gene
(Fig. 52).
Fig. 52: Schematic view of TET1 gene promoter.
Two NF-κB binding sites, named “A” and “B” were
identified based on 3 prediction algorithms
(JASPAR, AliBaba and TFBIND). Binding site
locations are indicated in base pair (bp), relative to
TET1 transcription start site (TSS).
The presence of consensus p65 binding sequences raised the possibility that NF-
κB might bind to TET1 promoter in order to regulate its expression, therefore we next
sought to test this hypothesis. First, we investigated whether NF-κB-mediated regulation
of TET1 was linked to its promoter region. This was performed with luciferase reporter
assay. Briefly, MDA-MB-231 cells were transfected with a plasmid coding for the
luciferase enzyme under the control of the TET1 promoter and luciferase activity was
measured. Upon NF-κB activation (attained by overexpressing p65 or TNF treatment),
luciferase signal was decreased, indicating that the effect on TET1 expression was, at least
in part, promoter-dependent (Fig. 53A).
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Fig. 53: Binding of p65 to TET1 promoter. (A) TET1 promoter activity was assessed
by cotransfecting a Firefly luciferase vector under the control of TET1 promoter (TET1-
LUC) and a control Renilla luciferase vector (R-LUC) in MDA-MB-231 cells before TNF
treatment or overexpressing p65 (n=3, data expressed as mean ± SD, relative to control
condition). (B) ChIP was performed with an antibody targeting p65 or control IgG to
assess binding to TET1 promoter in MDA-MB-231 cells. TNF treatment (30 min) was
used to induce nuclear translocation of p65. Positive and negative controls (Pos1-2 and
Neg1-2) were chosen based on public p65 ChIP-seq data (GSM1055811). (C)
Streptavidin-agarose pulldown assays were performed with biotinylated DNA probes
corresponding to the predicted NF-κB binding sites A and B. To assess the specificity of
the binding, pulldowns were achieved with either the wildtype sites or a mutated version
in which the consensus NF-κB binding sequence was disrupted (wildtype probes: A, B;
mutated probes: A mut, B mut, representative of three independent experiments).
Next, we tested the putative binding of NF-κB to TET1 promoter with two
different methods. First, we performed ChIP-qPCR with a p65-targeting antibody. Upon
TNF treatment, binding of p65 to TET1 promoter was increased by 2.7-fold over IgG
(p=0.03) (Fig. 53B). While this result confirmed that TET1 promoter could be bound by
p65, the close proximity of the two binding sites (around 200bp) did not allow to distinct
them properly by ChIP-qPCR. Hence, we used a second approach: streptavidin-agarose
pulldown assays. This method, based on short DNA probes, allowed a better resolution
than ChIP analyses and had been used previously to study NF-κB binding sites (Deng et
al. 2003; Wu 2006). Briefly, proteins extracted from cells (treated or not with TNF) were
incubated with biotinylated probes representing the two potential binding sites, and the
binding of proteins to the probes was revealed by pulldown with streptavidin-agarose
beads followed by Western blot (Fig. 53C). The assay was performed with either the
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wildtype A and B site probes or mutated versions, in which the consensus NF-κB binding
sequences were disrupted (see supplementary figure S6 of the manuscript presented in
appendix I for more information on the probe sequences). Strikingly, the B probe showed
a strong p65 binding upon TNF treatment that was mostly lost with the disruption of the
consensus sequence. In contrast, the A probe showed a weak binding at the background
level of its mutated version.
The above results suggested that TET1 is repressed through binding of NF-κB to
its promoter. The B site was the most potent binding site responsible for NF-κB-mediated
regulation in breast.
1.2.5. TET1 is downregulated by NF-κB in other cancer types
In the last part of this study, we investigated whether NF-κB-dependent
dysregulation of TET1 occurred in other cancer types, beside basal-like breast cancers.
This question was raised by the knowledge that both TET1 downregulation and NF-κB
activation have been observed in many cancer types (Ben-Neriah & Karin 2011; Jeschke
et al. 2016).
Therefore, we screened the TCGA cohorts for all available cancer types with
RNA-Seq data. First, we scored the “20 immune gene signature” initially identified in
BLBC (Fig. 46) in each cancer cohort in order to evaluate the global immune state of the
tumors. Then, we calculated the correlation between this signature score and TET1
expression. The list of all TCGA cancer cohorts and their correlation coefficient is
provided in Table 1 of the manuscript presented in appendix I. Most cancer types
displayed a shift in global immune state that was significantly correlated with TET1
expression, including thyroid carcinoma (THCA), skin cutaneous melanoma (SKCM)
and lung adenocarcinoma (LUAD) (Fig. 54). The combined scores for the immune
signature presented a correlation coefficient of r=-0.33 (p=10-14), r=-0.33 (p=10-13) and
r=-0.28 (p=9.10-11) for THCA, SKCM and LUAD, respectively. Interestingly, these
cancers are all known to be infiltrated or surrounded by immune-reactive cells (Oble et
al. 2009; Imam et al. 2014; Kargl et al. 2017).
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Fig. 54: TET1 expression and immune markers in cancer. Heat map illustrating the
expression (RSEM z score) of the 20 “immune response” gene signature from Fig. 45 in
several cancer types (from left to right, THCA: thyroid carcinoma, SKCM: skin cutaneous
melanoma, and LUAD: lung adenocarcinoma). Data were taken from TCGA cohorts and
ordered by TET1 expression for each cancer type.
Hence, TET1 downregulation was associated with increased expression of many
immune markers in several cancers. Furthermore, we investigated the potential link
between NF-κB signaling and TET1 regulation in the same cohorts by scoring the NF-κB
signature previously used (Van Laere et al. 2006). Consistent with results in BLBC, high
expression of TET1 was associated with low NF-κB signature, and vice versa, in THCA,
SKCM and LUAD (Fig. 55). This result was the first piece of evidence supporting the
concept that NF-κB activation might be associated with TET1 dysregulation in cancers,
beyond BC.
Fig. 55: TET1 expression and NF-κB signature. High expression of TET1 was
associated with a low NF-κB signature score in THCA (n=509), SKCM (n=472) and
LUAD (n=510) tumors. Gene expression data (RSEM z scores) were obtained from
TCGA.
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We further explored the association between TET1 and NF-κB as we tested the
causality of the relationship. This was performed by activating NF-κB with TNF in vitro
in TPC1, A375 and A579 cell lines, derived from thyroid cancer, melanoma, and lung
cancer, respectively (Fig. 56A). Similarly to our results in breast cancer cells, decreased
TET1 expression was consistently observed upon TNF treatment in TPC1, A375 and
A579 cells (3.2-fold with p=0.0001; 1.9-fold with p=0.02 and 1.6-fold with p=0.03;
respectively). Therefore, NF-κB-dependent regulation of TET1 appears to not be
restricted to BLBC, and instead can also occur in other tumors. Finally, we confirmed
that NF-κB could bind to the TET1 promoter via its consensus target sequence.
Streptavidin-agarose pulldown assays were performed with the probe corresponding to
the NF-κB B binding site, and p65 binding was observed upon TNF induction, in thyroid
TPC1 cells, melanoma A375 cells and lung A549 cells (Fig. 56B). As observed in the
breast cells, disruption of the NF-κB consensus sequence of the probe reduced binding.
Fig. 56: NF-κB represses TET1 expression in cancer. (A) NF-κB was activated by TNF
treatment for 4h in thyroid cancer TPC1 cells, melanoma A375 cells and lung cancer
A549 cells. TET expression was measured by RT-qPCR (n=3, data expressed as mean ±
SD relative to control). (B) Streptavidin-agarose pulldown assays were performed as
previously described to assess the binding of NF-κB member p65 to TET1 promoter in
TPC1, A375 and A549 cell lines (representative of three independent experiments).
Taken together, these results suggested that the previously identified mechanism,
in which immunity drives TET1 downregulation through NF-κB activation and binding
to its promoter, was not restricted to BLBC and could instead be mechanisms shared by
many cancer types.
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1.3. Key findings
This study aimed to explore dysregulation of TETs and 5hmC in BC, and some of
the uncovered concepts extend well beyond the epigenetics of breast tissues. First, TET1
dysregulation (either up- or downregulation) correlates with distinct profiles of DNA
hydroxymethylation in BC. These profiles are further associated with changes in 5mC
and gene expression. Secondly and importantly, TET1 downregulation is associated with
increased expression of immune markers in basal-like breast cancers. Noteworthy,
beyond the mere activation of immune genes, the tumor infiltration by several types of
leukocytes is also negatively correlated with TET1 expression. Thirdly, activation of the
NF-κB pathway can downregulate TET1. NF-κB member p65 notably binds to the TET1
promoter upon activation of the pathway, and TET1 repression in this context is promoter-
dependent. Finally, examination of the TCGA cohorts and in vitro results suggest that the
mechanism of TET1 downregulation is relevant to many cancer types.
In conclusion, our findings unravel for the first time a paradigm in which the
immune system can regulate cancer epigenetics via dysregulation of the TETs, opening
new avenues in terms of cross-talk between cancer cells and the micro-environment.
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2. RNA hydroxymethylation: a new player in the
game
2.1. Introduction
In DNA, TET enzymes (TET1, TET2, and TET3) catalyze the oxidation of 5-
methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). Since the discovery of their
implication in DNA demethylation, TETs and 5hmC have been involved in many
mechanisms linked to health and disease (Delatte & Fuks 2013). Yet, TET-mediated
regulation is not restricted to their activity on DNA. For instance, several studies have
already suggested an effect of TETs independent of their catalytic activity (Kaas et al.
2013; Deplus et al. 2013). Furthermore, and most importantly, the existence of TETs’
substrate, 5-methylcytosine, in RNA transcripts (here abbreviated as 5mrC) raised the
question of the potential existence of TET-mediated oxidation to form RNA 5-
hydroxymethylcytosine (5hmrC). Two independent studies have confirmed the existence
of 5hmrC in various species, although its functional role remains undetermined (Fu et al.
2014; Huber et al. 2015).
In this context, our host laboratory set the aim to unravel the first transcriptome-
wide distribution and function of RNA hydroxymethylcytosine in order to provide a better
understanding of 5hmrC. We started by investigating 5hmrC in Drosophila melanogaster
for the following reasons: (i) 5mC and 5hmC in Drosophila DNA are absent, and (ii) there
is only one Tet gene in Drosophila, making it an easier model to study than mammals.
We notably mapped the transcriptome-wide 5hmrC landscape, revealing
hydroxymethylcytosine in the transcripts of many genes, and particularly in coding
sequences. Moreover, we found that RNA hydroxymethylation can favor mRNA
translation. Finally, Tet and hydroxymethylated RNA are particularly abundant in the
brain, and loss of Tet and 5hmrC impaired brain development.
Following up on this pioneer study, we started to explore the involvement of RNA
hydroxymethylcytosine in other contexts related to human health and disease. Hence, we
decided to map 5hmrC in cancer, a disease in which TETs are known to be vastly
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dysregulated, more specifically in breast cancer. By doing so, we identified hundreds of
transcripts differentially hydroxymethylated, including some key cancer factors. This
finding raises the question of the functional impact of 5hmrC dysregulations on
tumorigenesis and highlights a new level of cellular alterations in malignant cells.
Personal contribution to this study includes:
- Interactions with the other contributors, including the teams of Véronique
Kruys and Ruth Steward;
- Cell culture, knockdown by RNA interference, RT-qPCR, in vitro
transcription, dot blot assays, interpretation of sequencing results, statistical
analyses;
- Preparation of the figures (along with Rachel Deplus)
This chapter summarizes the major points of our study. For further details, please see the
related manuscript provided in Appendix II (Delatte et al., “Transcriptome-wide
distribution and function of RNA hydroxymethylcytosine”, published in Science in 2016).
Of note, the breast cancer results are not included in the published manuscript and are
part of an ongoing study.
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2.2. Results
2.2.1. RNA hydroxymethylation by dTet in Drosophila S2 cells
To detect and measure 5-hydroxymethylcytosine RNA modification in
Drosophila, we set up a dot blot method based on an antibody raised against the modified
base (Kallin et al. 2012; Wu et al. 2011). First, to confirm that this method can detect
5hmrC, we performed dot blot experiments using in vitro transcribed templates
containing either unmodified, methylated, or hydroxymethylated cytosines (Fig. 57). As
expected, the antibody specifically recognized the transcripts containing 5-
hydroxymethylcytosine, and the binding was abolished after ribonuclease (RNase) A
treatment.
Fig. 57: Validation of 5hmrC-detecting
dot blot method. Antibody specificity and
sensitivity for 5-hydroxymethylcytosine-
containing RNA. Serially halved amounts
(starting at 50 ng) of transcribed template
containing either C, 5mrC or 5hmrC were
dot blotted and detected with an anti-5hmC
antibody. A representative experiment is
shown, which was successfully repeated 3
times.
