Multilayer OMIC Data in Medullary Thyroid Carcinoma ... · Biology of Human Tumors Multilayer OMIC Data in Medullary Thyroid Carcinoma Identiﬁes the STAT3 Pathway as a Potential

Biology of Human Tumors

Multilayer OMIC Data in Medullary ThyroidCarcinoma Identifies the STAT3 Pathway as aPotential Therapeutic Target in RETM918T TumorsVeronika Mancikova1, Cristina Montero-Conde1, Javier Perales-Paton2,Agustin Fernandez3, María Santacana4, Karolina Jodkowska5, Lucia Inglada-P�erez1,6,Esmeralda Castelblanco7,8, Salud Borrego9, Mario Encinas4, Xavier Matias-Guiu4,10,Mario Fraga3, and Mercedes Robledo1,6

Abstract

Purpose:Medullary thyroid carcinoma (MTC) is a rare diseasewith few genetic drivers, and the etiology specific to each knownsusceptibility mutation remains unknown. Exploiting multilayergenomic data, we focused our interest on the role of aberrantDNAmethylation in MTC development.

Experimental Design: We performed genome-wide DNAmethylation profiling assessing more than 27,000 CpGs in thelargest MTC series reported to date, comprising 48 molecularlycharacterized tumors. mRNA and miRNA expression data wereavailable for 33 and 31 tumors, respectively. Two human MTCcell lines and 101 paraffin-embedded MTCs were used forvalidation.

Results: The most distinctive methylome was observed forRETM918T-related tumors. Integration of methylation data withmRNA and miRNA expression data identified genes negativelyregulated by promoter methylation. These in silico findings were

confirmed in vitro for PLCB2, DKK4, MMP20, and miR-10a, -30a,and -200c. Themutation-specific aberrant methylation of PLCB2,DKK4, and MMP20 was validated in 25 independent MTCs bybisulfite pyrosequencing. Themethylome and transcriptome dataunderscored JAK/Stat pathway involvement in RETM918T MTCs.Immunostaining [immunohistochemistry (IHC)] for the activeform of signaling effector STAT3 was performed in a series of 101MTCs. As expected, positive IHC was associated with RETM918T-bearing tumors (P < 0.02). Pharmacologic inhibition of STAT3activity increased the sensitivity to vandetanib of the RETM918T-positive MTC cell line, MZ-CRC-1.

Conclusions:Multilayer OMIC data analysis uncovered meth-ylation hallmarks in genetically defined MTCs and revealed JAK/Stat signaling effector STAT3 as a potential therapeutic target forthe treatment of RETM918T MTCs. Clin Cancer Res; 23(5); 1334–45.�2016 AACR.

IntroductionMedullary thyroid carcinoma (MTC) is a malignant tumor of

the thyroid gland. It is composed of C cells and accounts for up to

2% of all thyroid cancers (1). Around 25% of MTCs arise due togermlinemutations in the "rearranged during transfection" (RET)gene as part of multiple endocrine neoplasia type 2 syndrome,whereas the remaining forms are sporadic. The genomic land-scape of recurrent driver alterations found in sporadic diseaseseems to bemainly restricted to activatingmutations affecting theRET (2) or RAS oncogenes (3, 4). There is a distinct genotype–phenotype correlation for each of these alterations (3, 5, 6). TheRETM918T mutation seems to trigger the most aggressive disease,showing poor prognosis with a higher prevalence of distantmetastases during follow-up (5, 7). MTC cases with advanceddisease are treated with tyrosine kinase inhibitors (TKI), such asvandetanib or cabozantinib, which have shown to have thera-peutic benefits. However, many patients suffer secondary toxi-cities that lead to dose reductions or treatment interruption (8, 9).A comprehensive understanding of the molecular mechanismsdriving the different genetic classes of this disease is required toidentify new therapeutic opportunities for these patients.

High-throughput technologies have already been used todefine genomic signatures linked to particular driver mutationsin cell lines and other thyroid cancer subtypes (10–15), uncover-ing molecular events associated with progression and recurrence.However, data on MTC are scarce due to the disease's lowprevalence and, in consequence, the difficulty in collecting aninformative sample set. On top of that, C cells represent less than1% of the thyroid gland, and therefore it is very challenging to

1Hereditary Endocrine Cancer Group, Spanish National Cancer Research Centre(CNIO), Madrid, Spain. 2Translational Bioinformatics Unit, Clinical ResearchProgram, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.3Cancer Epigenetics Laboratory, Institute of Oncology of Asturias (IUOPA),HUCA, University of Oviedo, Asturias, Spain. 4Department of Endocrinology andNutrition, University Hospital Arnau deVilanova, IRBLLEIDA, Lleida, Spain. 5DNAReplication Group, Spanish National Cancer Research Centre (CNIO), Madrid,Spain. 6ISCIII Center for Biomedical Research on Rare Diseases (CIBERER),Madrid, Spain. 7Department of Endocrinology and Nutrition, Germans Trias iPujol Hospital, Health Sciences Research Institute of the "Germans Trias i Pujol"Foundation (IGTP), Badalona, Spain. 8Centre for Biomedical Research on Dia-betes and Associated Metabolic Diseases (CIBERDEM), ISCIII, Spain. 9HospitalVirgen del Rocío, Sevilla. 10Department of Pathology, Hospital Universitari deBellvitge, IDIBELL, Barcelona.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

V. Mancikova and C. Montero-Conde contributed equally to this work.

Corresponding Author: Mercedes Robledo, Spanish National Cancer Center(CNIO), Calle Melchor Fernandez Almagro 3, Madrid 28029, Spain. Phone 34-917-328000; Fax. 34-912-246972; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-16-0947

�2016 American Association for Cancer Research.

