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Introduction to Bioinformatics For the Life Sciences
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Inleiding tot de bio-informatica en computationele biologie
Lab for Bioinformatics and computational genomics
10 “genome hackers” mostly engineers (statistics)
42 scientiststechnicians, geneticists, clinicians
>100 people hardware engineers,
mathematicians, molecular biologists
What is Bioinformatics ?
• Application of information technology to the storage, management and analysis of biological information (Facilitated by the use of computers)– Sequence analysis?– Molecular modeling (HTX) ?– Phylogeny/evolution?– Ecology and population studies?– Medical informatics?– Image Analysis ?– Statistics ? AI ?– Sterkstroom of zwakstroom ?
• Medicine (Pharma)– Genome analysis allows the targeting of genetic
diseases– The effect of a disease or of a therapeutic on RNA
and protein levels can be elucidated– Knowledge of protein structure facilitates drug
design– Understanding of genomic variation allows the
tailoring of medical treatment to the individual’s genetic make-up
• The same techniques can be applied to crop (Agro) and livestock improvement (Animal Health)
Promises of genomics and bioinformatics
Math
Informatics
Bioinformatics, a life science discipline …
(Molecular)Biology
Math
Informatics
Bioinformatics, a life science discipline …
Theoretical Biology
Computational Biology
(Molecular)Biology
Computer Science
Math
Informatics
Bioinformatics, a life science discipline …
Theoretical Biology
Computational Biology
(Molecular)Biology
Computer Science
Bioinformatics
Math
Informatics
Bioinformatics, a life science discipline … management of expectations
Theoretical Biology
Computational Biology
(Molecular)Biology
Computer Science
Bioinformatics
Interface Design
AI, Image Analysisstructure prediction (HTX)
Sequence Analysis
Expert Annotation
NPDatamining
Math
Informatics
Bioinformatics, a life science discipline … management of expectations
Theoretical Biology
Computational Biology
(Molecular)Biology
Computer Science
BioinformaticsDiscovery Informatics – Computational Genomics
Interface Design
AI, Image Analysisstructure prediction (HTX)
Sequence Analysis
Expert Annotation
NPDatamining
Time (years)
• Timelin: Magaret Dayhoff …
Happy Birthday …
PCR + dye termination
Suddenly, a flash of insight caused him to pull the car off the road and stop. He awakened his friend dozing in the passenger seat and excitedly explained to her that he had hit upon a solution - not to his original problem, but to one of even greater significance. Kary Mullis had just conceived of a simple method for producing virtually unlimited copies of a specific DNA sequence in a test tube - the polymerase chain reaction (PCR)
naturetheHumangenome
Setting the stage …
Biological Research
Adapted from John McPherson, OICRAdapted from John McPherson, OICR
And this is just the beginning ….
Next Generation Sequencing is here
One additional insight ...
Read Length is Not As Important For Resequencing
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
8 10 12 14 16 18 20
Length of K-mer Reads (bp)
% o
f P
aire
d K
-mer
s w
ith
Un
iqu
ely
Ass
ign
able
Lo
cati
on
E.COLI
HUMAN
Jay Shendure
ABI SOLID
Paired End Reads are Important!
Repetitive DNAUnique DNA
Single read maps to multiple positions
Paired read maps uniquely
Read 1 Read 2
Known Distance
Single Molecule Sequencing
Helicos Biosciences Corp.
Microscope slide
Single DNA molecule
dNTP-Cy3
* * *
*
primer
Super-cooledTIRF microscope
Adapted from: Barak Cohen, Washington University, Bio5488 http://tinyurl.com/6zttuq http://tinyurl.com/6k26nh
Complete genomics
Next next generation sequencing
Third generation sequencing
Now sequencing
Pacific Biosciences: A Third Generation Sequencing Technology
Eid et al 2008
Nanopore Sequencing
Ultra-low-cost SINGLE molecule sequencing
Genome Size
DOGS: Database Of Genome Sizes
E. coli = 4.2 x 106
Yeast = 18 x 106
Arabidopsis = 80 x 106
C.elegans = 100 x 106
Drosophila = 180 x 106
Human/Rat/Mouse = 3000 x 106
Lily = 300 000 x 106
With ... : 99.9 %To primates: 99%
Anno 2012
Anno 2012
IdentityThe extent to which two (nucleotide or amino acid) sequences are invariant.