After validating the dot blot method, we detected 5hmrC in dot blot experiments
on total RNA extracted from Drosophila S2 cells (Fig. 58A). Then, in order to determine
whether certain RNA fractions were more enriched in 5hmrC in Drosophila, isolation of
polyadenylated (poly A) RNA from S2 cells was performed, followed by
immunoblotting. S2 poly A RNA showed strong enrichment in 5hmrC signal as compared
with that of total cellular RNA (Fig. 58B). In contrast, in fractions enriched in small RNAs
or ribosomal RNAs, no 5hmrC signal was detected (Fig. 58C). These results indicated
that 5hmrC was most frequent in poly A RNA, which includes mostly messenger RNAs
and long non-coding RNAs.
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Fig. 58: 5hmrC is enriched in poly A RNA in S2 Drosophila cells. (A) Dot blotting on
total RNA from Drosophila S2 cells with antibody to 5hmC, treated or not with RNase A
(serially halved amounts of RNA, starting at 1 µg). Data are mean ± SD (n = 4
experiments run) with a representative blot shown. (B) Immunoblotting with anti-5hmC
antibody was performed on polyadenylated and total RNA from S2 cells. Data are mean
± SD (n = 3 experiment run). (C) 5hmrC content of total RNA as well as fractions
enriched in small RNA or rRNA was assessed by dot blotting. Data are mean ± SD (n =
3 experiments run). A vertical line indicates juxtaposition of lanes within the same blot,
exposed for the same time.
Next, we sought to assess whether Tet was responsible for RNA
hydroxymethylation. As previously mentioned, Drosophila possesses only one conserved
Tet ortholog, hereafter named dTet (Gowher et al. 2000; Zhang et al. 2015). Following
knockdown of dTet in S2 cells by using RNA interference (dTet KD), we observed a 44%
reduction in 5hmrC signal detected by dot blot (Fig. 59). This result suggests that dTet is
the enzyme responsible, at least in part, for the formation of RNA hydroxymethylation.
Fig. 59: dTet knockdown leads to reduced
5hmrC levels. (Left) Knockdown of dTet was
assessed by RT-qPCR analysis. (Right)
5hmrC levels were assessed by dot blotting.
Data are mean ± SD (n = 4 experiments run).
In conclusion, dot blot analyses indicate that 5hmrC exists in Drosophila RNA, is
enriched in the poly A fraction, and could be catalyzed by dTet.
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2.2.2. Transcriptome-wide mapping of 5hmrC in S2 cells
Next, we aimed to provide the first transcriptome-wide mapping of 5hmrC. This
was performed by hydroxymethylated RNA immunoprecipitation followed by
sequencing (hMeRIP-seq). This method involves immunoprecipitation of 5hmrC-
containing RNA with the antibody to 5hmC, followed by next-generation sequencing.
In S2 cells, hMeRIP-seq uncovered 3058 significantly enriched regions (“5hmrC
peaks,” p < 10−10). Among those regions, 1597 coding gene transcripts were found.
Examples of enrichment profiles are shown in Fig. 60A. The distribution of 5hmrC peaks
revealed by hMeRIP-seq analyses showed an enrichment in coding sequences (48%) and
introns (17%) (Fig. 60B). Further analyses also uncovered a motif commonly associated
with 5hmrC peaks (64% of identified target sites) that was highly UC-rich and containing
UCCUC repeats (see Fig. 2 of the manuscript presented in appendix II for more details).
Fig. 60: Transcriptome-wide distribution of 5hmrC in Drosophila cells. (A)
Representative UCSC Genome Browser plot from hMeRIP-seq data. The upper lane
represents the hMeRIP data, the lower lane the control (Ctrl, representative of the sample
input). (B) Distribution of 5hmrC peaks according to the type of structural element within
the transcript. *P < 10−5).
Given that the hMeRIP-seq was a newly established technique, we included
several controls in order to ensure the specificity of the signal. First, bioinformatic
analyses established that our hMeRIP-seq data did not merely select abundant RNA
fragments in non-specific manner (see Fig. 2B and Fig. S5 of the published manuscript).
Secondly, upon dTet depletion, up to 79.4% of peak sites showed a significant reduction
in 5hmrC signal, as compared with control S2 cells. Moreover, among reduced targets,
85.5% of peak sites displayed more than four-fold of reduction in 5hmrC levels (Fig. 61).
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Finally, we replicated our hMeRIP-seq results by performing the immunoprecipitation
with a different antibody targeting 5hmC. We obtained a strong agreement between
sequencing performed with the two 5hmC antibodies (77% of common peaks, see Fig.
S7 of the published manuscript for more details).
Fig. 61: dTet mediates transcriptome-wide RNA hydroxymethylation in Drosophila.
(A) hMeRIP-seq in cells depleted of dTet shows reduced 5hmrC levels at the majority of
target regions. (Left) Box plot of the normalized number of 5hmrC reads in dTet-depleted
cells versus control cells. (Center) Pie chart showing the percentage of reduced 5hmrC
peaks, with (right) a more than fourfold reduction at most targets. (B) Example of
enrichment profiles (UCSC tracks) of hMeRIP-seq done in control and dTet-depleted
cells.
Next, we aimed to better understand the role of dTet in gene regulation. Thus, we
performed RNA-seq in S2 cells, and we identified 574 differentially expressed mRNAs
upon depletion of dTet: 50.4% showed increased expression, 49.6% decreased expression
(Fig. 62). We then compared dTet-regulated mRNAs with the targets identified by
hMeRIP-seq, and we identified a small but very significant subset of common targets (p
< 10−43). Precisely, 26% of dTet-regulated mRNAs contained at least one 5hmrC peak
(Fig. 61). It is worth noting that dTet contains an N-terminal CXXC Zn-finger domain.
In mammalian cells, the presence of this DNA-binding domain is thought to explain, at
least in part, the ability of Tets to regulate gene expression independently of their catalytic
activity (Williams et al. 2011; Deplus et al. 2013). Thus, in Drosophila cells, dTet might
also affect gene expression via its CXXC domain independently of its
hydroxymethylation activity.
Interestingly, by gene ontology analysis of the 5hmrC targets, we observed an
enrichment for genes involved in cellular processes, including in the regulation of
embryogenesis, development and neurogenesis (see Fig. S8B of the published
manuscript). These findings suggest that dTet-mediated RNA hydroxymethylation might
influence the development of fruit flies.
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Fig. 62: 5hmrC and gene expression upon depletion
of dTet in S2 cells. (A) RNA-seq was performed on
dTet-knockdown and control S2 cells. (B) A partial
overlap was observed between hMeRIP-seq and RNA-
seq data sets.
In conclusion, 5hmrC was identified in over 3000 mRNA regions in Drosophila
S2 cells, and this RNA hydroxymethylation seems, at least in part, mediated by dTet.
Gene ontology analysis performed on 5hmrC targets suggest a potential role in
developmental regulation.
2.2.3. Hydroxymethylation can favor mRNA translation
Next, we sought to determine how cytosine hydroxymethylation might affect
mRNA functions. One aspect of RNA metabolism that can be affected by chemical
modifications is the efficiency of protein translation (Meyer et al. 2015).
Thus, we looked at the distribution of 5hmrC, in respect with the translational
status of mRNAs in Drosophila S2 cells. Specifically, we performed standard sucrose-
gradient fractionation, in order to separate free mRNAs from mRNA bound by ribosome
subunits (40S and 60S), single ribosomes (80S) or polysomes, and we measured 5hmrC
by dot blot in each fraction. Interestingly, we observed a positive correlation between
5hmrC abundance and active mRNA translation: fractions with low translation activity
(free mRNAs and mRNAs bound by 40S/60S/80S) displayed low levels of 5hmrC,
whereas highly translated mRNAs (bound by several ribosomes to form a polysome)
showed a high 5hmrC content (Fig. 63A). In contrast, dot blot quantification of
methylated RNA (5mrC) in the ribosomal fractions showed an enrichment in lowly-
translated mRNAs (bound by single ribosomes) and low levels in polysomes (Fig. 63B).
This was a first piece of evidence that 5hmrC, but not 5mrC, was associated with high
levels of translation.
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Fig. 63: Highly translated mRNAs
display high levels of 5hmrC. (A)
Sucrose gradient fractionation followed
by dot blot quantification shows that
active translation is associated with high
5hmrC contents. Data are represented as
mean ± SD (n = 3 experiments run). (B)
Dot blot quantification of 5mrC in the
same fractions shows that low translation
is associated with high 5mrC contents.
Data are represented as mean ± SD (n =
3 experiments run).
Based on the above findings, we next investigated whether mRNA
hydroxymethylation might affect mRNA translation. Therefore, we produced by in vitro
transcription RNA templates (encoding Firefly Luciferase) bearing unmodified,
methylated, or hydroxymethylated cytosines. RNA templates were then incubated in
rabbit reticulocyte lysate to allow protein translation to occur in vitro, and translation
efficiency was assessed by the incorporation of 35S-radiolabeled methionine and by
Western blot (targeting the Firefly Luciferase) (Fig. 64). With both methods, a decrease
in translation was observed for methylated RNAs, compared to unmodified RNAs. In
contrast, 5hmrC-modified templates restored translation levels to near-control levels,
suggesting that hydroxymethylation can counteract the effect of RNA methylation. These
experiments were performed in collaboration with the team of Véronique Kruys.
Fig. 64: 5hmrC favors mRNA translation. In vitro
translation of unmodified C-, 5mrC-, and 5hmrC-
containing RNAs (encoding for Firefly Luciferase, or
“F-luc”), was measured by incorporation of 35S-
radiolabeled methionine (top panel, n=3 with data as
mean ± SD) and Western blot (bottom panel,
representative of 3 experiments run).
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Taken together, our data suggest that hydroxymethylation is associated with
highly translated transcripts and that it can restore the translation efficiency of previously
methylated substrates. Overall, 5hmrC appears to favor translation.
2.2.4. In vivo relevance of 5hmrC in Drosophila
Next, we collaborated with the team of Ruth Steward (Rutgers University, New
Jersey, USA), who specializes in the study of fruit flies, in order to assess 5hmrC in vivo.
First, we looked at the expression of dTet and the levels of 5hmrC by RT-qPCR and dot
blot, respectively, during early embryogenesis of Drosophila melanogaster (Fig. 65). Both
showed a similar pattern: increasing levels up to 10h post-fertilization, followed by a
decrease. Thus, dTet expression and 5hmrC levels displayed a positive correlation during
embryogenesis.
Fig. 65: Levels of dTet and 5hmrC
in early embryogenesis. dTet
expression and 5hmrC levels were
measured by RT-qPCR (left) and dot
blot (right), respectively, during
Drosophila embryogenesis. Time is
indicated in hours post-fertilization.
Data are mean ± SD (n = 4
experiments run).
We also analyzed publicly available RNA-seq data from the modENCODE
database in order to assess dTet expression in organs at different stages of D. melanogaster
development. Strikingly, dTet expression was the highest in the central nervous system
of the third-instar larvae (see Fig. S13B of the published manuscript). This observation,
combined with the presence of genes related to neurogenesis among 5hmrC targets in S2
cells, raised the question of a potential role for dTet-mediated hydroxymethylation of
RNA in the fruit fly brain development. To test this hypothesis, we first confirmed the
high expression of dTet in the central nervous system compared to other tissues by two
different methods. Firstly, we used a transgenic fly model, in which endogenous dTet was
tagged with the green fluorescent protein (GFP). The GFP-dTet fusion protein was
detected throughout the larval brain, with the highest levels being detected in the optic
lobe and central brain (Fig. 66A). Secondly, RT-qPCR confirmed the high expression of
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dTet as compared to two other organs, the salivary glands and the ovaries (Fig. 66B).
These organs were chosen as controls because: (i) ovaries were the organs in which it was
previously shown that dTet possesses a DNA m6A demethylation activity (Zhang et al.
2015), and (ii) the salivary gland was an organ of sufficient size for us to extract enough
RNA to measure 5hmrC levels. Importantly, 5hmrC positively correlates with dTet
expression, with the highest levels being in the brain (Fig 66B). Hence, several
observations supported the possibility of a role for 5hmrC in this organ.
Fig. 66: dTet and 5hmrC levels in the brain. (A) (Left) Pattern of endogenous GFP-
tagged dTet in the larval brain was visualized by confocal microscopy analysis. OL and
CB indicate the optic lobe and the central brain, respectively. Scale bar, 50 mm. (Center)
Scheme of the larval brain. (B) (Top) dTet expression in the salivary gland, brain, and
ovary, measured by RT-qPCR. Data are mean ± SD (n = 3 experiments run). (Bottom)
Immunoblotting with 5hmC antibody in RNA from salivary gland, brain, and ovary.
Vertical line indicates juxtaposition of lanes within the same blot, exposed for the same
time.
To confirm the role of RNA hydroxymethylation in fruit flies brain development,
we took advantage of loss-of-function mutant of dTet developed by the team of Dr.