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portray their normal genomic landscape. To date, only a handfulof studies using mRNA expression arrays have been published,reporting that MTC expression profiles are mutation-specific(16–18). Interestingly, the overexpression of genes related tothe tumor growth factor-b pathway (17) and tumor invasionand metastases (16, 18) were notable among MTCs caused bythe RETM918T mutation. On the other hand, the miRNA pro-filing studies published so far have focused on patient out-comes rather than on genetic subtypes of the disease but havenevertheless uncovered some molecular events related tometastasis and worse outcomes (19–21).

Some of the differentially expressed genes identified in theprevious profiling studies are undoubtedly regulated by aberrantmethylation. However, explorations of this epigenetic mecha-nism inMTC, the deregulation of which is a well-known hallmarkof cancer, have been limited, either focused on specific candidategenes, such as RAS association domain family protein 1 (RASSF1;ref. 22) and Sprouty 1 (SPRY1; ref. 23) or exploring the wholemethylome in very few samples (15). Thus, it remains poorlycharacterized.

Therefore, we studied the role of aberrant DNA methylation inthe pathology of genetic subtypes of MTC. We quantitativelyprofiled the largest cohort of MTCs published to date, composedof 48 samples, on the basis of the DNAmethylation levels of morethan 27,000 CpGs across the genome. We observed significantdifferences between the methylation patterns among the samplescarrying distinct mutations. Taking advantage of multilayer geno-mic data consisting of the transcriptome, miRNome, and methy-lome, we uncovered genes negatively regulated by methylation.Moreover, pathway enrichment analysis underscored JAK/Statpathway involvement in theRETM918T-driven tumors.Usinga seriesof101 formalin-fixed,paraffin-embedded (FFPE)MTCsamples,weobserved that phosphorylation of the JAK/Stat pathway effectorSTAT3 (Tyr705) was more evident among RETM918T-positivetumors. In vitro pharmacologic inhibition of STAT3 phosphoryla-tion in 2 human MTC cell lines (MZ-CRC-1 and TT) revealed ahigherdependencyof theRETM918T-mutatedMZ-CRC-1cell lineonSTAT3 signaling. Furthermore, we observed an apparently syner-gistic effect between the action of STAT3 inhibitor and Vandetanib,which could be exploited to treat patients with MTC.

Materials and MethodsHuman MTC tissue samples

Forty-eight fresh-frozen MTC tumor samples were collected atthe Spanish National Cancer Research Center (CNIO) in collab-oration with the CNIO Tumor Bank. Written informed consentwas obtained from all study participants, and the study wasapproved by the relevant institutional review board (Comit�e debio�etica y bienestar animal of the Instituto de Salud Carlos III).Sections of each sample were evaluated by a pathologist. Onlysamples inwhich at least 80%of cells were cancerouswere used inthis study, and cases with high amyloid content were excluded. Atotal of 101 paraffin-embedded (FFPE) MTC samples from unre-lated individuals were used for validation studies. Twenty-fivewere used for the validation of DNA functional methylationmarkers, and the entire collection was used for an immunohis-tochemical (IHC) study. Hematoxylin and eosin–stained sectionsof each FFPE tumor sample were examined by a pathologist toselectMTC areas representative of each tumor and thesewere usedto construct tissue microarrays (TMA). Three TMAs containing 2cores from each of 94 tumor samples were constructed. Theremaining 7 tumors were evaluated as complete sections.

Cell lines and culture conditionsMZ-CRC-1 and TT cell lines were provided by Dr. James Fagin

(Human Oncology & Pathogenesis Program, Memorial SloanKettering Cancer Centre, New York, NY) and authenticated byRET sequencing. The MZ-CRC-1 cell line is derived from a met-astatic MTC and harbors the RETM918T mutation, whereas TT cellline harbors amutation in the 634th codonofRET.MZ-CRC-1 andTT were cultured in DMEM GlutaMAX (Invitrogen) and F12 NutMix medium with GlutaMAX (Invitrogen), respectively, supple-mented with 10% (v/v) FBS (PAA Laboratories), 1% (v/v) pen-icillin/streptomycin, and 0.6% (v/v) Fungizone (Gibco).

DNA, RNA, and protein extractionGenomic DNA from all samples (frozen and FFPE tumors and

cell lines) was extracted using the DNeasy Blood and Tissue Kit(Qiagen) according to the manufacturer's protocol. Total RNAwas extracted from 5� 106 ofMZ-CRC-1 and TT cells and 1mL ofTRIzol (Life Technologies) using the standard conditions. Forimmunoblotting, cells lysates were produced in RIPA lysis buffer(Sigma) containing protease inhibitormixture (Sigma) and Phos-phatase Inhibitor Cocktail 2&3 (Sigma).

Mutation analysisAll sampleswere screened for hotspotmutations in exons 8, 10,

11, 13, 14, 15, and16ofRETby Sanger sequencing. If negative, thehotspot codons 12, 13, and 61 of exons 2 and 3 of all RAS geneswere screened using the same technique. Primer sequences andexamples of the identified mutations can be found in Supple-mentary Table S1. Tumors were classified as "wild-type" (WT) ifno mutation was found.