HomologySimilarity attributed to descent from a common ancestor.
Definitions
RBP: 26 RVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWD- 84 + K ++ + + GTW++MA+ L + A V T + +L+ W+ glycodelin: 23 QTKQDLELPKLAGTWHSMAMA-TNNISLMATLKAPLRVHITSLLPTPEDNLEIVLHRWEN 81
Orthologous Homologous sequences in different species that arose from a common ancestral gene during speciation; may or may not be responsible for a similar function.
Paralogous Homologous sequences within a single species that arose by gene duplication.
Definitions
speciation
duplication
• Simple identity, which scores only identical amino acids as a match.
• Genetic code changes, which scores the minimum number of nucieotide changes to change a codon for one amino acid into a codon for the other.
• Chemical similarity of amino acid side chains, which scores as a match two amino acids which have a similar side chain, such as hydrophobic, charged and polar amino acid groups.
• The Dayhoff percent accepted mutation (PAM) family of matrices, which scores amino acid pairs on the basis of the expected frequency of substitution of one amino acid for the other during protein evolution.
• The blocks substitution matrix (BLOSUM) amino acid substitution tables, which scores amino acid pairs based on the frequency of amino acid substitutions in aligned sequence motifs called blocks which are found in protein families
Overview
BLOSUM (BLOck – SUM) scoring
DDNAAVDNAVDDNNVAVV
Block = ungapped alignentEg. Amino Acids D N V A
a b c d e f1
2
3
S = 3 sequencesW = 6 aaN= (W*S*(S-1))/2 = 18 pairs
A. Observed pairs
DDNAAVDNAVDDNNVAVV
a b c d e f1
2
3
D N A V
D NAV
1 413
111
14
1
f fij
D N A V
D NAV
.056
.222
.056
.167
.056.056.056
.056
.222
.056
gij
/18
Relative frequency table
Probability of obtaining a pair if randomly choosing pairs from block
AB. Expected pairs
DDDDDNNNNAAAAVVVVV
DDNAAVDNAVDDNNVAVV
Pi
5/184/184/185/18
P{Draw DN pair}= P{Draw D, then N or Draw M, then D}P{Draw DN pair}= PDPN + PNPD = 2 * (5/18)*(4/18) = .123
D N A V
D NAV
.077
.123
.154
.123
.049.123.099
.049
.123
.049
eijRandom rel. frequency table
Probability of obtaining a pair of each amino acid drawn independently from block
C. Summary (A/B)
sij = log2 gij/eij
(sij) is basic BLOSUM score matrix
Notes:• Observed pairs in blocks contain information about relationships at all levels of evolutionary distance simultaneously (Cf: Dayhoffs’s close relationships)• Actual algorithm generates observed + expected pair distributions by accumalution over a set of approx. 2000 ungapped blocks of varrying with (w) + depth (s)
• blosum30,35,40,45,50,55,60,62,65,70,75,80,85,90• transition frequencies observed directly by identifying
blocks that are at least – 45% identical (BLOSUM 45) – 50% identical (BLOSUM 50) – 62% identical (BLOSUM 62) etc.
• No extrapolation made
• High blosum - closely related sequences• Low blosum - distant sequences • blosum45 pam250• blosum62 pam160 • blosum62 is the most popular matrix
The BLOSUM Series
Overview
• Church of the Flying Spaghetti Monster
• http://www.venganza.org/about/open-letter
– Henikoff and Henikoff have compared the BLOSUM matrices to PAM by evaluating how effectively the matrices can detect known members of a protein family from a database when searching with the ungapped local alignment program BLAST. They conclude that overall the BLOSUM 62 matrix is the most effective.