Steward. In agreement with recent published data (Zhang et al. 2015), dTet-deficient flies
grew up to the pupal stage, then displayed massive lethality. From over 5000 dTet-null
animals analyzed, no animal reached the adult stage. Moreover, morphological defects
were found at larval stages, with decreased brain size and abnormal neuroblast
organization in the central part. In particular, the width of the medulla region was reduced
in dTet-null flies (p < 2.10−9) (Fig. 67A). Importantly, in dot blot analyses, 5hmrC was
also reduced in the brains of dTet deficient larvae (Fig. 67B). It is worth noting that even
in RNA extracted from dTet-null animals, there are residual levels of 5hmrC, which
means that dTet-mediated oxidation might not be the only source of 5hmrC.
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Taken together, our data indicate that dTet and 5hmrC are particularly relevant to
brain development in Drososphila.
Fig. 67: dTet-deficient fruit flies show
impaired brain development, accompanied
by decreased 5hmrC. (A) Average width of the
medulla region containing the neuroblasts (left,
red arrow). Error bars represent 95%
confidence intervals (p < 2.10−9) for 20 brain
lobes. (B) Brains of dTet-deficient larvae show
a decrease in 5hmrC by dot blot analysis. Data
are mean ± SD (n = 3 experiments run).
In conclusion, the investigation of RNA hydroxymethylation in Drosophila
constituted the first study addressing the distribution, localization, and function of this
RNA modification. Our work provided a picture of the hydroxymethylated transcriptome,
identified an unrecognized function for 5hmrC in translation regulation, and identified a
major role of this modification in neurogenesis. Following on this pioneer study, we next
aimed to explore the role of 5hmrC in other biological contexts, and in particular, human
health and disease. In the next section, we will specifically focus on the study of 5hmrC
in the context of breast cancer.
2.2.5. Alterations of 5hmrC in breast cancer
Taking advantage of the methods developed for the study of 5hmrC in Drosophila,
we next aimed to investigate whether 5hmrC could be dysregulated in breast cancer (BC).
For this study, we had at our disposal a panel of breast cell lines: one non-cancerous breast
cell line (MCF12) and six BC cell line (MCF7, ZR75-1, T47D, MDA-MB-231, BT474,
SKBR3). We started by measuring 5hmrC and 5mrC by dot blot analysis in the different
fractions of RNA in all these cell lines (Fig. 68). First, we observed that 5hmrC and 5mrC
had different distributions overall. In agreement with our results in Drosophila (Fig. 58),
5hmrC was globally enriched in poly A RNA compared to total RNA (Fig. 68A).
However, we were also able to detect 5hmrC – albeit at low to medium levels – in small
RNA and rRNA, in discrepancy with results from Drosophila where 5hmrC was
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undetectable in the same RNA fractions. This might be an interspecies difference between
human and fly cells. In contrast, 5mrC showed a different distribution: the mark was
globally depleted in poly A RNA and increased in small RNA and rRNA (Fig. 68B).
Moreover, and importantly, BC cells displayed overall reduced levels of 5hmrC, both in
total and poly A RNA, compared to non cancerous breast cells. In contrast, 5mrC was
either unchanged or increased in BC cells compared to non-cancerous cells.
Fig. 68: RNA methylation and hydroxymethylation in mammary cells.
Immunoblotting for (A) 5hmrC, and (B) 5mrC, in RNA from a panel of breast cell lines.
The different RNA species (100ng of poly A RNA or 200ng of total RNA, small RNA and
rRNA) were blotted on the same membrane and exposed for the same time in order to
allow comparison.
Next, we aimed to provide the first transcriptome-wide map of 5hmrC alterations
in BC. Thus, we performed hMeRIP-seq in the non-cancerous breast MCF12 cells and in
breast cancer MDA-MB-231 cells. We identified 1605 and 946 5hmrC peaks, which were
associated with 1093 and 681 transcripts, in MCF12 and MDA-MB-231, respectively. In
both breast cell lines, 5hmrC peaks showed a non-random distribution, with a specific
enrichment in intronic regions (Fig. 69). This result is in contrast with previous results
showing a preferential enrichment in coding sequences in Drosophila cells, suggesting
again that there might be interspecies differences in the transcriptomic distribution of
5hmrC.
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Fig. 69: Distribution of 5hmrC peaks in breast cells. The region enrichment
corresponds to the ratio between the observed number of peaks and the number expected
by chance, in each region.
When comparing the two cell lines, we identified 998 differentially
hydroxymethylated regions, associated with 725 RNA transcripts (Fig. 70A). Overall, the
majority of 5hmrC changes were hypo-hydroxymethylated in the cancer MDA-MB-231
cells, which was in agreement with the global loss previously observed (Fig. 68 and 70B).
Furthermore, the majority of transcripts (nearly 70%) showed at least a 4-fold difference
(Fig. 70B). Thus, breast cancer cells display a massive redistribution of 5hmrC.
Fig. 70: Changes in 5hmrC in BC. (A) Based on 5hmrC-sequencing, we identified 725
differentially methylated transcripts between the non-cancerous breast cells (MCF12A)
and the BC cells (MDA-MB-231). These changes are displayed on the heatmap. (B) Pie-chart showing that changes in 5hmrC included more losses (hypo-hydroxymethylation, in
green) than gains (hyper-hydroxymethylation, in red). The majority of changes
corresponded at least to a 4-fold difference.
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Gene ontology analysis, performed with IPA®, indicated that genes associated
with 5hmrC changes between MCF12 and MDA-MB-231 cells were significantly linked
to pathways such as “estrogen receptor signaling”, “breast cancer” and “cytoskeleton”,
which are all relevant pathways in the context of mammary tumors. This finding
suggested that changes in 5hmrC are associated with known molecular alterations of BC.
Changes in 5hmrC notably affected transcripts of key cancer genes, such as BRCA2, p53,
CDKs, or RAD51. Examples of 5hmrC tracks are provided in Fig. 71.
Fig. 71: Examples of 5hmrC tracks in breast cells.
In conclusion, we have performed a first investigation of RNA
hydroxymethylation dysregulations in breast cancer. Although these are still early results,
our data suggest that 5hmrC is vastly altered in BC, both globally and locally, and notably
at mRNAs that are known to be involved in cancer development.
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2.3. Key findings
This study aimed to uncover for the first time the distribution and function of
5hmrC. Hydroxymethylcytosine, which is well established in DNA, was recently shown
to also occur in RNA. In Drosophila, we showed that 5hmrC preferentially marked
polyadenylated RNAs and was mediated by dTet. We mapped the transcriptome-wide
landscape of the mark, revealing 5hmrC in the transcripts of many genes, notably in
coding sequences. We also found that RNA hydroxymethylation can favor mRNA
translation. Importantly, in Drosophila, dTet and 5hmrC are found to be most abundant
in the brain, and Tet-deficient fruit flies suffered from embryonic lethality with impaired
brain development and decreased RNA hydroxymethylation. Thus, we identified a central
role for 5hmrC in Drosophila development.
In a follow-up study, we provided the first hints that 5hmrC might be widely
dysregulated in breast cancer. Comparison between non-cancerous and cancer cell lines
notably showed hundreds of differentially transcripts, including of key cancer genes. Our
findings highlight a new level of dysregulation in cancer, thus supporting the potential of
RNA modifications as new cancer biomarkers.
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3. Dysregulations of m6A and its machinery
support breast cancer
3.1. Introduction
N6-Methyladenosine (m6A) is by far the most abundant modification in mRNA,
and it also exists at lower levels in tRNA, rRNA, small nuclear RNA (snRNA) as well as
several long non-coding RNA. It has been identified in bacteria and eukaryote cells,
including mammals, insects, plants and yeast. It is also the RNA modification with the
best characterized machinery: the mark is added by a METTL3/METTL14 complex, and
it removed by ALKBH5 and FTO, which offers a very dynamic system. Furthermore,
several m6A readers have been identified (e.g. the YTHDF and YTHDC families) and
they can carry out the effect of m6A on RNA stability, splicing, localization and
translation. Taking advantage of this understanding of the m6A dynamicity and functions,
recent attention was brought to the biological roles of the mark. It has notably been
involved in the regulation of pluripotency and cell differentiation during development
(Wang et al. 2014; Geula et al. 2015), thus suggesting that RNA modifications can have
crucial roles in health and diseases.
In that context, we aimed to study the role of m6A and its machinery in breast
cancer development and progression. Recent studies have indeed suggested that loss of
m6A on the transcripts of several key cancer factors could promote leukemia and
glioblastoma (Zhang et al. 2017; Cui et al. 2017; Li et al. 2017), indicating that the study
of m6A players could be valuable in cancer. In breast cancer, we notably mapped the
transcriptome-wide m6A landscape, revealing N6-methyladenosine in the transcripts of
many genes, and particularly in coding sequences, near the stop codon and the 3’UTR. In
cell lines, we identified many transcripts differentially methylated between non-
cancerous breast cells and cancer cells, particularly in major cancer pathways. In human
tumors, we uncovered that FTO, the m6A demethylase, is often downregulated, which is
associated with increased m6A levels and poor survival. Finally, we showed in vitro that
depletion of FTO in mammary cells led to a more aggressive phenotype of the cancer
cells.
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Taken together, our data on m6A and 5hmrC (see previous chapter) in breast
cancer both indicate that alterations of RNA modifications and their machinery constitute
a novel level of aberrant gene regulation promoting cancer. Thus, understanding how
RNA modifications can affect various cancers will be a key challenge for the future.
Personal contribution to this study includes:
- The general coordination of this project through interactions with the other
contributors;
- The design of experiments and interpretation of the data (along with Dr. Jana
Jeschke from our host laboratory);
- Cell culture and cell treatments, establishment of stable knockdown in cell
lines, RT-qPCR, dot blot analysis, Western blot, luciferase assays,
xCELLigence experiments (migration and invasion), statistical analyses;
- Preparation of the figures;
This chapter summarizes the major points of our study, which will be prepared for
publication in a near future.
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3.2.Results
3.2.1. Transcriptome-wide m6A landscape in breast cancer
Our first aim in this study was to determine whether changes in m6A occurred in
BC. To answer this question, we mapped m6A in several breast cell lines, including
MCF12, SKBR3, and MCF7. This was performed by methylated RNA
immunoprecipitation followed by sequencing (MeRIP-seq), based on a method described
in a published study (Dominissini et al. 2012).
Fig. 72: Transcriptome-wide mapping of m6A in SKBR3 cells. (A) Number of m6A
peaks and m6A-containing transcripts, based on MeRIP-seq in SKBR3 cells (top).
Examples of two m6A peaks are shown (bottom). (B) Region m6A enrichment, measured
by the ratio between the number of observed peaks and the number expected by chance
for each transcriptomic region (from left to right: 5’UTR, near start codon, coding
regions, introns, near stop codon and 3’UTR). (C) Motif analysis revealed that the GACU
motif, a known m6A motif, was commonly observed in m6A targets.
We began by assessing the quality of our m6A-sequencing experiments, and this
was done by looking the features of m6A distribution. In the SKBR3 BC cells, we
identified 5760 m6A peaks, associated with 3342 different transcripts. Examples of peaks
are shown in Fig. 72A. The m6A signal was specifically enriched in the coding region,
near the stop codon and in the 3’UTR (Fig. 72B). These were the same regions that were
shown to be enriched for m6A in other biological contexts (Luo et al. 2014; Geula et al.
2015). Furthermore, analyses also uncovered a motif commonly associated with m6A
target regions (approximately 1/3 of all peaks), i.e. the GACU motif (Fig. 72C). This
sequence corresponded exactly to one of the main known motifs for m6A, which has been
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previously detected in ES and other cells (Wu et al. 2016). Similar results were obtained
for the other breast cell lines (i.e. MCF7, BT20, T47D – data not shown). Hence, the
distribution of m6A was non-random and the “classical” m6A feature were conserved.
Next, we aimed to provide the first transcriptome-wide map of m6A alterations in
BC, and we performed this by comparing m6A sequencing results in SKBR3 breast
cancer cells and in MCF12 breast cells.
The latter is a non-tumorigenic epithelial cell line established from a reduction
mammoplasty (Paine et al. 1992). MCF12 cells are non-transformed but immortalized
and hyperplastic. They differ from true normal breast cells, notably through their
abnormal karyotype. Nevertheless, they constitute a non-cancerous breast model that has
been previously used to highlight epigenetic alterations in BC (Stolzenburg et al. 2012;
Park et al. 2015; Bagu et al. 2017). This cell line is particularly useful in studies that
require large amount of material, as was the case for our m6A MeRIP-seq experiments.
Regrettably, reduction mammoplasty only yields limited amount of material, as the
majority of the volume is taken by fatty tissues, and we were unable to acquire enough
RNA from normal breast tissue. Thus, we decided to compare our cancerous SKBR3 cells
to the non-cancerous MCF12 breast cells.