DNA methylation assay, data processing, and data analysisGenomic DNA was bisulfite-converted using the EZ DNA

Methylation Kit (Zymo Research) following the manufacturer'srecommended procedures. Genome-wide promoter DNA meth-ylation was determined using the Illumina Infinium Human-Methylation 27K Platform (Illumina) as described previously(24). This assay generates DNAmethylation data for 27,578 CpGdinucleotides covering 14,473 genes. For each CpG site,

Translational Relevance

Medullary thyroid carcinoma (MTC) is a rare disease withfew genetic drivers that, when diagnosed at advanced stage,remains incurable. Because of its rarity, its genomic dissectionhas not been comprehensively explored. Thismultilayer geno-mic study, considering the transcriptome, miRNome andmethylome, is the first of its kind and has uncovered genesnegatively regulated by methylation. Functional annotationenrichment analysis identified the JAK/Stat pathway as aspecific hallmark of RETM918T-bearing MTCs. In vitro studieswith MTC cell models point to a RETM918T genetic class–specific proliferative dependency on STAT3 activity. Remark-ably, the inhibition of STAT3 increases the sensitivity ofRETM918T-bearing MTC cells to the FDA-approved RET inhib-itor vandetanib. This combinational treatment could poten-tially overcome the adverse effects encountered in clinicalpractice when vandetanib monotherapy is applied.

Genomic Integration in Medullary Thyroid Carcinoma

www.aacrjournals.org Clin Cancer Res; 23(5) March 1, 2017 1335




methylation levels were quantified using b-values, which repre-sent the proportion of methylation, calculated as M/(M þ U),whereM is the methylated probe intensity andU is the unmethy-lated probe intensity. b-values range from 0 to 1, with 0 beingcompletely unmethylated and 1 being completely methylated.We excluded probes designed for sequences on either the X or theY chromosome (1,085 or 7 probes, respectively), as well as 46probes withmissing value in at least one sample. The data used inthese analyses have been deposited in NCBI's Gene ExpressionOmnibus and are accessible through GEO Series accession num-ber GSE72729 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc¼GSE72729). The applied analysis workflow is schematicallydepicted in Supplementary Fig. S1.

Unsupervised hierarchical clustering was carried out usingCluster 3.0 software with "average linkage" (Pearson correlation,uncentered metrics). The clusters were subsequently visualizedusing Treeview (http://rana.stanford.edu/software). Principalcomponent analysis (PCA) was performed using R CRAN version2.15.3 (R, 2013). Differences in DNA methylation status amongtumor groups (based on the driver mutations present in thesamples) were tested using POMELO II, applying linear models(limma; ref. 25). Only groups containing at least 5 tumors wereconsidered. Each genetic class was compared withWT tumors andwith each of the other classes. To account for multiple hypothesistesting, P values were adjusted using Benjamini and Hochbergfalse discovery rate (FDR) correction. We defined a probe to behypomethylated or hypermethylated when it displayed a meanb-value difference (Db-value) <�0.2 or >0.2, respectively, calcu-lated asmeandifference amongdistinct tumor groups, andhadanFDR < 0.05. With the extracted lists of differentially methylatedgenes, we performed Venn diagram analyses using BioVenn(http://www.cmbi.ru.nl/cdd/biovenn/) and Pathway enrichmentanalysis using the functional annotation clustering tool imple-mented in DAVID Bioinformatics Resources 6.7 (http://david.abcc.ncifcrf.gov/home.jsp).

Because we had bothmRNA (18) andmiRNA expression (GEOseries accession number GSE72728; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc¼GSE72728] data for 33 and 31 of thetumors used in this study, respectively, we aimed to identify geneswhose expression is correlatedwith themethylation status of theircorresponding promoter regions. Expression of all the availableprotein-coding genes identified as differentially methylated bytumor genotype (FDR < 0.05, |Db-value| > 0.2) was examined aspreviously described (12, 26). The integration of miRNA datarequired a genome-wide approach, as there was only one micro-RNA gene promoter showing differential methylation. Morespecifically, there were 254 probes included on the methylationplatform that mapped to promoters of 110 microRNA genespreviously annotated by the manufacturer (24). To this number,we added 423 probes belonging to putative promoter regions of151miRNAs identified using the PROmiRNAmethod (27). Theseadditional probes and their correspondingmiRNAgenes are listedin Supplementary Table S2. Correlation was measured by theSpearman coefficient using R CRAN version 2.15.3 (R, 2013) andgenes with a negative correlation coefficient and P < 0.05 wereconsidered significantly correlated.

Confirmation of inverse correlation between methylation andexpression in MTC cell lines

To confirm the effect of DNA methylation on gene expression,MZ-CRC-1 cells at 60% of confluence were treated with 50 mg/mL

5-aza-20-deoxycytidine (Sigma). This treatment was repeatedevery 48 hours over 6 days and then the cells were collected forRNA and DNA extraction.

DNA methylation level assessmentBisulfite sequencing ofMTC cell lines using the primers listed in

Supplementary Table S3 was carried out according to protocolsdescribed elsewhere (12) to assess methylation level of the select-ed promoters (belonging to 3 protein-coding genes and 3miRNAgenes).

qRT-PCRFor the assessment of expression of the selected protein-coding

genes, 1 mg of total RNAwas reverse-transcribed using SuperscriptII (Invitrogen) and an oligo dT14 primer followingmanufacturer'sinstructions. The amounts of DKK4, MMP20, and PLCB2 mRNAwere quantified by real-time PCR with QuantStudio 6 Flex Real-Time PCR system (Applied Biosystems), using primers designedto be specific for the 3 genes (Supplementary Table S3) andprobesfrom the Universal ProbeLibrary Set, Human (Roche). Normal-ization was carried out with the internal standard b-actin (ACTB).