• However, all the substitution matrices investigated perform better than BLOSUM 62 for a proportion of the families. This suggests that no single matrix is the complete answer for all sequence comparisons.
• It is probably best to compliment the BLOSUM 62 matrix with comparisons using 250 PAMS, and Overington structurally derived matrices.
– It seems likely that as more protein three dimensional structures are determined, substitution tables derived from structure comparison will give the most reliable data.
Overview
Rat versus mouse RBP
Rat versus bacteriallipocalin
• Exhaustive …– All combinations:
• Algorithm – Dynamic programming (much faster)
• Heuristics– Needleman – Wunsh for global
alignments(Journal of Molecular Biology, 1970)
– Later adapated by Smith-Waterman for local alignment
Alignments
A metric …
GACGGATTAG, GATCGGAATAG
GA-CGGATTAGGATCGGAATAG
+1 (a match), -1 (a mismatch),-2 (gap)
9*1 + 1*(-1)+1*(-2) = 6
Needleman-Wunsch-edu.pl
The Score Matrix----------------
Seq1(j)1 2 3 4 5 6 7 8 9 10Seq2 * C K H V F C R V C I(i) * 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -101 C -1 1 0 -1 -2 -3 -4 -5 -6 -7 -82 K -2 0 2 1 0 -1 -2 -3 -4 -5 -63 K -3 -1 1 1 0 -1 -2 -3 -4 -5 -64 C -4 -2 0 0 0 -1 0 -1 -2 -3 -45 F -5 -3 -1 -1 -1 1 0 -1 -2 -3 -46 C -6 -4 -2 -2 -2 0 2 1 0 -1 -27 K -7 -5 -3 -3 -3 -1 1 1 0 -1 -28 C -8 -6 -4 -4 -4 -2 0 0 0 1 09 V -9 -7 -5 -5 -3 -3 -1 -1 1 0 0
Needleman-Wunsch-edu.pl
The Score Matrix----------------
Seq1(j)1 2 3 4 5 6 7 8 9 10Seq2 * C K H V F C R V C I(i) * 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -101 C -1 1 0 -1 -2 -3 -4 -5 -6 -7 -82 K -2 0 2 1 0 -1 -2 -3 -4 -5 -63 K -3 -1 1 1 0 -1 -2 -3 -4 -5 -64 C -4 -2 0 0 0 -1 0 -1 -2 -3 -45 F -5 -3 -1 -1 -1 1 0 -1 -2 -3 -46 C -6 -4 -2 -2 -2 0 2 1 0 -1 -27 K -7 -5 -3 -3 -3 -1 1 1 0 -1 -28 C -8 -6 -4 -4 -4 -2 0 0 0 1 09 V -9 -7 -5 -5 -3 -3 -1 -1 1 0 0
Needleman-Wunsch-edu.pl
The Score Matrix----------------
Seq1(j)1 2 3 4 5 6 7 8 9 10Seq2 * C K H V F C R V C I(i) * 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -101 C -1 1 0 -1 -2 -3 -4 -5 -6 -7 -82 K -2 0 2 1 0 -1 -2 -3 -4 -5 -63 K -3 -1 1 1 0 -1 -2 -3 -4 -5 -64 C -4 -2 0 0 0 -1 0 -1 -2 -3 -45 F -5 -3 -1 -1 -1 1 0 -1 -2 -3 -46 C -6 -4 -2 -2 -2 0 2 1 0 -1 -27 K -7 -5 -3 -3 -3 -1 1 1 0 -1 -28 C -8 -6 -4 -4 -4 -2 0 0 0 1 09 V -9 -7 -5 -5 -3 -3 -1 -1 1 0 0
abc
A: matrix(i,j) = matrix(i-1,j-1) + (MIS)MATCH if (substr(seq1,j-1,1) eq substr(seq2,i-1,1)
B: up_score = matrix(i-1,j) + GAP
