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Tumor hypomethylation at 6p21.3 associates with longer time to recurrence of high-grade serous epithelial ovarian cancer.

ABSTRACT: To reveal biologic mechanisms underlying clinical outcome of high-grade serous (HGS) epithelial ovarian carcinomas (EOC), we evaluated the association between tumor epigenetic changes and time to recurrence (TTR). We assessed methylation at approximately 450,000 genome-wide CpGs in tumors of 337 Mayo Clinic (Rochester, MN) patients. Semi-supervised clustering of discovery (n=168) and validation (n=169) sets was used to determine clinically relevant methylation classes. Clustering identified two methylation classes based on 60 informative CpGs, which differed in TTR in the validation set [R vs. L class, P=2.9×10(-3), HR=0.52; 95% confidence interval (CI), 0.34-0.80]. Follow-up analyses considered genome-wide tumor mRNA expression (n=104) and CD8 T-cell infiltration (n=89) in patient subsets. Hypomethylation of CpGs located in 6p21.3 in the R class associated with cis upregulation of genes enriched in immune response processes (TAP1, PSMB8, PSMB9, HLA-DQB1, HLA-DQB2, HLA-DMA, and HLA-DOA), increased CD8 T-cell tumor infiltration (P=7.6×10(-5)), and trans-regulation of genes in immune-related pathways (P=1.6×10(-32)). This is the most comprehensive assessment of clinical outcomes with regard to epithelial ovarian carcinoma tumor methylation to date. Collectively, these results suggest that an epigenetically mediated immune response is a predictor of recurrence and, possibly, treatment response for HGS EOC.

SUBMITTER: Wang C

PROVIDER: S-EPMC4054691 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Tumor hypomethylation at 6p21.3 associates with longer time to recurrence of high-grade serous epithelial ovarian cancer.

Wang Chen C Cicek Mine S MS Charbonneau Bridget B Kalli Kimberly R KR Armasu Sebastian M SM Larson Melissa C MC Konecny Gottfried E GE Winterhoff Boris B Fan Jian-Bing JB Bibikova Marina M Chien Jeremy J Shridhar Viji V Block Matthew S MS Hartmann Lynn C LC Visscher Daniel W DW Cunningham Julie M JM Knutson Keith L KL Fridley Brooke L BL Goode Ellen L EL

Cancer research 20140411 11

To reveal biologic mechanisms underlying clinical outcome of high-grade serous (HGS) epithelial ovarian carcinomas (EOC), we evaluated the association between tumor epigenetic changes and time to recurrence (TTR). We assessed methylation at approximately 450,000 genome-wide CpGs in tumors of 337 Mayo Clinic (Rochester, MN) patients. Semi-supervised clustering of discovery (n=168) and validation (n=169) sets was used to determine clinically relevant methylation classes. Clustering identified two ...[more]

PMID: 24728075

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Project description:BackgroundEpithelial ovarian cancer (EOC) is one of the most lethal gynecological cancers; the majority of EOC is the serous histotype and diagnosed at advanced stage. IL6 is the cytokine that has been found most frequently associated with carcinogenesis and progression of serous EOCs. IL6 is a growth-promoting and anti-apoptotic factor, and high plasma levels of IL6 in advanced stage EOCs correlate with poor prognosis. The objective of the present study was to identify IL6 co-regulated genes and gene network/s in EOCs.ResultsWe applied bioinformatics tools on 7 publicly available data sets containing the gene expression profiles of 1262 EOC samples. By Pearson's correlation analysis we identified, in EOCs, an IL6-correlated gene signature containing 40 genes mainly associated with proliferation. 33 of 40 genes were also significantly correlated in low malignant potential (LMP) EOCs, while 7 genes, named C5AR1, FPR1, G0S2, IL8, KLF2, MMP19, and THBD were IL6-correlated only in advanced stage EOCs. Among the 40-gene signature EGFR ligand HBEGF, genes of the EGR family members and genes encoding for negative feedback regulators of growth factor signaling were included. The results obtained by Gene Set Enrichment and Ingenuity Pathway Analyses enabled the identification, respectively, of gene sets associated with 'early growth factor response' for the 40-gene signature, and a biological network related to 'thrombosis and cardiovascular disease' for the 7-gene signature. In agreement with these results, selected genes from the identified signatures were validated in vitro by real time RT-PCR in serous EOC cell lines upon stimulation with EGF.ConclusionsSerous EOCs, independently of their aggressiveness, co-regulate IL6 expression together with that of genes associated to growth factor signaling, arguing for the hypothesis that common mechanism/s driven by EGFR ligands characterize both advanced-stage and LMP EOCs. Only advanced-stage EOCs appeared to be characterized by a scenario that involves genes which are so far associated with thrombosis and cardiovascular disease, thus suggesting that this pathway is implicated in the growth and/or spread of more aggressive tumors. We have discovered novel activated signaling pathways that drive the expression of IL6 and of co-regulated genes and are possibly involved in the pathobiology of EOCs.

Project description:BackgroundSerous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ~100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS).Methodology/principal findingsWe implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).Conclusions/significanceWe provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.

Dataset Information

Tumor hypomethylation at 6p21.3 associates with longer time to recurrence of high-grade serous epithelial ovarian cancer.

Publications

Tumor hypomethylation at 6p21.3 associates with longer time to recurrence of high-grade serous epithelial ovarian cancer.

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