For miRNA gene expression quantification, 10 ng of total RNAwas used for first-strand cDNA synthesis using themiRCURY LNAUniversal RT miR PCR System (Exiqon), and LNA miR-PCRprimer/SYBR Green Mix (Exiqon) was used for subsequent quan-tification of miR-10a, -30a, and -200c according to the manu-facturer's recommendations. miR-16 was selected as the referencegene for normalization.

Negative controls were included in all PCR series and assayswere carried out in triplicate. The DDCt method was used for thecalculation of mRNA/miRNA content (28).

Methylation status validationThree of the most differentially methylated probes, all show-

ing negative correlation with expression of the correspondingprotein-coding gene, and with biologic functions potentiallyrelevant to MTC were selected for validation. The technicalvalidation of microarray results in a subset of the originaldiscovery series (comprising 6 RETM918T, 6 RETC634, 3 RAS, 2WT tumors, and 6 tumors bearing other RET mutations) wasperformed using bisulfite sequencing, as described elsewhere(12). The candidate functional methylation markers were thenvalidated by pyrosequencing in 25 independent FFPE MTCsamples (8 RETM918T, 4 RETC634, 3 RAS, and 10 WT tumors).The region of interest, including the CpG assessed by theInfinium array, was first amplified from the bisulfite-treatedDNA by a set of primers designed using the PyroMark assaydesign software (version 2.0.01.15). Following PCR amplifica-tion, pyrosequencing was performed using PyroMark Q24reagents, vacuum prep workstation, equipment, and software(Qiagen). The primers used in these steps are listed in Supple-mentary Table S3.

IHC studyThree paraffin-embedded TMAs and 7 complete sections were

used for the evaluation of a JAK/Stat pathway effector pSTAT3(Tyr705).Monoclonal antibodywas obtained fromCell SignalingTechnology (#9145). Two independent experienced pathologists(X. Matias-Guiu and M. Santacana) evaluated the staining byvisual examination under amicroscope. Since each TMA included2 different tumor cores from each case, IHC scoringwas done after

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Clin Cancer Res; 23(5) March 1, 2017 Clinical Cancer Research1336




examining both samples. When the 2 cores gave inconsistentresults, the complete section of the corresponding tumor wasevaluated. Samples were scored from 0 (no immunostaining) to300 (intensive immunostaining) depending on both the intensityand extension of staining.

Inhibitor treatment and cell proliferation assayFor the study of proliferation inhibition by TKI and the

inhibition of the phosphorylation of STAT3, vandetanib(Seleckchem) and LLL12 (EMD Millipore; ref. 29) were madeup as a stock solution of 5 mmol/L in DMSO. MZ-CRC-1 andTT cells were plated in 200 mL of medium at concentrations of 2� 104 cells per well. After 36 hours, cells were treated withincreasing concentrations of the indicated compounds (0,0.005, 0.05, 0.1, 0. 5, 1, 2.5, 5 mmol/L). A concentration of0.1% DMSO was used in all conditions. This treatment wasrepeated every 4 days. Proliferation was measured at 10 daysusing a cell proliferation kit (WST-1 reagent, Roche). Theconcentration that led to 50% growth inhibition (IC50) was

calculated using GraphPad Prim. All experiments were per-formed in triplicate. For the combinational treatment, wealso used the above-mentioned experimental scheme. A fixedconcentration of 0.04 mmol/L of LLL12 was combined withincreasing concentrations of vandetanib (0, 0.005, 0.05, 0.1,0.5, 1, 2.5, 5 mmol/L). The suboptimal LLL12 concentration(0.04 mmol/L) used causes only 10% growth inhibition of MZ-CRC-1 cells. In addition, for Western blot analysis, MZ-CRC-1cells were treated with increasing concentrations of vandetanib(0.01, 0.1, 0.5 mmol/L), 0.04 mmol/L LLL12, and a combinationof LLL12 and vandetanib (0.04 and 0.1 mmol/L, respectively)for 24 hours. Cells were then washed with ice-cold PBS,collected by scraping, and cell lysates were prepared using theabove-mentioned protocol. The following primary antibodieswere used for immunoblotting: p-ERK, pAKT, pSTAT3, andGAPDH (Cell Signaling; #9101, #4060, #9145, and #2118,respectively). Absolute band intensities of the indicated pro-teins were captured and quantified with the Image Lab softwarev4.1 (BioRad). GAPDHwas used as loading control normalizer.

A B

918634RASWT

Other

DNA Methylation

0 0.5 1

Figure 1.

Methylome of the discovery series. Unsupervised hierarchical cluster analysis of 48 medullary tumors divided the sample set into 2 main clusters. "Cluster A" wascomposed mostly of RETM918T-related samples. "Cluster B" included the majority of RETC634 and wild-type cases. Color codes used are as follows: red ¼ RETM918T

mutation, black ¼ RETC634 mutation, yellow ¼ RAS mutation, transparent ¼ other RET mutation, green ¼ WT.






Data were expressed in relative units using the control condi-tion as reference.