By comparing SKBR3 and MCF12 cells, we identified 1962 differentially
methylated regions (DMRs, defined by a minimum fold-change of 2), with a majority
(about 2/3) of hypo-methylation in cancer cells (Fig. 73A and 73B). Overall these changes
were vast, with nearly half of them displaying at least a 4-fold difference. A heat map
illustrating the top 100 DMRs and example of m6A tracks are shown on Fig. 73C and
73D, respectively. In conclusion, our data from MeRIP-seq suggested that there was a
vast redistribution of m6A in breast cancer cells.
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Fig. 73: m6A changes in cultured BC cells. (A) Changes observed in m6A MeRIP-seq
(IP), in respect with gene expression (input), between SKBR3 BC cells and MCF12 breast
cells. (B) Pie-chart representing the gains (hyper) and losses (hypo) of m6A in BC cells
(left panel). About half of these changes corresponded at least to a 4-fold difference (right
panel). (C) Heat map showing the m6A changes of the top 100 differentially methylated
regions (DMRs). (D) Examples of m6A tracks in SKBR3 and MCF12 cells.
Importantly, we were able to get access to, and profile m6A by MeRIP-seq in three
human breast cancer biopsies. This was the first mapping of m6A performed in any
human cancer tissue, all previous sequencing of m6A in cancer having been performed
on RNA extracted from cell lines (Cui et al. 2017; Li et al. 2017; Zhang et al. 2017). The
three samples yielded similar numbers of m6A peaks (4132, 4124 and 3541, respectively)
and associated transcripts (2681, 2721 and 2350) (Fig. 74A). Notably, profiling of m6A
in human biopsies showed a high level of consistency, with 86% of m6A-containing
targets being shared by at least two out of three samples, and 67% being shared by all
three. Even within transcripts, the position and shape of the peaks showed great similarity
(Fig. 74B). Furthermore, we looked at the transcriptomic distribution of m6A, and all
three human biopsies showed an enrichment of peaks in coding sequences, near the stop
codon, and in the 3’UTR (Fig. 74C), which was similar to our observations in breast cell
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lines (Fig. 72B) and in published results from other biological models (Luo et al. 2014;
Geula et al. 2015)
Fig. 74: Transcriptome-wide mapping of m6A
in human BC biopsies. (A) MeRIP-seq was
performed on RNA extracted from three distinct
human BC biopsies. The number of m6A peaks
and m6A-containing transcripts are indicated for
each sample. (B) Example of m6A tracks for a
peak shared by all three samples. (C) Region m6A
enrichment, measured by the ratio between the
number of observed peaks and the number
expected by chance for each transcriptomic
region (from left to right: 5’UTR, near the start
codon, coding regions, introns, near the stop
codon and 3’UTR). The data displayed are from
one sample and are representative of the three
biopsies.
Next, taking advantage of the m6A sequencing in human BC biopsies, we assessed
whether our data from BC cell lines, cultured in vitro, constituted a good model for the
study of m6A in BC. Thus, we compared the m6A peaks from breast cancer cells lines
and human biopsies. In terms of transcripts, there was a significant overlap between the
two types of samples. For instance, SKBR3 shared 67% of its m6A targets with at least
one human breast biopsy. In comparison, the transcripts obtained by the same MeRIP-
seq method in our host laboratory in an unrelated model (i.e. ES cells) shared only 37%
of its m6A targets with the breast biopsies. While both overlaps were significant (p <
2.10-16), the breast samples clearly shared more similarity between themselves than with
an unrelated control. In addition, gene ontology analyses performed with IPA® revealed
that the BC cell lines and the human biopsies were both enriched in signaling pathways
such as “molecular mechanisms of cancer”, “Wnt signaling”, “glioblastoma” and “stem
cells”. Hence, cell lines and human tissues displayed changes of m6A in the same key
cancer pathways, and BC cell lines represent a relevant model for the study of m6A.
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In conclusion, our transcriptome-wide data presented a picture in which RNA
from BC conserved the known features of m6A (e.g. the region distribution and the
motifs) but was deregulated in many transcripts associated with major cancer pathways.
3.2.2. Deregulation of the m6A machinery in BC
Given our that many m6A changes occurred in breast cancer, we investigated
whether the enzymes related with its cycle were also commonly deregulated in BC. To
assess expression in breast tumors, we used publicly available RNA-seq data from The
Cancer Genome Atlas (TCGA) consortium (Koboldt et al. 2012) (Fig. 75A). The m6A
methyltransferases METTL3 and METTL14, as well as the ALKBH5 demethylase, showed
little change in expression. In contrast, the FTO demethylase appeared to be globally
downregulated in BC. We confirmed the downregulation of FTO in an in-house cohort
by RT-qPCR (p=0.002; Fig. 75B).
Fig. 75: Expression of m6A enzymes in BC. (A) Expression was assessed in breast
tissue by meta-analysis of public RNA-Seq data from the TCGA cohort (101 normal
breast versus 812 BC samples). (B) Validation of FTO expression in an in-house cohort
(9 normal breast versus 47 BC samples)
Given the downregulation of FTO in BC, we next investigated whether the global
amount of m6A was also affected. Thus, we quantified m6A by mass spectrometry, which
is considered the gold standard method for m6A quantification. This was done in
collaboration with the team of Pr. Bi Feng Yuan (Wuhan University, China). First, we
were able to analyze RNA from 6 pairs of matched breast tissues (i.e. tumor and normal
samples coming from the same patients), and we observed an increase in global m6A
levels in BC (p=0.02; Fig. 76A). Secondly, in our in-house BC cohort, we observed a
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negative correlation between FTO expression and m6A levels: tumors with low FTO
expression had significantly more m6A (p=0.002; Fig. 76B). Thus, loss of FTO, the m6A
demethylase, was associated with a global gain of m6A in BC.
Fig. 76: Quantification of m6A in BC by
mass spectrometry. (A) Relative amount
of m6A in paired matched samples (normal
breast and BC from the same patients)
(n=6). Lines connect corresponding pairs.
Red dots indicate the mean of each group.
(B) Quantification of m6A in BC in respect
with FTO expression (25 tumors with low
expression versus 15 tumors with high
expression).
Finally, we investigated the clinical relevance of FTO expression in BC.
Interestingly, decreased FTO expression was significantly associated with poor survival
in Kaplan-Meier analysis, both in terms of recurrence-free survival (RFS) and distant
metastasis free survival (DMFS) (Fig. 77). Survival analyses were performed with the
online Kaplan-Meier plotter tool (Szász et al. 2016).
In conclusion, loss of FTO is a common event in breast cancer and it is associated
with a global gain in m6A and poor prognosis.
Fig. 77: FTO expression and survival in BC. (A) Recurrence-free survival (RFS) and
(B) distant metastasis free survival (DMFS) both displayed significant association with
FTO expression in BC (low FTO expression: black line; high FTO expression: red line).
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3.2.3. Phenotypic effect of FTO in BC
Taking the previous findings into account, we next investigated whether FTO
dysregulation had an impact on BC phenotype. To address this matter, we produced a
stable knockdown of FTO in SKBR3 cells, which was verified by RT-qPCR and by
Western blot (Fig. 78A and 78B). The subsequent gain in m6A was measured by dot blot
(Fig. 78C).
Fig. 78: FTO-knockdown in BC. (A) Knockdown of FTO was performed in SKBR3 cells.
The downregulation was verified (A) at the mRNA level by RT-qPCR (n=3) and (B) at the
protein level by Western blot (representative of 3 independent experiments). (C) Serially
halved amounts (starting at 100 ng) of poly A RNA from control (shScramble) or FTO-
knockdown (shFTO) cells were dot blotted and detected with an anti-m6A antibody
(representative of 3 independent experiments).
Then, we evaluated the effects of FTO depletion, according to three classical
features of cancer cells: migration, invasion and stemness.
First, we used the xCELLigence system to assess the migratory properties of BC
cells. This technology allows real-time measurement of the passage of cells from an upper
chamber to a lower chamber, guided by a chemoattractant (i.e. serum). With this system,
we observed an increase in migration upon depletion of FTO (Fig. 79A). In order to verify
that this effect was not specific to SKBR3 cells, we also used another BC cell line with
migrating capacities: the BT20 cells. A similar increase in migration was observed in this
cell line upon depletion of FTO (Fig. 79B). Furthermore, we mimicked the loss of FTO
function by treating cells meclofenamic acid (MA), a known inhibitor of its catalytic
activity (Huang et al. 2015), to ensure that the effect was merely not an off-target effect
of RNA interference. Consistently with previous results, inhibition of FTO activity led to
increased migrating properties in SKBR3 cells (Fig. 79C). This result also suggested that
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the observed phenotype was linked to the enzyme’s m6A-demethylating activity. Finally,
in order to assess the invasive properties of the cells (i.e. the capacity to travel through a
matrix or membrane), we added a layer of Matrigel between the two chambers, before
performing the xCELLigence assay. In these conditions, we observed an increase in
invasion upon depletion of FTO in SKBR3 cells (Fig. 79D). Taken together, these data
indicated that loss of FTO could enhance the mobility properties of cancer cells, which is
a feature commonly associated with EMT, and thus cancer progression.
Fig. 79: FTO depletion
enhances the mobility of
cancer cells. Knockdown of
FTO promoted cell migration
in (A) SKBR3 cells and (B)
BT20 cells. (C) Treatment of
SKBR3 with meclofenamic
acid (MA) increased
migration, as compared to
control (CTL) cells. (D)
Knockdown of FTO
promoted cell invasion.
All experiments were
performed in triplicate, one
representative result is
shown.
Next, we performed with FTO-depleted cells a tumorsphere (or mammosphere)
assay, in which breast cancer cell were isolated at the single cell level and grew in a media
that did not allow attachment. The capacity of the cells to grow in “colonies” (i.e.
tumorspheres) in such conditions is considered as a reflection of their stemness-like
properties, in particular the self-renewal capability. Using this assay, we observed an
increase in both the number (Fig. 80A) and the size (Fig. 80B) of tumorspheres upon loss
of FTO. This result indicated that reduced FTO expression could support the stemness of
cancer cells, a feature associated high tumorigenicity.
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Fig. 80: FTO depletion enhances in
vitro tumorsphere formation. (A)
Tumorsphere assays were performed
with control (shScramble) and FTO-
knockdown (shFTO) cells (n=3, data
as mean ± standard deviation). (B)
Representative pictures of the
tumorsphere are shown.
Taking into account the various phenotypic assays, loss of FTO appeared to
support an aggressive phenotype in terms of migration, invasion and stemness in breast
cancers.
3.2.4. FTO regulates the Wnt/β-catenin pathway in BC
Given the impact of FTO expression over the phenotype of BC cells, we wanted
to uncover the molecular mechanisms underlying this effect and whether they relied on
its demethylase activity.
Based on our sequencing data (see 3.2.1 Transcriptome-wide m6A landscape in
breast cancer), we noticed that the Wnt/β-catenin pathway was often targeted by m6A,
both in BC cell lines and in human biopsies. Thus, we wondered whether dysregulations
of m6A, through loss of FTO, could influence this signaling pathway, which is known to
play a major oncogenic role in breast and several other cancers. The canonical Wnt
pathway is summarized at Fig. 81. Briefly, on a basal level, β-catenin is a transcription
factor that is targeted for degradation by phosphorylation mediated by a multiprotein
“destruction complex” containing, among others, the GSK3 kinase. Upon binding of Wnt
ligands to their Frizzled receptors at the cellular membrane, a transduction signal leads to
the inactivation of the destruction complex, notably through inhibitory phosphorylation
of GSK3. Escaping degradation, β-catenin can then be accumulated, migrate to the
nucleus and bind to DNA to influence the expression of many genes. In BC, aberrant
activation of this pathway causes increased EMT and increased stem cell population, and
it is associated with recurrence and metastases (Lamb et al. 2013). The transcripts that
bore m6A peaks in BC cell lines included Wnt ligands (e.g. WNT3, WNT4, WNT6,
WNT8B), receptors and co-receptors (e.g. FZD1, FZD2, FZD4, FZD5, FZD6, FZD7,
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FZD8, FZD9, LRP5, LRP6), members of the destruction complex (GSK3B, PPP2CB,
BTRC), and even β-catenin itself. Thus, all levels of the signaling pathways displayed
m6A peaks.
Fig. 81: Canonical Wnt pathway.
(Left) GSK3-mediated phosphorylation
and proteolyse of β-catenin. (Right)
Binding of Wnt ligand to its receptor
leads to GSK3 phosphorylation and
inactivation of the destruction
complex. Subsequent binding of β-
catenin to DNA influences gene
expression and favors an aggressive
phenotype in breast cancer.
Therefore, we next assessed whether the canonical Wnt pathway was affected by
FTO dysregulation by measuring levels of β-catenin. First, in our FTO-knockdown cells,
we observed increased amounts of β-catenin by Western blot (Fig. 82A). This result
suggested that loss of FTO might promote the Wnt pathway. We further confirmed the
role of FTO in Wnt/ β -catenin regulation by producing a model of inducible
overexpression for FTO. This was performed in SKBR3 cells with the T-Rex™ system,
in which expression of the target is triggered by adding tetracycline in the culture medium.