C: left_score = matrix(i,j-1) + GAP
Needleman-Wunsch-edu.pl
The Score Matrix----------------
Seq1(j)1 2 3 4 5 6 7 8 9 10Seq2 * C K H V F C R V C I(i) * 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -101 C -1 1 0 -1 -2 -3 -4 -5 -6 -7 -82 K -2 0 2 1 0 -1 -2 -3 -4 -5 -63 K -3 -1 1 1 0 -1 -2 -3 -4 -5 -64 C -4 -2 0 0 0 -1 0 -1 -2 -3 -45 F -5 -3 -1 -1 -1 1 0 -1 -2 -3 -46 C -6 -4 -2 -2 -2 0 2 1 0 -1 -27 K -7 -5 -3 -3 -3 -1 1 1 0 -1 -28 C -8 -6 -4 -4 -4 -2 0 0 0 1 09 V -9 -7 -5 -5 -3 -3 -1 -1 1 0 0
Needleman-Wunsch-edu.pl
Needleman-Wunsch-edu.pl
Seq1: CKHVFCRVCISeq2: CKKCFC-KCV ++--++--+- score = 0
• Practicum: use similarity function in initialization step -> scoring tables
• Time Complexity
• Use random proteins to generate histogram of scores from aligned random sequences
Time complexity with needleman-wunsch.pl
Sequence Length (aa) Execution Time (s)10 025 050 0100 1500 51000 192500 5595000 Memory could not be
written
Average around -64 !
-80-78-76-74-72 **-70 *******-68 ***************-66 *************************-64 ************************************************************-60 ***********************-58 ***************-56 ********-54 ****-52 *-50-48-46-44-42-40-38
If the sequences are similar, the path of the best alignment should be very close to the main diagonal.
Therefore, we may not need to fill the entire matrix, rather, we fill a narrow band of entries around the main diagonal.
An algorithm that fills in a band of width 2k+1 around the main diagonal.
Multiple Alignment Method
Multiple Alignment Method
Phylogenetic methods may be used to solve crimes, test purity of products, and determine whether endangered species have been smuggled or mislabeled: – Vogel, G. 1998.
HIV strain analysis debuts in murder trial. Science 282(5390): 851-853.
– Lau, D. T.-W., et al. 2001. Authentication of medicinal Dendrobium species by the internal transcribed spacer of ribosomal DNA. Planta Med 67:456-460.
Examples
– Epidemiologists use phylogenetic methods to understand the development of pandemics, patterns of disease transmission, and development of antimicrobial resistance or pathogenicity: • Basler, C.F., et al. 2001.
Sequence of the 1918 pandemic influenza virus nonstructural gene (NS) segment and characterization of recombinant viruses bearing the 1918 NS genes. PNAS, 98(5):2746-2751.
• Ou, C.-Y., et al. 1992. Molecular epidemiology of HIV transmission in a dental practice. Science 256(5060):1165-1171.