ResultsMolecular characterization of the MTC discovery series

Among the tumors whose genome-wide DNA methylationlevels were measured, 36 (75%) harbored a RET mutation (13

carried the RETM918T mutation, 14 RETC634, and 9 less frequentRETmutations) and 6 (12.5%) tumors harbored a RASmutation.The remaining 6 tumors (12.5%) did not carry any alteration inthe studied genes and were considered WT. The results of themutational screening together with the main clinicohistopatho-logic characteristics of the samples are summarized in Supple-mentary Table S4.

B

982

58 36 14

5222

RET M918T

RET C634 RAS

63

94

1

5119

RET M918T

RET C634 RAS

Hypermethylated probes Hypomethylated probes

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C

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1,200

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HypermethylatedHypomethylated

Methylated loci in ESCPolyComb targetsCpG Islands<0.0015 <0.0001 <0.0001

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Out

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Out

In

Out

In

P = 0.05

P = 0.05P = 0.05

Figure 2.

Identification of differentiallymethylatedprobes.A,Volcanoplots, fromeachof the supervised analysis carried out, identifyingdifferentially hypomethylated (green)and hypermethylated (red) probes, defined on the basis of FDR < 0.05 and |Db-value|� 0.2. B, Venn diagram showing the overlap between the identifiedmutation-specific hyper- and hypomethylatedprobes fromcomparisonswithWT tumors.C,Number of probes hypermethylated and hypomethylated, by locationwith respectto CpG islands; gene targeted by PolyComb repressive complex; loci that are heavily methylated in embryonic stem cells.

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MTC methylation profiles relate closely to RET mutationalstatus

We identified 11,915 probes that were constitutively unmethy-lated (b < 0.2) in all samples. According to DAVID functionalenrichment analysis (30), the majority (97.5%) of these werelocated within CpG islands adjacent to housekeeping genes(Benjamini–Hochberg adjusted P ¼ 7.1 � 10�10). On the otherhand, 179 probes were constitutively methylated (b-values > 0.8

for all samples). Both constitutively unmethylated and methyl-ated probes were excluded from further analyses. A PCA of theremaining 14,346 probes (belonging to 9,216 consensus codingsequences) did not identify any batch effects affecting the data(Supplementary Fig. S2).

Unsupervised hierarchical analysis using the 851 probes withthe variance >0.2 identified 2 principal clusters that differed intheir underlying genetics (Fig. 1). Only 2 samples did not fall into

0

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TT

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MZ-CRC-1MZ-CRC-1 5AdC

MZ-CRC-1MZ-CRC-1 5AdC

MZ-CRC-1TT

MZ-CRC-1TT

Figure 3.

Confirmation of negative correlation betweenmethylation and gene expression in MZ-CRC-1 and TT cell lines.A,DNAmethylation levels of promoter CpGs ofDKK4,PLCB2, MMP20, miR-10a, -200c, and -30a as measured by bisulfite sequencing. B, Expression of DKK4, PLCB2, MMP20, miR-10a, -200c, and -30a showsan opposite trend as compared with the DNA methylation levels of the genes, confirming in silico results. C, 5-aza-20-deoxycytidine (5AdC) treatment causes thereactivation of DKK4, PLCB2, MMP20, miR-10a, -200c, and -30a expression in the MZ-CRC-1 cell line.






either cluster. Interestingly, cluster B showed higher levels ofmethylation when compared with cluster A (P ¼ 3.0 � 10�9)and was enriched withWT cases and those harboring the RETC634

mutation, whereas cluster A was composed mostly of RETM918T-positive tumors (P<0.02).Wedid not observe any clear clusteringof the RAS-mutated samples.

Supervised analysis according to driver mutation allowed us toidentify a list of differentially methylated probes associated withspecific mutations, which was especially long for RETM918T-relat-ed tumors. These findings confirmed those from the unsupervisedclustering, as in this group, there were more hypomethylatedprobes (Fig. 2A and B and Supplementary Table S5). The protein-coding gene DKK4 (Dickkopf WNT signaling pathway inhibitor4), previously described to be upregulated in RETM918T-relatedMTCs (18), was one of the genes affected by hypomethylationamong RETM918T-positive carcinomas. Also consistent with theunsupervised clustering result, the lists of differentially methyl-ated probes characteristic of the other genetic conditions(RETC634- and RAS-related tumors) when comparing to WT, wereconsiderably shorter (Fig. 2B). Nevertheless, hypomethylation ofGAL was detected among RETC634-positive tumors when com-

paring with WT, and this event could cause the increased expres-sion of this molecule reported elsewhere (18).

Hypermethylation more frequently affected probes locatedwithin CpG islands (P ¼ 0.0014) as well as those located nearstem cell PolyComb Group target genes (P < 0.0001). Hypo-methylated probes, on the other hand, were enriched with CpGsthat are heavily methylated in embryonic stem cells (P <0.0001; Fig. 2C).

OMIC data integration reveals potentially functional DNAmethylation changes in MTC

The above findings indicated that some epigenetic changes canhave a functional consequence, which encouraged us to system-atically explore genes (protein and miRNA coding) regulated bymethylation at their promoters (see Supplementary Fig. S1 forfurther details). mRNA expression data were available for morethan 91% of protein-coding genes that were differentially meth-ylated on the basis of tumor genotype. A negative correlationbetween expression and methylation was observed for 5% ofthese (Supplementary Fig. S3). PLCB2, DKK4, and MMP20 wereselected for further study, as they have been previously described

ReplicationTechnical validationDiscovery

MMP20

PLCB2

DKK4

*** *

*** *

*** **

*P < 0.05; ** P < 0.01; *** P < 0.001

Figure 4.