We used either the wildtype form of FTO, or a catalytically inactive mutant. While
overexpression of WT FTO led to reduced β-catenin, overexpression of the mutant did
not change β-catenin levels (Fig. 82B). This finding suggested that the enzymatic activity
of FTO was required to sustain the regulation of the Wnt/β-catenin pathway.
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Fig. 82: FTO regulates β-catenin in BC. (A) Knockdown of FTO in SKBR3 cells
increased β-catenin levels, as measured by Western blot (representative of 3 independent
experiments). (B). Overexpression of wildtype FTO, but not its catalytic mutant,
decreased β-catenin, as measured by Western blot. SKBR3 cells were stably transfected
with FTO (wildtype or mutant) in Trex™ vector and overexpression was induced by
tetracycline (+Tet) (representative of 3 independent experiments).
We further examined the upregulation of the Wnt/β-catenin pathway upon loss of
FTO in SKBR3 cells. First, β-catenin downstream targets c-MYC and SOX9 were found
increased by Western blot (Fig. 83A). Secondly, Flash reporter assay showed increased
sensibility to Wnt3a treatment (Fig. 83B). Briefly, this method consists in a luciferase
reporter measurement of β-catenin-mediated transcriptional activation. Moreover, we
also observed by Western blot reduced ZO-1 levels and increased FN1 levels, two
indicators of increased epithelial–mesenchymal transition (EMT), upon loss of FTO (Fig.
83C). This result was particularly interesting given that (i) the EMT process is known to
be supported by Wnt signaling, and (ii) this event is key in the formation of metastasis
and could be linked with the increased migration and invasion we previously observed
upon loss of FTO (Fig. 79).
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Fig. 83: Loss of FTO enhances β-catenin signaling. (A) Knockdown of FTO in SKBR3
cells increased β-catenin downstream targets c-MYC and SOX9, as measured by Western
blot (representative of 3 independent experiments). (B) SKBR3 cells were treated by
Wnt3a (100ng/ml) for 6h and increased activation of β-catenin was measured by the
Flash reporter assay upon depletion of FTO (n=2). (C) Loss of ZO-1 and gain of FN1,
two signs of EMT, were observed by Western blot upon depletion of FTO (representative
of 2 independent experiments).
Finally, we asked what the clinical relevance of FTO-mediated regulation of the
Wnt pathway could be. Interestingly, a range of inhibitors targeting specifically the Wnt
pathway have been developed in recent years and are currently being investigated as anti-
cancer agents in clinical trials, notably for the treatment of BC (Lu et al. 2016). Thus, we
used iCRT3, an inhibitor that can cause BC cell death by blocking Wnt signaling at the
level of the β-catenin transcriptional complex (Bilir et al. 2013). Interestingly, SKBR3
cells appeared less sensitive to the inhibitor upon loss of FTO (Fig. 84) as increased
concentrations of iCRT3 were required in order to kill cancer cells. Most likely, high
levels of the drug were needed to override the enhanced activation of the pathway in FTO-
knockdown cells. This result suggested that expression of FTO might influence the
patient response to Wnt inhibitors and that this gene could be a potential predictive
marker.
Fig. 84: Loss of FTO affects
response to Wnt inhibitor in BC.
Control (shScramble) and FTO-
knockdown (shFTO) SKBR3 cells
were treated with increasing
concentrations of Wnt inhibitor iCRT3
for 24h. Cell viability was measured as
the ratio between the number of living
cells in the treated and untreated
conditions (n=3, data are represented
as mean ± SD).
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Taken together, our data indicated that depletion of FTO in BC led to enhance
Wnt/β-catenin pathway, which was notably associated with enhanced EMT and resistance
to Wnt inhibitors. Thus, as suggested by our previous sequencing data, dysregulations of
m6A enzymes could affect key cancer pathways.
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3.3. Key findings
This study aimed to investigate the role of m6A and its machinery in breast cancer,
and we performed our research both in cultured cells and in human breast biopsy.
Importantly, we mapped for the first time the transcriptome-wide m6A profile in BC cells
lines as well as human biopsies. Although classical features of m6A distribution are
conserved in BC, we identified nearly 2000 regions differentially methylated between
non-cancerous and cancer breast cells. Then we highlighted that one m6A enzyme in
particular, FTO, was dysregulated in BC. Downregulation of this demethylase was
associated with higher global m6A levels and poor survival in patients. Therefore, we
explored the role of FTO in vitro and we observed that loss of FTO supported an
aggressive phenotype in SKBR3 BC cells, with enhanced migration and invasion
properties, as well as increased stemness. We also unraveled dysregulations of the Wnt
pathway – a major signaling pathway involved in cancer – occurred upon depletion of
FTO. This was associated with increased EMT (a feature associated notably with
migration and invasion) and reduced sensitivity to Wnt inhibitors.
Overall, and in line with recent studies, our findings brought further evidence of
the role of m6A in cancer regulation (Cui et al. 2017; Li et al. 2017; Zhang et al. 2017).
Hence, RNA modifications and their machinery appear to be a new level of dysregulation
that can support cancer development and progression.
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148
149
Discussion
Discussion
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151
1. Immune activation of NF-κB drives TET1
dysregulation in cancer
Malignant transformation and progression are complex processes in which several
layers of regulation are disrupted. In addition to genetic aberrations, epigenetic
mechanisms play a key role in tumorigenesis. Cancer cells undergo widespread changes
in histone and cytosine modification patterns, and DNA methylation aberrations are a
well-established hallmark of cancer. Global loss of 5mC, mainly affecting large, gene-
poor regions and repeated sequences, has been linked to DNA recombination and
genomic instability, while a gain in 5mC at gene promoters causes transcriptional
inactivation of tumor suppressor genes (TSG). For a long time, 5mC was considered a
relatively permanent mark, but this view changed abruptly. The discovery of TET-
mediated DNA hydroxymethylation as a mechanism of DNA demethylation, along with
the observation of disrupted hydroxymethylation patterns in cancer, sparked high hopes
of better understanding malignant processes.
This work of research addressed the essential question of TET and 5hmC
dysregulation in cancer, and more specifically in BC. Several previous studies have
provided insight into cancer-related 5hmC changes in various genomic regions. First
mappings uncovered wide redistribution of 5hmC in melanoma and pancreatic and liver
cancer (Lian et al. 2012; Bhattacharyya et al. 2013; Thomson et al. 2013). The existence
of vast 5hmC alterations was further confirmed in colorectal tumors, which show reduced
5hmC levels at repetitive elements and within genes, despite 5hmC distributions globally
similar to those of normal tissues (Uribe-Lewis et al. 2015). Furthermore, as shown in
leukemia, intergenic regions and enhancers in particular are also widely affected by
changes in 5hmC (Rampal et al. 2014; Rasmussen et al. 2015). In regards to previous
5hmC profiling data, our results confirmed and extended our understanding of cancer-
related 5hmC changes. Firstly, 5hmC had not been mapped in breast cancer tissues before.
In the light of the finding that 5hmC is highly tissue-specific in normal cells (Nestor et al.
2012), it is necessary to examine the diversity of 5hmC changes among tumor types.
Secondly, we observed a global anti-correlation between 5hmC and 5mC in breast cancer.
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Despite the established role of 5hmC in DNA demethylation, such a relationship was not
always straightforward in published studies. Research groups have in turn highlighted a
negative correlation (Figueroa et al. 2010; Lian et al. 2012; Yamazaki et al. 2015) or
failed to show this relation, particularly when a concomitant loss of 5mC and 5hmC was
observed at many loci (Ko et al. 2010; Uribe-Lewis et al. 2015). Extended maps may
further allow to better understand the relationship between 5hmC and 5mC in cancer.
Thirdly, we established a link between TET1 expression and 5hmC patterns in BLBC.
Our data are partly in contrast with published results, because all previous cancer-related
mappings of 5hmC were performed in a context of TET loss of function (either by
downregulation or mutation) and, overall, unraveled mainly 5hmC loss. However, we
observed that basal-like tumors with high levels of TET1 displayed almost exclusively
gains of 5hmC compared to normal breast tissues, whereas tumors with low levels of
TET1 displayed a majority of loss of 5hmC. Therefore, we provide evidence that 5hmC
dysregulations might be more diverse than formerly thought.
Our observations in regard to the 5hmC landscape in BC should however be
considered with caution, as only a limited number of samples were sequenced (4 tumors
and 4 matched normal tissues). This limits the strength of our study for two reasons.
Firstly, although the use of matched samples and pairwise analysis allows to control for
some interindividual variability, the low number of samples reduced the statistical power
of the analysis. Secondly, we could not measure the actual range of dhmRs in BC, as
observed in a heterogeneous population, with only two pairs of samples per group.
Therefore, mapping of 5hmC should be performed in a bigger cohort in order to reflect
the changes of 5hmC in BC, both extensively and accurately. In addition, we specifically
focused on the BLBC subtype. Mapping of 5hmC in other subtypes could provide
additional clues to the diversity of BC.
In regard to TET dysregulation, BLBC stand apart from most cancer types.
Decreased abundance of TET and/or 5hmC has been observed, both in solid and liquid
tumors, and multiple mechanisms interfering either directly or indirectly with TET
expression have been identified, notably involving the transcriptional repressor HMGA2
(Sun et al. 2013), various miRNAs (Song et al. 2013; Cheng et al. 2013; Chuang et al.
2015), or methylation of the TET1 or TET2 gene (Chim et al. 2010; Kim et al. 2011;
Cimmino et al. 2015). However, a study by Huang et al. has demonstrated that, in MLL-
rearranged leukemia, upregulation of TET1 expression by various MLL fusion proteins
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led to increased expression of oncogenic target genes (Huang et al. 2013). As this finding
contrasts starkly with the many reports of impaired TET expression in several
malignancies, it would seem that TET proteins can play diverse roles in tumorigenesis
and thus be differently dysregulated depending on the context. The wide range of TET1
expression observed in BLBC, including both increased and decreased TET1 levels,
brings evidence supporting the idea that TET dysregulation in cancer is more complex
than previously thought. It is worth noting that the BLBC subtype displays a particular
chromatin environment with lower 5mC levels at many loci than the other BC subtypes,
including TET1 promoter. This lack of methylation could support the higher expression
of TET1 in a subset of BLBC, however some tumors display low TET1 expression despite
the hypomethylated status of its promoter, as observed in the TCGA cohort (Koboldt et
al. 2012). Therefore, we took advantage of this particular subtype to investigate new
mechanisms of TET regulation.
As TET downregulation is observed in nearly all cancer types, we speculated that
alterations in signaling pathways frequently associated with tumors could play a role in
this regulation. Therefore, investigating the potential association between TET1
expression and major signaling pathways in BLBC, we established an unprecedented
association with immune pathways. In the BLBC subtype, high TET1 expression was
exclusively observed in tumors with low expression of immune genes and low infiltration
by major immune populations, including B lymphocytes, CD4+ and CD8+ T
lymphocytes and macrophages. This is of most interest, because the immune system has
emerged in recent years as a prominent feature of cancer. Many cancer types, including
BLBC, display a striking infiltration by immune populations. Cancer infiltration by
immune cells is, at least in part, due to secretion of recruiting factors by the cancer cells
themselves, and has major impacts in terms of disease progression and response to
treatment (Mantovani 2010). The immune system displays a dual action in cancer
(Lakshmi Narendra et al. 2013). On one hand, secretion of pro-inflammatory factors has
been shown to enhance cancer progression and to favor resistance to treatment. On the
other hand, tumor immune response, and in particular tumor-infiltrating lymphocytes
(TILs), are increasingly recognized to be associated with better clinical outcome in many
cancers (Galon et al. 2006; Oble et al. 2009; Melichar et al. 2014). In addition, the recent
emergence of immunotherapy (e.g. PD-L1 and PD-1 inhibitors) as promising tools to
prevent cancer from escaping destruction by the immune system has brought a new hope
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in terms of cancer treatment (Dany et al. 2016; McArthur 2016). Thus, the immune system
is a major component of the TME with important clinical relevance, in breast and other
cancers. Accordingly, we extended our findings to other cancer types, including
melanoma, thyroid and lung cancers, supporting the idea that immune regulation of TET1
occurs in most cancer types.