• Bacillus Antracis:
Examples
Tree Of Life
Modeling
Ramachandran / Phi-Psi Plot
Protein Architecture
• Finding a structural homologue• Blast
–versus PDB database or PSI-blast (E<0.005)
–Domain coverage at least 60%• Avoid Gaps
–Choose for few gaps and reasonable similarity scores instead of lots of gaps and high similarity scores
Modeling
Bootstrapping - an example
Ciliate SSUrDNA - parsimony bootstrap
Majority-rule consensus
Ochromonas (1)
Symbiodinium (2)
Prorocentrum (3)
Euplotes (8)
Tetrahymena (9)
Loxodes (4)
Tracheloraphis (5)
Spirostomum (6)
Gruberia (7)
100
96
84
100
100
100
Overview
Personalized Medicine,
Biomarkers …
… Molecular Profiling
First Generation Molecular Profiling
Next Generation Molecular Profiling
Next Generation Epigenetic Profiling
Concluding Remarks
Overview
Personalized Medicine,
Biomarkers …
… Molecular Profiling
First Generation Molecular Profiling
Next Generation Molecular Profiling
Next Generation Epigenetic Profiling
Concluding Remarks
Personalized Medicine
• The use of diagnostic tests (aka biomarkers) to identify in advance which patients are likely to respond well to a therapy
• The benefits of this approach are to– avoid adverse drug reactions– improve efficacy– adjust the dose to suit the patient– differentiate a product in a competitive market– meet future legal or regulatory requirements
• Potential uses of biomarkers– Risk assessment– Initial/early detection– Prognosis– Prediction/therapy selection– Response assessment– Monitoring for recurrence
Biomarker
First used in 1971 … An objective and « predictive » measure … at the molecular level … of normal and pathogenic processes and responses to therapeutic interventions
Characteristic that is objectively measured and evaluated as an indicator of normal biologic or pathogenic processes or pharmacologic response to a drug
A biomarker is valid if:– It can be measured in a test system with well
established performance characteristics – Evidence for its clinical significance has been
established
Rationale 1:Why now ? Regulatory path becoming more clear
There is more at stake than efficient drug development. FDA « critical path initiative » Pharmacogenomics guideline
Biomarkers are the foundation of « evidence based medicine » - who should be treated, how and with what.
Without Biomarkers advances in targeted therapy will be limited and treatment remain largely emperical. It is imperative that Biomarker development be accelarated along with therapeutics
Why now ?
First and maturing second generation molecular profiling methodologies allow to stratify clinical trial participants to include those most likely to benefit from the drug candidate—and exclude those who likely will not—pharmacogenomics-based
Clinical trials should attain more specific results with smaller numbers of patients. Smaller numbers mean fewer costs (factor 2-10)
An additional benefit for trial participants and internal review boards (IRBs) is that stratification, given the correct biomarker, may reduce or eliminate adverse events.
Molecular Profiling
The study of specific patterns (fingerprints) of proteins, DNA, and/or mRNA and how these patterns correlate with an individual's physical characteristics or symptoms of disease.
Generic Health advice
• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolarance)• Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)• & your grains (HLA-DQ2 – Celiac disease)• & your iron (HFE - Hemochromatosis)• Get more rest (HLA-DR2 - Narcolepsy)
Generic Health advice (UNLESS)
• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolarance)• Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)• & your grains (HLA-DQ2 – Celiac disease)• & your iron (HFE - Hemochromatosis)• Get more rest (HLA-DR2 - Narcolepsy)
Generic Health advice (UNLESS)
• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolerance)• Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)• & your grains (HLA-DQ2 – Celiac disease)• & your iron (HFE - Hemochromatosis)• Get more rest (HLA-DR2 - Narcolepsy)
Generic Health advice (UNLESS)
• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolerance)• Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)• & your grains (HLA-DQ2 – Celiac disease)• & your iron (HFE - Hemochromatosis)• Get more rest (HLA-DR2 - Narcolepsy)
EGFR based therapy in mCRC
Overview
Personalized Medicine,
Biomarkers …
… Molecular Profiling
First Generation Molecular Profiling
Next Generation Molecular Profiling
Next Generation Epigenetic Profiling
Concluding Remarks
Before molecular profiling …
Before molecular profiling …
Before molecular profiling …
First Generation Molecular Profiling
• Flow cytometry correlates surface markers, cell size and other parameters
• Circulating tumor cell assays (CTC’s) quantitate the number of tumor cells in the peripheral blood.
• Exosomes are 30-90 nm vesicles secreted by a wide range of mammalian cell types.
• Immunohistochemistry (IHC) measures protein expression, usually on the cell surface.