Validation of selected loci. Three CpGs from promoter regions of MMP20, PLCB2, and DKK4 were selected for validation. Left, results from the discovery series arerepresented. Middle, correlation between the results from Illumina Infinium HumanMethylation 27K Platform and bisulfite sequencing for selected loci is shown.Bisulfite sequencing was performed in a subset of samples included in the discovery series (6 RETM918T, 6 RETC634, 3 RAS, 2 WT tumors, and 6 tumors bearing otherRET mutations). Right, results of bisulfite pyrosequencing of 25 independent MTC samples (8 RETM918T, 4 RETC634, 3 RAS, and 10 WT tumors) are depicted.

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to have a role in the promoting tumor growth (18, 31–33).Moreover, a genome-wide assessment ofmethylation andmiRNAexpression ofmatching samples identified 5miRNA genes (6.9%)showing negative correlation (Supplementary Fig. S3). MiR-10awas also found as differentially methylated in the previoussupervised analyses and was selected for further studies. Twoadditional miRNA genes (miR-30a and -200c) showing negativecorrelation between expression and methylation, and a tendencytoward differential methylation (FDR < 0.15) in supervised clus-tering, were also studied further due to their biologic function(21, 34, 35). As assessed by bisulfite sequencing, the levels ofmethylation of the 6 genes (3 protein-coding and 3 microRNAs)differed between the 2 available MTC cell lines (Fig. 3A). Apartfrom 2 genes that were not expressed by either of the cell lines(MMP20 and miR-30a), the expression of the other 4 genesconfirmed the results of in silico predictions of inverse correlationbetweenDNAmethylation and gene expression (Fig. 3B). BecauseMZ-CRC-1 showed systematically higher levels of DNA methyl-ation of these 6 loci, we treated it with the demethylating agent5-aza-20-deoxycytidine. A higher expression of all genes in theMZ-CRC-1 cell line was detected in the treated condition ascompared with untreated one (Fig. 3C), further confirming thefunctional effect of these epigenetic changes.

Validation of aberrant methylation of candidate oncogenesCpGs from the promoters of 3 differentially methylated

genes (DKK4, MMP20, PLCB2) were selected for validation.These genes also showed a negative correlation between DNAmethylation levels and gene expression (Supplementary Fig.S3). As depicted in Fig. 4, the results of bisulfite sequencing in asubset of the original series revealed a high correlation with thearray-based measures (R2 ranging from 0.66 to 0.80). More-over, we were able to replicate the findings by bisulfite pyr-osequencing of an independent series of 25 FFPE MTCs (Fig. 4),although the difference between the DNA methylation levels ofthe compared groups was smaller, probably because the DNAsource was paraffin tissue.

Multi-OMIC data and pathway enrichment analyses uncoverJAK/Stat pathway involvement in RETM918T MTCs

Functional annotation clustering by DAVID (30) of the geneswith aberrant methylation (Supplementary Fig. S4) and byGene Set Enrichment Analysis (GSEA) of differentiallyexpressed genes (18) among RETM918T-positive carcinomasuncovered a significant enrichment for signaling pathways suchas cytokine–cytokine receptor interaction (P ¼ 1.5 � 10�7) andJAK/Stat (P ¼ 2.3 � 10�4). STAT3 is a JAK/Stat pathway effectorand a putative downstream target of RET (36). Thus, weassessed by IHC its active phosphorylated form (at Tyr705)in 101 MTC samples. RETM918T-related tumors were morefrequently and intensively positively stained than the othergenetic classes (P < 0.02, Fig. 5A) confirming activation of theJAK/Stat cascade and the in silico prediction. We aimed toexploit these differences to study in vitro the functional rele-vance of STAT3 activation in MTC. As revealed by proliferationassay, MZ-CRC-1 cells were more sensitive to inhibition ofSTAT3 phosphorylation than TT cells [IC50 (MZ-CRC-1) ¼ 0.06mmol/L, IC50 (TT) ¼ 0.23 mmol/L, Fig. 5B]. In fact, theRETM918T-mutated cell line showed a higher sensitivity to thistreatment than to the standard of care, TKI vandetanib [IC50

(MZ-CRC-1) ¼ 0.62 mmol/L, Fig. 5B].

Because STAT3 is a downstream point of convergence formanygrowth factor pathways, synergymaybe expectedwhen combinedwith other inhibitors (37). A suboptimal dose of pSTAT3 inhib-itor, when combined with vandetanib, apparently showed asynergistic effect on cell growth inhibition of RETM918T-positivecells (Fig. 5C). Moreover, treatment with vandetanib produced adose-dependent inhibition of pSTAT3 in MZ-CRC-1 and subop-timal combinational treatment of the 2 drugs (0.04 mmol/L LLL12with 0.1 mmol/L vandetanib) potentiated the inhibitory effect onboth canonical (pERK, pAKT) and noncanonical (pSTAT3) effec-tors of RET downstream signaling (Fig. 5D).

DiscussionThe current genomic era offers great opportunities to answer

many clinically relevant questions concerning various types ofcancer through comprehensive and integrative analyses of OMICdata. Some cancer types are being neglected by these efforts,generally due to their low prevalence, and thus small overallpublic health impact.MTC is oneof the least prevalent subtypes ofthyroid cancer but is responsible for a large proportion of thyroidcancer–related deaths (38), mainly because treatment with cyto-toxic drugs and/or standard radiotherapy has been proven to beineffective (39).