In recent years, epigenetics and immunity have been increasingly interwoven in
the context of cancer. DNA methylation, in particular, has been shown to be altered in
relation with the immune infiltration of the tumor (Dedeurwaerder & Fuks 2012). In this
study, however, we highlight a new dimension in the epigenetics-immunity connection,
i.e. the immunity-driven repression of TET1 in cancer cells. Thus far, TET enzymes have
only been involved in the regulation of immune cells themselves. In T regulatory (Treg)
cells, for instance, TETs promote FOXP3 expression and Treg-cell-associated immune
homeostasis (Yang et al. 2015). In myeloid cells, Tet2 has been shown to repress the
proinflammatory cytokine IL6, thus controlling inflammation (Zhang et al. 2015). Tet2
has also been found to promote the activation of cytokine genes in CD4+ T cells
(Ichiyama et al. 2015). Additionally, TET1 was reported as an epigenetic regulator
involved in Th2 differentiation (Yang et al. 2016). In contrast to previously published
literature, we demonstrate that the link between immune pathways and TETs extends
beyond the immune system itself. We highlight a new paradigm in which the immune
system can influence cancer cell epigenetics, specifically by NF-κB-dependent repression
of TET1. This is of great importance as epigenetic drugs have been shown to modulate
the anti-tumor immune response (Roulois et al. 2015; Chiappinelli et al. 2015) and
dissecting the epigenetic mechanisms underlying the cross-talk between the immune
system and cancer could help optimize therapeutic strategies (Jeschke et al. 2017).
In this study, we specifically linked TET1 repression with the immune regulator
NF-κB. In order to establish the involvement of this transcription factor in TET1
repression, we activated NF-κB through three different methods: overexpression of the
p65 subunit, LPS treatment and TNF treatment. We also blocked the activation of the
pathway with an inhibitor of the proteasome, MG-132, before TNF treatment to prevent
TET1 repression. However, it is worth mentioning that LPS and TNF influence several
pathways and that not all of their effects can be attributed to NF-κB activation. Likewise,
MG-132 is not a specific inhibitor of NF-κB, as it acts on the entire proteasome function.
A stronger evidence of the implication of NF-κB could be provided by using specific
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inhibitors. For instance, a dominant-negative mutant of IKK2 has been engineered and
can block the activation of the pathway (Mercurio 1997). This would also provide the
reverse experiment of the phenotype observed in IKMV mice, where NF-κB activation
was in fact performed through expression of a constitutively active mutant of IKK2 in the
mammary gland. Likewise, it is also possible to take advantage of non-degradable forms
of IκB, bearing mutations on the phosphorylation and ubiquitylation sites targeting the
protein to the proteasome, in order to prevent NF-κB activation (Gupta et al. 2010). Using
these controls could provide stronger evidence of the role of NF-κB in TET1 regulation.
NF-κB is a major transcription factor, known to be activated in many cancer types
(Ben-Neriah & Karin 2011). It is mostly considered as pro-tumorigenic, particularly when
its activation is associated with an inflammatory context, however NF-κB signaling can
also be a marker of the immune response targeting malignant cells (Chen et al. 2008;
Hoesel & Schmid 2013). In the context of TET1 regulation, we highlighted the role of
NF-κB member p65. In general, p65-p50 dimer is widely recognized to promote the
expression of many cytokines and chemokines, while the repressive activity is more often
attributed to p50-p50 homodimer. Nevertheless, a repressive effect of p65-p50
heterodimer has been previously reported as well. For instance, p65 can recruit DNMT1
leading to repression of the tumor suppressor gene BRMS1 by promoter hypermethylation
(Y. Liu et al. 2012). In another study, histone deacetylase HDAC1 and HDAC2, both
known as co-repressors of gene expression, were shown to interact with NF-κB p65 and
could repress NF-κB-regulated genes (Ashburner et al. 2001). Alternatively, p65-
mediated repression can also occur as a consequence of promoter occupancy and
subsequent steric hindrance. The exact molecular mechanisms involved in TET1
regulation remains to be uncovered.
Interestingly, gene regulation by NF-κB is another example of bidirectional cross-
talk between epigenetics and immune signaling (Vanden Berghe et al. 2006). Firstly, and
as mentioned above, epigenetic enzymes can act as co-effectors in the NF-κB
transcriptional complex. And while NF-κB-responsive elements in promoters are mostly
responsible for the recruitment of the NF-κB complex, the response patterns can vary in
terms of kinetics and quantity, depending on the context. Modulation of the NF-κB
response is, at least in part, achieved through epigenetic alteration of the chromatin. Both
DNA methylation and histone modifications have been widely involved in NF-κB
signaling (Zhou et al. 2013; De Andrés et al. 2013). Furthermore, epigenetic factors have
156
been linked to the regulation of NF-κB transcriptional activity through post-translational
modifications of its subunits. For instance, HDAC-mediated deacetylation of p65 is
critical for orchestrating the transcriptional program of NF-κB and can play a critical role
in cell differentiation (Chen et al. 2011). Thus, epigenetic factors are both downstream
effectors and upstream modulators of the NF-κB signaling pathway. Our study uncovered
a new dimension of this relationship. Our findings notably raise the question of the
potential role of TETs and 5hmC regulation in the modulation of NF-κB-mediated gene
regulation.
Fig. 85: Proposed model illustrating the
immune regulation of TET1 in cancer. (1) In
tumors with high immune infiltration and
expression of immune mediators, the canonical
NF-κB signaling pathway is activated in cancer
cells. (2) Nuclear translocation of p65 allows
p65-p50 NF-κB dimers to bind to TET1 gene
promoter. (3) Binding of NF-κB dimers to TET1
promoter leads to reduced expression of the gene.
Our work also opens new avenues in terms of perspectives. Firstly, the molecular
mechanisms involved in this regulation should be further examined. Notably, the
potential recruitment of co-repressor(s) by p65 could be demonstrated through
streptavidin-agarose assay followed by protein mass spectrometry. The modulation(s) of
the chromatin state of TET1 promoter upon NF-κB activation could also be studied by
ChIP-qPCR (e.g. targeting the main histone modifications or other features, depending
on the previously identified co-repressors). We should also keep in mind that different
mechanisms might be involved in different cancer types. Secondly, and importantly, in
light of the link between TET1 repression and immunity, as well as the prominent role of
immune infiltration in predicting clinical outcome, the relevance of TET1 expression in
cancer should be reconsidered. So far, loss of TET function has almost exclusively been
considered as a marker of bad prognosis, linked with their described tumor suppressive
role. However, our data demonstrate that this loss can also be associated with high
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immune infiltration, including of TILs, which is recognized as a marker of better clinical
outcome. Hence, further investigation should be completed in order to clarify this
apparent discrepancy. In that regard, it is important to recall that the role of TETs in
cancer might not be as one-dimensional as first thought, and that several studies have
brought an intriguing duality to light by displaying TET enzymes and 5hmC as potential
promoters of cancer (Huang et al. 2013; Ahsan et al. 2014; Navarro et al. 2014). It would
be particularly relevant and interesting to evaluate the effects of TET modulation (e.g. by
exogenous overexpression or by knockdown/knockout) in cancer cells in a context of
immune activation. For instance, would loss of TET function exacerbate the pro-
tumorigenic effect of inflammatory signaling or influence the recruitment of immune
cells to the TME? The potential impact of TET1 regulation on immune signaling remains
unknown.
In conclusion, our data assign a novel function to NF-κB, which is known to
orchestrate immune and inflammatory responses, as well as oncogenesis (Oeckinghaus &
Ghosh 2009; Ben-Neriah & Karin 2011). Although identified in BLBC, NF-κB-mediated
repression of TET1 appears to be a mechanism shared by many cancer types. Our findings
set the stage for future studies on TET dysregulation in cancer, in placing the immune
system under the spotlight and unraveling a new facet in the relationship between
immunity and cancer epigenetics.
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2. RNA hydroxymethylation: a new player in the
game
A new area of epigenetic research is on the rise: RNA epigenetics. This field has
increasingly intrigued scientists due to the numerous existing modifications identified in
RNA, as well as their involvement in the regulation of major biological processes, such
as stem cell pluripotency and differentiation (Geula et al. 2015; Zhang et al. 2016).
Chemical modifications play an important role in modifying and regulating the function
of RNA. Until recently, attention had been focused on tRNA modifications and their
impact on protein translation (Torres et al. 2014). However, other classes of RNA can
also be modified, and these modifications impact many aspects of RNA metabolism, aside
from translation (e.g. stability, splicing, or localization). Of particular interest, mRNA is
now acknowledged to also bear several chemical modifications, and this is important
since they constitute a regulatory layer positioned between DNA and proteins. Thus,
although our understanding of the post-transcriptional modifications that decorate RNA
is still in its infancy, RNA epigenetics shows much promise for the future.
In this context, we have decided to tackle the challenge of yet uncharacterized
RNA modifications. Specifically, we have conducted a pioneer study addressing cytosine
hydroxymethylation in RNA, using Drosophila melanogaster as a model. During the
preparation of our manuscript, two studies demonstrated the existence of 5hmrC in RNA
(L. Fu et al. 2014; Huber et al. 2015). The first report indicated that 5hmrC occurred in
mammalian RNA, though at much lower frequency than 5mrC (approximately in a
1:5000 ratio), according to mass spectrometry. Through in vitro assays and
overexpression of Tets in HEK293T cells, they also demonstrated that Tet enzymes could
oxidize 5mrC into 5hmrC. The second report described the existence of 5hmrC across all
three domains of life (i.e. archaea, bacteria and eukaryotes). They also showed that 5hmrC
was enriched in mammalian poly A RNA, with up to 10 times more 5hmrC than in total
RNA. Therefore, these two studies set the bases for the investigation of a new RNA
modification by demonstrating the existence of 5hmrC and suggesting that it might be
part of a dynamic regulatory mechanism. Yet, these findings relied solely on global
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quantification and did not address the distribution and function of 5hmrC. Therefore, our
work confirmed and extended prior observations by (i) providing the first transcriptome-
wide picture of 5hmrC distribution, (ii) assigning a novel function for 5hmrC in
translation regulation, and (iii) suggesting a central role for this RNA modification and
dTet in the Drosophila brain.
An important issue in the study of any new epigenetic modification is the
identification of its enzymatic machinery. Results from our study and from Fu et al.
indicated that Tet enzymes are responsible for RNA hydroxymethylation. However,
several pieces of evidence also suggested that other mechanisms might be implicated in
5hmrC formation. Firstly, Fu et al. observed that while 5hmrC levels are reduced in Tet-
null ES cells, appreciable levels of the modification remained detectable by mass
spectrometry (approximately 50% of wildtype ES cells). Similarly, in the Drosophila
brain, approximately 40% of 5hmrC remained detectable upon knockout of dTet (Fig.
67). Finally, Huber et al. found 5hmrC in the RNA of species that lack both DNA
hydroxymethylation and TET homologues in their genomes, such as C. elegans and A.
thaliana. Taken together, these results suggest that the formation of 5hmrC in RNA can
also occur through a non-TET mechanism. In this context, two hypotheses could be
considered: (i) oxidation of 5mrC into 5hmrC might be induced by cellular reactive
oxygen species in a non-specific manner, or (ii) other enzyme(s), yet unidentified, might
have the capacity to oxidize 5mrC, in a reaction similar to TET-mediated RNA
hydroxymethylation. While we cannot formally exclude the first option, it seems unlikely
that the remaining 5hmrC signal derives merely from random oxidation, since we were
able to detect hundreds of regions significantly enriched for 5hmrC after depletion of dTet
in S2 cells. This result suggests that 5hmrC distribution was not random even in dTet-
depleted cells. In regard to the second option, a search for a Tet-like enzyme in Drosophila
by BLAST (Basic Local Alignment Search Tool) analysis did not provide any obvious
candidate, thus the identity of the putative enzyme(s) remains unknown. However, we
also cannot entirely reject the possibility that another type of oxidase enzyme might be
involved.
Another issue raised by our results is the tissue-specificity of 5hmrC. In our study,
we observed that both 5hmrC and dTet were enriched in the central nervous system, as
compared to other tissues, such as salivary glands and ovaries (Fig 65B). Similarly, Fu et
al. measured 5hmrC in several mouse tissues and observed the highest levels in the heart
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and the lowest in the pancreas. Interestingly, and in contrast with results from the
Drosophila, mouse brain only displayed intermediary levels of 5hmrC. These results
suggest not only that the amount of 5hmrC differs between tissues, but also that the tissue
distribution might be species-dependent. Beyond global quantification of 5hmrC, we can
expect that this tissue-specificity would also extend to local enrichments. In line with this
idea, several other epigenetic modifications have been shown to display highly tissue-
specific distributions, including DNA methylation and hydroxymethylation (5mC,
5hmC), as well as RNA methylation (m6A) (Meyer et al. 2012; Jeschke et al. 2015;
Ponnaluri et al. 2017). Therefore, the diversity of 5hmrC distribution across different
tissues should be examined by transcriptome-wide sequencing in the years to come. In
that regard, a first clue was provided by the sequencing of 5hmrC we performed in breast
cell lines as a follow-up of our first study in Drosophila. While the majority of 5hmrC
was found in coding regions in S2 cells, the mark showed a preferential enrichment in
intronic regions in the human breast cells, thereby displaying some discrepancies in the
general distribution of 5hmrC between the two types of sample. This example illustrate
how profiling 5hmrC in different tissues and species would help us understand whether
5hmrC peaks are conserved or display evolutionary divergence.