First Generation Molecular Profiling
• Gene sequencing for mutation detection
• Microarray for m-RNA message detection • RT-PCR for gene expression
• FISH analysis for gene copy number • Comparative Genome Hybridization (CGH) for
gene copy number
Basics of the “old” technology
• Clone the DNA.• Generate a ladder of labeled (colored)
molecules that are different by 1 nucleotide.• Separate mixture on some matrix.• Detect fluorochrome by laser.• Interpret peaks as string of DNA.• Strings are 500 to 1,000 letters long• 1 machine generates 57,000 nucleotides/run• Assemble all strings into a genome.
Genetic Variation Among People
0.1% difference among people
GATTTAGATCGCGATAGAGGATTTAGATCTCGATAGAG
Single nucleotide polymorphisms(SNPs)
The genome fits as an e-mail attachment
First Generation Molecular Profiling
• Gene sequencing for mutation detection
• Microarray for m-RNA message detection • RT-PCR for gene expression
• FISH analysis for gene copy number • Comparative Genome Hybridization (CGH) for
gene copy number
mRNA Expression Microarray
First Generation Molecular Profiling
• Gene sequencing for mutation detection
• Microarray for m-RNA message detection • RT-PCR for gene expression
• FISH analysis for gene copy number • Comparative Genome Hybridization (CGH) for
gene copy number
Overview
Personalized Medicine,
Biomarkers …
… Molecular Profiling
First Generation Molecular Profiling
Next Generation Molecular Profiling
Next Generation Epigenetic Profiling
Concluding Remarks
Second Generation DNA profiling
• Exome Sequencing (aka known as targeted exome capture) is an efficient strategy to selectively sequence the coding regions of the genome to identify novel genes associated with rare and common disorders.
• 160K exons
Second Generation DNA profiling
Second Generation DNA profiling
Con
tent
s-S
ched
ule
Besides the 6000 protein coding-genes …
140 ribosomal RNA genes275 transfer RNA gnes40 small nuclear RNA genes>100 small nucleolar genes
Function of RNA genes
pRNA in 29 rotary packaging motor (Simpson et el. Nature 408:745-750,2000)Cartilage-hair hypoplasmia mapped to an RNA (Ridanpoa et al. Cell 104:195-203,2001)The human Prader-Willi ciritical region (Cavaille et al. PNAS 97:14035-7, 2000)
Second Generation RNA profiling
RNA genes can be hard to detects
UGAGGUAGUAGGUUGUAUAGU
C.elegans let-27; 21 nt (Pasquinelli et al. Nature 408:86-89,2000)
Often smallSometimes multicopy and redundantOften not polyadenylated (not represented in ESTs)Immune to frameshift and nonsense mutationsNo open reading frame, no codon biasOften evolving rapidly in primary sequence
Second Generation RNA profiling
ncRNAs in human genome
tRNA 60018S rRNA 2005.8S rRNA 20028S rRNA 2005S rRNA 200snoRNA 300miRNA 250U1 40U2 30U4 30U5 30U6 20U4atac 5U6atac 5U11 5U12 5
SRP RNA 1
RNase P RNA 1
Telomerase RNA 1
RNase MRP 1
Y RNA 5
Vault 4
7SK RNA 1
Xist1
H191
BIC1
Antisense RNAs 1000s?
Cis reg regions 100s?
Others ?