Using the largest cohort of MTC samples reported to date, wehave applied a multilayer OMIC data integration focused onthe effects of DNA methylation in the etiology of this tumor.The current work is the first to report not only that underlyinggenetics relates closely to genome-wide DNA methylation fin-gerprints but also that the aberrant methylation events affectspecific genomic loci (e.g., PolyComb targets) pointing to theexistence of an epigenetic progenitor cell signature in MTC(40). Furthermore, we identify both protein-coding and micro-RNA genes, the expression of which is negatively correlatedwith the methylation status of their promoters, and we confirmsome of these findings in MTC cell lines. We propose promotermethylation of MMP20, DKK4, and PLCB2 as functional epi-genetic markers of MTC genetic classes. The integrativeapproach allowed us to uncover the involvement of the JAK/Stat pathway in RETM918T-related MTCs, which apparentlysustains the proliferative features of the MTC cell model forthis genetic class.

DNA methylation is traditionally believed to be a regulatorof gene expression. In the case of protein-coding genes, theclassical view of the negative effect of methylation on expres-sion is being challenged by recent technical advances. Manycancer-orientated studies, including the current one, report asurprisingly low percentage of genes for which this inverserelationship applies (26). The situation with microRNAs iseven more complex, given the difficulties with identifying theirpromoter regions (41). Recently, a methodology became avail-able that recognizes putative promoter regions of miRNAsusing both sequence- and histone-based techniques (27). Byintegrating this strategy in our analysis, we were able to identifyCpGs belonging to additional putative miRNA promotersincluded on the 27K array. The overall portion of miRNA genesfor which a negative correlation between methylation andexpression was observed was similar to that for protein-codinggenes. Nevertheless, for the majority of the genes selected forvalidation of the inverse correlation between expression andmethylation in MTC cell lines, we were able to functionally






B

ICVANDETANIB 50 ( 95% CI (mol/L) mol/L)

MZ-CRC-1 0.62 0.48–0.79

TT 0.09 0.05–0.16

ICLLL12 50 ( 95% CI (mol/L) mol/L)

MZ-CRC-1 0.06 0.05–0.08

TT 0.23 0.21–0.25

A

C

Dp-STAT3 (Tyr705)

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LLL12Combination 0.04 µmol/L LLL12 + Vandetanib

p-AKT (Ser473)

GAPDH

1 1.38 0.170.72 0.69 0.66

0.420.970.190.560.961

0.110.670.320.781.431

p-ERK 1/2 (Tyr202/Tyr204)

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confirm the in silico predictions. One of these genes, DKK4, haspreviously been linked to MTC pathogenesis (18), and here wepropose aberrant methylation as the regulatory mechanismresponsible for its overexpression in RETM918T-related tumors.On the other hand, PLCB2 plays a role in multiple transmem-brane signal transduction pathways involving inositol lipidmetabolism. In cancer, its overexpression has been associatedwith mitosis promotion, migration, and poor outcome (31,42). Our finding of hypomethylation in the RETM918T-positivetumor samples, which was negatively associated with increasedPLCB2 expression, is consistent with this gene having a cancer-promoting role. Even though the prognostic utility of MMP20,a member of the family of metalloproteinases, has already beendemonstrated in cancer (32), we did not detect its expression inthe studied cell lines. Nevertheless, its validation in an inde-pendent series of MTC carcinomas confirms its potential utilityas a methylation marker of RETM918T-related tumors.

Regarding miRNA genes, the expression of miR-200c has beenrecently described as causative of the metastatic potential ofhumanMTCs (21). Interestingly, according to our results in MTCcell lines, the aberrant methylation of the putative promoter ofmiR-200c could underlie its differential expression amongpatients with distinct disease outcomes. MiR-10a mostly exertsan oncogenic effect (43, 44) andwe found little DNAmethylationof its promoter in both MTC cell lines. There is evidence fromhepatocellular carcinoma that miR-10a is negatively regulated byDNA methylation (45), which was further validated by ourfindings. MiR-30a warrants further study due to its tumor-sup-pressive features (34, 35). PROM1, a cancer stem cell markerfound overexpressed in RETM918T-positive tumors (18), is one ofits predicted targets, according to targetscan tool (http://www.targetscan.org/). Our findings indicate that a hypermethylation-driven downregulation of miR-30a could lead to an upregulationof its target PROM1.

Our integrated analysis pointed to the involvement of sig-naling networks such as cytokine–cytokine interaction and JAK/Stat in RETM918T tumors. These pathways have previously beenrelated to malignant tumor behavior, which is consistent withthe observation that these patients with MTC have the worstclinical outcomes (5). The genetic class–specific implication ofJAK/Stat pathways was confirmed by IHC in a large number ofhuman tumors. This is in line with previous in vitro findingsfrom 293T cells transfected with WT RET or RETM918T, whereonly the latter induced constitutive phosphorylation of STAT3.On top of that, RETM918T was far more efficient in recruitingSTAT3 protein than both RETC634 and wild-type RET in vitro

(46). Constitutive activation of STAT3 has been reported athigh frequency in large numbers of malignant cell lines, in vivoanimal models, and human tumors (reviewed in ref. 37). Ingeneral, activation of STAT3 is associated with a worse prog-nosis due to its role in uncontrollable proliferation and mod-ulation of microenvironment (reviewed in ref. 47). Thus,STAT3 is a very attractive molecule for targeted therapies(reviewed in ref. 48). Indeed, while no drug has shown prom-ising results in clinical trials (49), it has been suggested thatinhibition of STAT3 signaling in combination with other ther-apies (targeted therapies or radiotherapy) could lead to clinicalbenefit (50). We found that low doses of the inhibitor ofpSTAT3 increased the sensitivity of MTC cells to vandetanibtreatment. This could have an important impact on the man-agement of patients with MTC, as vandetanib treatment leads tofrequent toxicities, which require dose reductions or evendiscontinuation (8).