While our research in Drosophila uncovered several interesting features of 5hmrC,
this work constituted the first fundamental study on this “new” mark, which raised even
more questions and possibilities in terms of perspectives. These are discussed in the next
paragraphs.
Firstly, we have discovered that 5hmrC can favor protein translation in mRNA.
However, we cannot exclude that this modification has other regulatory effects, and
further transcriptome-wide analysis should be performed to test this hypothesis. For
instance, coupling 5hmrC-mapping with paired-end RNA sequencing would allow to
investigate a potential role of the modification in RNA splicing regulation (Rossell et al.
2014). It is worth noting that the intronic enrichment of 5hmrC in breast cells raised the
possibility of a potential role in splicing regulation in these cells. Moreover, to assess the
effect of 5hmrC on RNA stability, the half-life of transcripts could be measured by
treating the cells with actinomycin D (which blocks transcription) for several hours before
performing RNA sequencing (Ayupe & Reis 2017). Another way to get an insight into
the functions of 5hmrC would be the identification of potential readers. This might be
performed by pulldown of protein extracts with biotinylated RNAs (in vitro transcribed
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and containing 5hmrC), followed by proteomic mass spectrometry. A similar method
(relying on DNA probes) was used for the identification of 5mC and 5hmC readers
(Spruijt et al. 2013). And, as for tissue distribution, one should keep in mind that distinct
readers might be identified in various tissues or species. Overall, these different
approaches would extend our understanding of the role of 5hmrC in RNA metabolism.
Secondly, the discovery of a new epigenetic modification naturally raises the
question of its potential involvement in mechanisms related to human health and disease.
Two biological contexts stand out in particular: neurogenesis and cancer.
The first arises from our observation that dTet-null fruit flies displayed impaired
brain development, as well as the implication of mammalian Tets in neuronal functions
(Guo et al. 2011; Xin et al. 2015; Hsieh et al. 2016). In mammals, the role of Tets in
neuronal development and homeostasis has so far been attributed to 5hmC and DNA
demethylation. However, in light of the discovery that RNA can also be
hydroxymethylated by Tets, this notion should be reevaluated. In Drosophila, many RNA
transcripts related to the nervous system were found hydroxymethylated. Should this
feature be conserved in mammals, it would suggest that 5hmrC might also be involved in
Tet-mediated regulation of neurons.
Likewise, the choice of cancer as a potential model to study 5hmrC comes from
the fact that Tets are widely dysregulated in cancers in mammals, as was discussed in a
previous chapter. This idea is also backed up by our 5hmrC sequencing data. In cell lines,
we have shown that 5hmrC displays a wide redistribution between non-cancerous breast
cells (MCF12) and BC cells (MDA-MB-231), with over 700 differentially
hydroxymethylated RNAs. These transcripts were associated with key BC genes, such as
BRCA2 and p53. Further analyses are required to confirm and extend these results;
however, our observations suggest a major dysregulation of 5hmrC in breast cancer.
However, these results are very preliminary data that should only be considered as a pilot
study, performed to evaluate the feasibility of studying 5hmrC in breast tissues. Although
we were able to measure and map 5hmrC in breast cells, the amount of RNA required for
such analysis (about 1mg) far exceeds what can be reasonably obtained from human
breast biopsies, and especially from normal tissues which only yields very limited
amounts of material. This constraint forced us to rely on breast cell lines at this stage, for
which sufficient amplification was possible. Furthermore, MCF12 cells are, admittedly,
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not a perfect control as the cells are immortalized and differ from actual normal breast
cells, notably in terms of karyotype and proliferation rate. However, they are non-
tumorigenic and were the best available control. In order to truly measure 5hmrC in BC
and normal breast cells, the technology of hMeRIP must be improved to allow the
detection of 5hmrC from smaller amounts of RNA. Nevertheless, should our results be
confirmed, gaining insight into the effects of 5hmrC on RNA metabolism (e.g. in terms
of splicing, stability, etc.) would also help understand better the consequences of such
dysregulations in regard to tumorigenesis. Hence, mapping 5hmrC could provide a new
dimension of cell regulation in contexts related to human health and disease.
A major challenge in the study of TET enzymes will be to discriminate the effects
linked to DNA and RNA hydroxymethylation. In terms of phenotype, it is likely that both
levels of regulation contribute to TET-mediated effects in a variety of cellular contexts.
There is no known specific modulator, and thus it would be almost impossible to
distinguish the effects of the marks in vivo. In contrast, in vitro experiments can at least
allow to study the functions of these marks separately. Briefly, 5hmC mostly acts at the
transcriptional level and its dysregulation is expected to affect the expression of RNA
transcripts. However, 5hmrC constitutes a post-transcriptional level of regulation. Our
results indicate that dysregulation of this mark could affect translation, and we also
suspect that it might affect RNA splicing and/or stability. We mentioned in a previous
paragraph the techniques that would allow the study of these potential functions. Hence,
while we cannot, as of today, isolate the roles of 5hmC and 5hmrC on a given phenotype,
we can at least explore the effects of these modifications through their functions and,
accordingly, speculate on related mechanisms. Furthermore, it is worth mentioning that
an RNA binding domain was recently identified in TET2 (He et al. 2016). Similar
domains can also be found in TET1 and TET3. It has not yet been shown that these
domains are required for RNA hydroxymethylation. But, should it be the case, deletion
of these domains could prevent RNA hydroxymethylation and thus provide a tool to study
5hmrC specifically. This concept is, admittedly, at the level of speculation, but it
constitutes an intriguing possibility.
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In conclusion, our work in Drosophila has yielded the following key findings: (i)
the first mapping of the hydroxymethylated transcriptome, (ii) a newly identified function
for 5hmrC, and (iii) a central role for this RNA modification and dTet in the Drosophila
brain. All in all, we expect this fundamental study to change the way we think about the
roles played by cytosine hydroxymethylation and the Tet proteins. Our findings in breast
cells also open new research prospects for the study of health and disease by drawing
attention to an emerging realm of biological regulation: epitranscriptomics.
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3. Dysregulations of m6A and its machinery
support breast cancer
Discovered in the 1970s (Desrosiers et al. 1974), m6A is the most abundant
modification of mRNAs, detected in a wide range of species, ranging from bacteria to
eukaryotes (Deng et al. 2015; Wu et al. 2016). Nevertheless, the study of m6A
modifications had long been stalled, owing to the lack of understanding of its machinery
and the short half-life of most RNAs. The situation changed recently, however, with the
identification of m6A methyltransferases and demethylases, as well as the development
of new technologies, including high-throughput sequencing of the methylated
transcriptome (Dominissini et al. 2012; Meyer et al. 2012; Maity & Das 2016). In
particular, the recognition of m6A as being part of a dynamic process has reignited the
interest of researcher in RNA epigenetics and has shaken many concepts of cytogenetics
related to RNA metabolism.
The present work of research addressed the topic of dysregulations of m6A and
its machinery in cancer, and more specifically in BC. In that regard, three recent studies
have provided insight into the landscape of cancer-related m6A changes and the impact
of associated enzymes on tumorigenesis. In 2016, Li et al. reported that FTO, an m6A
demethylase, was highly expressed in MLL-rearranged acute myeloid leukemia (AML),
and that the enzyme functioned as an oncogene promoting leukemogenesis. This pro-
tumor function was notably mediated by m6A-dependent regulation of ASB2 and RARA
transcripts, two key regulators of hematopoiesis (Li et al. 2016). Likewise, two groups
recently assigned oncogenic functions to the m6A demethylases, FTO and ALKBH5, in
glioblastoma. Both studies showed that m6A regulated the self-renewal, proliferation and
tumorigenesis of glioblastoma stem cells by controlling the expression of key cancer
factors, such as ADAM19 and FOXM1 (Cui et al. 2017; Zhang et al. 2017). In contrast
with previous studies, our results assigned a tumor suppressive function to FTO, as we
demonstrated that FTO was depleted in BC, which was associated with poor survival,
increased global m6A levels and an aggressive phenotype in vitro. Hence, our findings
bring a novel duality to light, as m6A demethylases appears to display both oncogenic
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and anti-tumor activity, depending on the cancer type and the cellular context. Such
dichotomy is not unheard of for epigenetic enzymes. For instance, EZH2, a member of
the Polycomb repressive complex 2 promoting H3K27 methylation, has in turn showed
pro- and anti-tumor functions in various cancer contexts (Herviou et al. 2016). We also
discussed TET enzymes in a previous chapter, whose tumor suppressive role has been put
into question by several studies (Huang et al. 2013; Ahsan et al. 2014; Navarro et al.
2014). These examples, as well as our findings about FTO in mammary tumors, illustrate
that the effects we attribute to enzymes should always be considered within a biological
context.
It is worth mentioning that, on a global level, changes in m6A upon modulation
of its related enzymes can be modest in terms of fold-change, despite substantial
phenotypical effects. For instance, m6A levels increased approximately by 30% upon
ALKBH5-knockdown in glioblastoma cells (Zhang et al. 2017). In breast cells, we
observed a similar increase of m6A upon FTO-knockdown by dot blot analysis. In line
with these findings, studies have previously reported in various cell types that modulation
(either knockdown or overexpression) of m6A methyltransferases or demethylases could
lead to changes in global m6A levels ranging from 10% to 40% approximately, depending
on the targeted enzyme and the cell type (Jia et al. 2011; Liu et al. 2014). In comparison,
overexpression of TET1 alone increased 5hmC levels by 10-fold in DNA (Tahiliani et al.
2009). Thus, changes of m6A in RNA, following modulation of related enzymes, can be
relatively subtle compared to other epigenetic marks; nevertheless they are associated
with major phenotypic changes in cancer. It is possible that m6A dysregulation in few
key transcripts would be sufficient to carry out the observed effects.
In regard to the transcriptome-wide landscape of m6A, the three previously
mentioned studies (Li et al. 2016; Cui et al. 2017; Zhang et al. 2017) found the classical
known features of m6A to be conserved in cancer, such as enrichment near the stop codon
and the 3’UTR and expected binding motifs (G. Luo et al. 2014; Geula et al. 2015). We
observed similar features in breast cell lines, suggesting that the general topography of
m6A distribution is robust in cancer. Yet, in comparison to these other studies, our
profiling of m6A stands out for two reasons. Firstly, we started our investigation by
comparing cancer cells to their non-malignant counterpart. This is the first time such a
comparison is drawn, since all previous differential m6A analyses in cancer instead
involved the direct modulation of the m6A machinery in a cancerous cell line. In contrast,
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we took advantage of the MCF12 cell line, which is a model for non-cancerous breast
(Paine et al. 1992), in order to draw a picture of transcriptome-wide changes of m6A in
BC. As previously mentioned, the MCF12 cells are immortalized and differ from true
normal breast cells. Similarly to our study of 5hmrC, the decision of using this cell line
was justified by the amount of RNA material currently required for MeRIP-seq (about
150µg), which exceeds what can be reasonably obtained from reduction mammoplasty.
Hence, our analyses should be considered with caution, and mostly as a first approach of
possible m6A dysregulations in breast cancer. Mapping of m6A in genuine normal breast
cells will require an improvement of the technology allowing lesser amount of starting
material.
The second characteristic that sets our study apart is our sequencing of m6A in
human breast tissues, i.e. obtained from patient biopsies rather than cultured cells. As
mentioned in the previous paragraph, sequencing of human tissues is not trivial due to the
amount of material required for m6A mapping. In fact, the vast majority of published
m6A profiles in mammals are from cultured cells (e.g. ES cells or cancer cell lines such
as HeLa or HEK293T), and the rare sequencing experiments performed on RNA extracted
from tissues are all from mice (Meyer et al. 2012; Dominissini et al. 2012; Batista et al.
2014; Zhao et al. 2014; Geula et al. 2015). Hence, our sequencing of breast tumors was,
to our knowledge, the first mapping of m6A in any human tissue. These data allowed us
to assess the relevance of cultured cells as a model for the study of m6A, and we found
that BC cell lines appeared to conserve the main features of human breast tumors, both in
terms of m6A-targeted transcripts and signaling pathways. For all the above-mentioned
reasons, our study presents strong evidence to support the notion that m6A represents a
new layer of dysregulation in BC. Nevertheless, the true validation of our hypothesis
should come from the comparison between BC tissues and normal breast tissue. As
mentioned previously, we could not provide the latter for technical reasons. We expect
however that the MeRIP-seq technologies will improve in the coming years and render
possible the mapping of m6A from human biopsies.