Mapping Structural Variation in Humans
- Thought to be Common 12% of the genome (Redon et al. 2006)
- Likely involved in phenotype variation and disease
- Until recently most methods fordetection were low resolution (>50 kb)
CNVs
>1 kb segments
Size Distribution of CNV in a Human Genome
Overview
Personalized Medicine,
Biomarkers …
… Molecular Profiling
First Generation Molecular Profiling
Next Generation Molecular Profiling
Next Generation Epigenetic Profiling
Concluding Remarks
CONFIDENTIAL
Defining Epigenetics
Reversible changes in gene expression/function
Without changes in DNA sequence
Can be inherited from precursor cells
Allows to integrate intrinsic with environmental signals (including diet)
Methylation I Epigenetics | Oncology | Biomarker
Genome
DNA
Gene Expression
Epigenome
Chromatin
Phenotype
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
Epigenetic Regulation: Post Translational Modifications to Histones and Base Changes in DNA
Epigenetic modifications of histones and DNA include:– Histone acetylation and methylation, and DNA methylation
HistoneAcetylation
HistoneMethylation
DNA Methylation
MeMe
Ac
Me
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
MGMT BiologyO6 Methyl-Guanine Methyl Transferase
Essential DNA Repair Enzyme
Removes alkyl groups from damaged guanine bases
Healthy individual: - MGMT is an essential DNA repair enzymeLoss of MGMT activity makes individuals susceptible to DNA damage and prone to tumor development
Glioblastoma patient on alkylator chemotherapy: - Patients with MGMT promoter methylation show have longer PFS and OS with the use of alkylating agents as chemotherapy
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
MGMT Promoter Methylation Predicts Benefit form DNA-Alkylating Chemotherapy
Post-hoc subgroup analysis of Temozolomide Clinical trial with primary glioblastoma patients show benefit for patients with MGMT promoter methylation
0
5
10
15
20
25Median Overall Survival
21.7 months
12.7 months
radiotherapy
plus temozolomide
Methylated MGMT Gene
Non-Methylated MGMT Gene
radiotherapy
Adapted from Hegi et al.NEJM 2005352(10):1036-8.Study with 207 patients
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
Genome-wide methylation by methylation sensitive restriction enzymes
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
Genome-wide methylation by probes
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL# samples
# markers
Genome-wide methylation …. by next generation sequencing
Discovery
Verification
Validation
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
MBD_Seq
DNA Sheared
Immobilized Methyl Binding Domain
Methylation I Epigenetics | Oncology | Biomarker
Condensed Chromatin
DNA Sheared
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
Immobilized Methyl binding domain
MgCl2
Next Gen SequencingGA Illumina: 100 million reads
MBD_Seq
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
MBD_SeqMGMT = dual core
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL# samples
# markers
MBD_Seq
Genome-wide methylation …. by next generation sequencing
Discovery
1-2 millionmethylation
cores
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
Data integrationCorrelation tracks
142
methylation methylation
expression expression
Corr =-1 Corr = 1
CONFIDENTIAL
Correlation trackin GBM @ MGMT
143Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX |
+1
-1
CONFIDENTIAL# samplesMethylation I Epigenetics | Oncology | Biomarker
# markers
MBD_Seq
454_BT_Seq
MSP
Genome-wide methylation …. by next generation sequencing
Discovery
Verification
Validation
I NEXT-GEN | PharmacoDX |
CONFIDENTIAL
GCATCGTGACTTACGACTGATCGATGGATGCTAGCAT
unmethylated alleles
less methylationmethylated alleles
more methylation
Deep Sequencing
CONFIDENTIAL
Deep MGMTHeterogenic complexity
Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
CONFIDENTIAL
147Methylation I Epigenetics | Oncology | Biomarker
I NEXT-GEN | PharmacoDX | CRC
Overview
Personalized Medicine,
Biomarkers …
… Molecular Profiling
First Generation Molecular Profiling
Next Generation Molecular Profiling
Next Generation Epigenetic Profiling
Concluding Remarks
Translational Medicine: An inconvenient truth
• 1% of genome codes for proteins, however more than 90% is transcribed
• Less than 10% of protein experimentally measured can be “explained” from the genome
• 1 genome ? Structural variation• > 200 Epigenomes ??
• Space/time continuum …
Translational Medicine: An inconvenient truth
• 1% of genome codes for proteins, however more than 90% is transcribed
• Less than 10% of protein experimentally measured can be “explained” from the genome
• 1 genome ? Structural variation• > 200 Epigenomes …
• “space/time” continuum
Epigenetic (meta)information = stem cells
Cellular programming
Cellular reprogramming
Tumor
Epigenetically altered, self-renewing cancer stem cells
Tumor Development and Growth
Gene-specificEpigeneticreprogramming
Cellular reprogramming
156
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