In summary, this comprehensive integrative genomic studyperformed using the largest cohort of MTC samples reportedto date, provides insights into the involvement of DNA meth-ylation in the etiology of this disease. We confirmed theregulatory role of DNA methylation for DKK4, PLCB2, MMP20,miR-10a, miR-30a, and miR-200c using MZ-CRC-1 and TT celllines. Moreover, hypomethylation may induce activation of keypathways related to the malignant behavior of RETM918T-relatedMTCs. Inhibition of one of these—the STAT3 signaling path-way—sensitizes RETM918T-mutated MTC cells to standard van-detanib treatment.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: V. Mancikova, C. Montero-Conde, M. RobledoDevelopment of methodology: V. Mancikova, C. Montero-Conde, J. Perales-Paton, E. Castelblanco, X. Matias-Guiu, M. RobledoAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): V. Mancikova, C. Montero-Conde, K. Jodkowska,E. Castelblanco, S. Borrego, M. Encinas, X. Matias-Guiu, M. RobledoAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): V. Mancikova, C. Montero-Conde, J. Perales-Paton,A. Fernandez, M. Santacana, L. Inglada-P�erez, X. Matias-Guiu, M. Fraga,M. RobledoWriting, review, and/or revision of themanuscript:V.Mancikova, C.Montero-Conde, K. Jodkowska, L. Inglada-Pwska, X. Matias-Guiu, M. Fraga, M. RobledoAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): V. Mancikova, X. Matias-Guiu, M. RobledoStudy supervision: M. Robledo

Figure 5.STAT3 signaling in RETM918T-related MTC. A, IHC study of 101 MTC samples. Staining was scored according to both extent and intensity. Each point represents onetumor staining result; bars represent the median value in a given genetic class. The most intense positive staining was detected among RETM918T-positiveMTCs (P ¼ 0.014). B, Effects of TKI and inhibitor of phosphorylation of STAT3 on MTC cell proliferation. MZ-CRC-1 and TT cell lines were treated with increasingconcentrations of vandetanib (TKI) or LLL12 (inhibitor of phosphorylation of STAT3) for 10 days. At day 10, the proliferation was assessed using WST-1reagent. Average values of triplicated experiments are presented with error bars representing the SD. Concentration (listed in mmol/L) leading to 50% of growthinhibition (IC50) and the 95% confidence interval (CI) were determined using GraphPad Prim. C, LLL12 sensitized MZ-CRC-1 cells to vandetanib treatment.MZ-CRC-1 cellswere treatedwith a fixed sub-IC50 concentration of LLL12 (0.04 mmol/L) combinedwith increasing concentrations of vandetanib, and the resultswerecompared with vandetanib and LLL12 monotherapy. The average values of triplicated experiments with the corresponding SD clearly showed an apparentlysynergistic effect of the 2 inhibitors.D,Western blot analysis of canonical (AKT, ERK) and noncanonical (STAT3) targets of RET signaling. Cell lysates from untreatedMZ-CRC-1 cells and those treated with increasing concentrations of vandetanib (0.01, 0.1, 0.5 mmol/L), 0.04 mmol/L LLL12, and a combination of LLL12 andvandetanib (0.04 and 0.1 mmol/L, respectively) were subjected to Western blot analyses for pERK, pAKT, pSTAT3, and GAPDH (loading control). Combinedtreatment with sub-IC50 concentrations of the two drugs potentiate the inhibition of effectors of RET downstream signaling.






AcknowledgmentsWe thankManuelMorente andMaría Jes�us Artiga from the SpanishNational

Tumor Bank Network (CNIO) for their hard work collecting the tumor samplesused in this study. We also thank Magdolna Djurec and Teresa Blasco for theirhelp with the Western blot analysis. The genotyping service was carried out atCEGEN-PRB2-ISCIII; it is supported by grant PT13/0001, ISCIII-SGEFI/FEDER.

Grant SupportThis work was supported by the Fondo de Investigaciones Sanitarias (FIS)

project PI14/00240, cofinanced by FEDER, and the Comunidad de Madrid(Grant S2011/BMD-2328 TIRONET) to M. Robledo. L. Inglada-P�erez is sup-

ported by the Centro de Investigacion Biom�edica en Red de Enfermedades Raras(CIBERER). V. Mancikova was supported by a predoctoral fellowship from the"la Caixa"/CNIO international PhD programme. C. Montero-Conde is sup-ported by a postdoctoral fellowship from the Fundaci�on AECC.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received April 13, 2016; revised August 5, 2016; accepted August 24, 2016;published OnlineFirst September 12, 2016.

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2017;23:1334-1345. Published OnlineFirst September 12, 2016.Clin Cancer Res Veronika Mancikova, Cristina Montero-Conde, Javier Perales-Paton, et al. Tumors

M918TRETSTAT3 Pathway as a Potential Therapeutic Target in Multilayer OMIC Data in Medullary Thyroid Carcinoma Identifies the

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Multilayer OMIC Data in Medullary Thyroid Carcinoma ... · Biology of Human Tumors Multilayer OMIC Data in Medullary Thyroid Carcinoma Identiﬁes the STAT3 Pathway as a Potential