It is important to note that identifying cancer-related m6A dysregulations can have
clinical implications. For instance, Cui et al. demonstrated that in glioblastoma – where
m6A demethylases displayed oncogenic functions – an FTO inhibitor suppressed cancer
progression and prolonged lifespan of xenografted animals, suggesting that targeting the
m6A machinery is a promising therapeutic tool (Cui et al. 2017). The use of such inhibitor
168
would most likely not be beneficial in BC however, as FTO appeared to carry out anti-
tumor activity instead. Nevertheless, we linked in vitro downregulation of FTO with
reduced sensitivity to iCRT3, a Wnt inhibitor, in BC cells. Furthermore, several key
cancer pathways showed differential RNA methylation between non-cancerous and
cancer cell lines, including Wnt and stemness signaling. Therefore, while FTO might not
be a relevant therapeutic target in breast, dysregulations of FTO and m6A might bear
great potential as future prognostic and/or predictive biomarkers.
Interestingly, FTO had been suspected to play a role in BC long before its
demethylase activity was discovered, as genetic variants had been associated with
increased risk of BC (Kaklamani et al. 2011). Importantly, this observation was originally
thought to be linked with obesity. Indeed, in genome wide association studies (GWAS),
single nucleotide polymorphisms (SNPs) in intron 1 of FTO (namely, the Fat Mass and
Obesity-associated protein) had been connected to obesity and type 2 diabetes (Grant et
al. 2008; Thorleifsson et al. 2009; Cho et al. 2009). Studies in mice have since suggested
that FTO was involved in adipogenesis and weight gain regulation (Church et al. 2010;
Farooqi 2011; Merkestein et al. 2015). This notion was confirmed and extended in a study
by Zhao et al., who demonstrated that m6A-mediated regulation of mRNAs by FTO was
involved in adipogenesis (Zhao et al. 2014). Therefore, researchers assumed that the role
of FTO in BC was mostly owed to obesity and dysregulations in adipocyte functions, as
it is known that obesity causes adipocyte hypertrophy in breast, which is in turn associated
with increased release of pro-tumor inflammatory cytokines and adipokines (Monteiro &
Azevedo 2010). Yet, our data partly contradict this hypothesis. Notably, depletion of FTO
in in vitro cultured breast cells enhanced an aggressive cancer phenotype (i.e. migration,
invasion and stemness of BC cells), which supports the idea of an intrinsic, or cell-
autonomous, mechanism – that is, independently of effects mediated by adipocyte
hypertrophy in breast. Thus, although the influence of FTO on adipocyte homeostasis is
undeniable, downregulation of the enzyme in breast cells is sufficient to promote BC.
Whether FTO-mediated changes in adipocytes has an additional causal effect on BC or is
merely a confounding factor remains to be determined.
169
Fig. 86: FTO, breast cancer and
adipocytes. Our data indicate that
FTO dysregulations intrinsically
affects the cancer phenotype of
breast cells. It is also well-
established that FTO regulates
adipocytes. And while it has been
suspected that the latter might also
influence BC development, this
hypothesis has not been proven yet.
While our research uncovered a new level of dysregulation in breast cancer
through m6A and assigned a tumor suppressive role to FTO, several major points have
yet to be addressed as perspectives.
Firstly, we have demonstrated that loss of FTO affects BC cells in vitro, but these
findings should be extended to in vivo experimentation. As mentioned previously, in
tissues, tumor cells do not develop alone – they constantly interact with their tumor
environment (TME), and the effects of these interactions cannot be properly characterized
through in vitro models. Therefore, we plan to assess the effects of FTO downregulation
in vivo in a xenograft model. Similar experiments were performed, for instance, by Cui
et al. to demonstrate the oncogenic role of FTO in glioblastoma (Cui et al. 2017). To
further investigate the role of FTO in BC, patient-derived xenograft (PDX) experiments
could also be conducted. Interestingly, PDX-derived tumors have been shown to display
a high degree of concordance with the original human tumor, because both the supporting
stroma and the cancer cells are maintained, contrary to cultured cell lines (Isella et al.
2015).
Secondly, and importantly, we have yet to fully characterize the molecular
mechanisms supporting the enhanced aggressiveness observed upon knockdown of FTO
in BC. So far, we have indications that this phenotype could be related to Wnt/β-catenin
pathway dysregulations and that it might be m6A-dependent. In order to validate our
hypothesis, the exact transcripts that are regulated by FTO through m6A changes must be
identified. Afterwards, we must also determine how they are related to the phenotype in
terms of mechanisms. Sequencing of m6A in FTO-knockdown cells would determine
which transcripts are altered. Changes in m6A should also be confirmed by m6A-qPCR,
as sequencing methods can present biases in terms of quantification. Further sequencing
170
experiments can be performed in parallel to investigate the effects of m6A modulation on
RNA metabolism at the global level (e.g. stability, splicing, or translation – see
perspectives related to the study of 5hmrC for further explanation). Finally, once key
m6A targets are identified, their implication in the phenotype could be demonstrated
through rescue by overexpression or downregulation of said targets. For instance, if
transcripts of the “β-catenin destruction complex” (such as GSK3B or Axin2) display
changes in m6A, we would first explore the consequences for these targets in term of their
stability, splicing, and translation. Then we would mimic m6A-mediated regulation by
either downregulating or overexpressing the targets (depending on the effects observed)
and assess whether the β-catenin levels and the cancer phenotype (in terms of migration,
invasion, and tumorsphere formation) are affected as in the FTO-knockdown cells. These
experiments are needed to demonstrate that m6A regulation is truly responsible for the
observed phenotype, as well as to identify the underlying mechanisms. They would also
allow us to refine our model of the effects of FTO downregulation in breast cancer.
Fig. 87: Proposed model illustrating effects of FTO depletion in BC. Reduced FTO
expression in breast cancer cells is associated with a global increase in m6A levels and
changes in RNA regulation. These changes lead to enhanced activation of the Wnt/β-
catenin pathway, which supports an aggressive phenotype in BC, as observed in vitro for
migration, invasion and tumorsphere formation.
In conclusion, our study of m6A dysregulations in breast cancer has yielded the
following key results: (i) a transcriptome-wide portrait of cancer-related changes of m6A
in breast cells; (ii) the discovery of a novel anti-tumor function for FTO; and (iii) a
connection between FTO and a major cancer pathway, i.e. the Wnt/β-catenin signaling
pathway. Our findings support the notion that RNA epigenetics constitutes a new level of
molecular dysregulation in cancer, as suggested by recent studies (Li et al. 2016; Cui et
al. 2017; Zhang et al. 2017), while also bringing to light a duality in the role of the m6A
machinery.
171
4. Concluding remarks
Throughout this PhD thesis, we had the opportunity to explore three epigenetic
modifications – and their related enzymes – that have been either identified or
characterized within the past decade: DNA hydroxymethylation (5hmC), RNA
hydroxymethylation (5hmrC) and RNA methylation (m6A). The field of epigenetics is a
domain in constant evolution, and, taking into account the growth of the known epigenetic
repertoire, the scientific community can no longer be satisfied by simply looking at the
“classical” epigenetic modifications (i.e. DNA methylation and histone modifications) as
a reflection of the epigenome.
Our research work was mainly focused on breast cancer, a disease that remains
the most frequent malignancy in women. A legitimate question would be the clinical
relevance of investigating all these “new” epigenetic modifications in cancer.
Considering the coming of age of the next-generation sequencing methods, both the
genome and the transcriptome of a tumor can now be profiled in a very limited amount
of time. One could thus doubt the interest of studying such epigenetic modifications, when
genetic and transcriptomic aberrations can be easily examined. In the last chapter of our
discussion, we wish to take a step back from our own research and consider the global
picture of current oncology field. Because the answer to the question of the clinical
relevance of epigenetics is both simple and extraordinary complex: profiling the genome
and the transcriptome of malignant cells, while certainly beneficial, is not a magic bullet
for cancer management.
Let us begin with an optimistic note: cancer treatment, including for breast cancer,
has improved tremendously over the past 40 years, and survival rates have never been as
high as they are nowadays (WHO 2012). This progress results mostly from the
combination of two occurrences: (i) the growth and improvement of the therapeutic
arsenal, and (ii) the use of relevant biomarkers to guide the choice of therapy. With the
side by side evolution of these two aspects of cancer management, we are gradually
coming to an age of personal medicine where each patient and each tumor is considered
as unique. Hence, in order to continue to improve patient survival, the major challenge of
172
modern oncology lies in finding the optimal treatment for each patient, as response to
treatment varies greatly between patients with the same cancer.
Variation in cancer response can be explained by differences in the molecular
profile of tumors. Already, profiling DNA mutations and the RNA transcriptome are
becoming accepted in oncology as a means to help guide clinicians and sequencing
protocols are being adapted to standard clinical practice (Gagan & Van Allen 2015).
However, cells are widely dysregulated in tumors, and multiple levels of regulation have
been implicated: genetic mutations and genomic defects, changes in gene expression, loss
of heterochromatin, abnormal DNA methylation, changes in histone modifications,
altered RNA interference, etc. To complicate matters even more, malignant cells can alter
their TME and these dysregulations can evolve over time. Thus, cancer is an extremely
complex disease, which explains the difficulty of finding a cure: whenever a treatment
targets a certain alteration, another level of dysregulation can take over and present a path
towards resistance. Accordingly, combination of therapies targeting different levels of
dysregulation can improve the efficiency of cancer treatment and reduce the risk of
relapse. For instance, demethylating agents of the DNA and HDAC inhibitors are known
to enhance the effects of chemotherapy (Dawson & Kouzarides 2012). In this context, it
appears obvious that restricting ourselves to profiling only the “classical” epigenetic
modifications limits our own understanding of the tumor biology.
Ideally, each tumor should be profiled at multiple molecular levels, and the
integration of all this information should determine the choice of the optimal therapeutic
strategy. We are not at this point yet, both because of our incomplete understanding of
tumor complexity, and because of technologic and economic limitations. Biomedical
sciences are making constant progresses though, and the clinical field would undeniably
benefit from integrating concepts developed by fundamental and translational research.
173
Fig. 88: The molecular portrait of
tumors is a multidimensional picture.
Theoretically, molecular profiling should
take into account all levels of
dysregulation in a tumor. In that regard,
the recently discovered epigenetic
modifications offers a growing platform
for the study of cancer-related
mechanisms.
In light of this, we believe that studying epigenetic modifications is highly
relevant to the improvement of our understanding of a multidimensional cancer biology.
Of particular interest, the growing epigenetic repertoire offers a wide variety of
modifications with various features and benefits that can be complementary in terms of
clinical use. For instance, DNA modifications such as 5mC and 5hmC are extremely
specific of cell types and their distribution can be used as a potential clinical tool for TME
characterization and cell typing within tumors (Jeschke et al. 2015). One advantage of
DNA modifications, compared to transcriptomics or proteomics, is the linearity of the
relationship between the number of cells and the mark (given the two DNA copies that
can be modified per cell). Recently, our host laboratory demonstrated the value of such
approach with a 5mC-based immune response signature which improved patient
diagnosis and predicted the response to chemotherapy in multiple cancers (Jeschke et al.
2017). Furthermore, DNA marks are stable and are not degraded when released in the
blood stream by cancer cells, which makes them useable as biomarkers in non-invasive
liquid biopsies. By contrast, RNA modifications present the advantage of being a level of
dysregulation which is more downstream than genetics and transcriptomics, thus they can
capture complementary information. Additionally, the recent development of RNA-based
therapeutics has highlighted the clinical potential of RNA modifications. Chemical
modifications of injected RNA can, for instance, impart resistance to degradation or block
translation. The degree to which RNA modifications affect the potency of the transcript
depends both on the type of RNA and its mechanism of action (Kaczmarek et al. 2017).
Indeed, heavy chemical modification is now a standard for in vivo siRNA use, whereas
174
mRNAs, which have to be translated by ribosomes, are more sensitive and studies have
suggested that they should only carry naturally-occurring modifications, such as 5mrC or
m6A (Behlke 2008; Kauffman et al. 2016). In line with this, the first RNA-based drugs
have recently gained FDA approval and are being tested in clinical trials, illustrating that
this research is beginning to bear fruit.
In conclusion, cancer management is, without a doubt, a multidisciplinary task.
Currently, the therapeutic decision is based on several criteria that include both clinical
features and the molecular profiling of the tumor. It has become clear in recent years,
however, that molecular profiling has not yet reached its full potential. In particular,
epigenetics is an important contributor of the molecular profile, and its expanding
repertoire bears a great potential in terms of cancer therapies and biomarkers that will
help chart a course towards future personal medicine.
175
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Appendix
Appendix
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The following documents are provided in appendix:
• Manuscript I: Immunity drives TET1 regulation in cancer through NF-κB
(research article submitted for publication).
• Manuscript II: Transcriptome-wide distribution and function of RNA
hydroxymethylcytosine (research article published in Science in 2016).
• Manuscript III: DNA methylome profiling beyond promoters – taking an
epigenetic snapshot of the breast tumor microenvironment (review published in
the FEBS Journal in 2015).
• Manuscript IV: Portraits of TET-mediated DNA hydroxymethylation in cancer
(review published in Current Opinion in Genetics & Development in 2016)
• Additional methods (related to unpublished results presented in this thesis).
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