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Assessing the genetic architecture of epithelial ovarian cancer histological subtypes.

ABSTRACT: Epithelial ovarian cancer (EOC) is one of the deadliest common cancers. The five most common types of disease are high-grade and low-grade serous, endometrioid, mucinous and clear cell carcinoma. Each of these subtypes present distinct molecular pathogeneses and sensitivities to treatments. Recent studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC subtypes. To this end, we used data of 10,014 invasive EOC patients and 21,233 controls from the Ovarian Cancer Association Consortium genotyped in the iCOGS array (211,155 SNPs). We estimate the array heritability (attributable to variants tagged on arrays) of each subtype and their genetic correlations. We also look for genetic overlaps with factors such as obesity, smoking behaviors, diabetes, age at menarche and height. We estimated the array heritabilities of high-grade serous disease ([Formula: see text] = 8.8 ± 1.1 %), endometrioid ([Formula: see text] = 3.2 ± 1.6 %), clear cell ([Formula: see text] = 6.7 ± 3.3 %) and all EOC ([Formula: see text] = 5.6 ± 0.6 %). Known associated loci contributed approximately 40 % of the total array heritability for each subtype. The contribution of each chromosome to the total heritability was not proportional to chromosome size. Through bivariate and cross-trait LD score regression, we found evidence of shared genetic backgrounds between the three high-grade subtypes: serous, endometrioid and undifferentiated. Finally, we found significant genetic correlations of all EOC with diabetes and obesity using a polygenic prediction approach.

SUBMITTER: Cuellar-Partida G

PROVIDER: S-EPMC4976079 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Assessing the genetic architecture of epithelial ovarian cancer histological subtypes.

Cuellar-Partida Gabriel G Lu Yi Y Dixon Suzanne C SC Fasching Peter A PA Hein Alexander A Burghaus Stefanie S Beckmann Matthias W MW Lambrechts Diether D Van Nieuwenhuysen Els E Vergote Ignace I Vanderstichele Adriaan A Doherty Jennifer Anne JA Rossing Mary Anne MA Chang-Claude Jenny J Rudolph Anja A Wang-Gohrke Shan S Goodman Marc T MT Bogdanova Natalia N Dörk Thilo T Dürst Matthias M Hillemanns Peter P Runnebaum Ingo B IB Antonenkova Natalia N Butzow Ralf R Leminen Arto A Nevanlinna Heli H Pelttari Liisa M LM Edwards Robert P RP Kelley Joseph L JL Modugno Francesmary F Moysich Kirsten B KB Ness Roberta B RB Cannioto Rikki R Høgdall Estrid E Høgdall Claus C Jensen Allan A Giles Graham G GG Bruinsma Fiona F Kjaer Susanne K SK Hildebrandt Michelle A T MA Liang Dong D Lu Karen H KH Wu Xifeng X Bisogna Maria M Dao Fanny F Levine Douglas A DA Cramer Daniel W DW Terry Kathryn L KL Tworoger Shelley S SS Stampfer Meir M Missmer Stacey S Bjorge Line L Salvesen Helga B HB Kopperud Reidun K RK Bischof Katharina K Aben Katja K H KK Kiemeney Lambertus A LA Massuger Leon F A G LF Brooks-Wilson Angela A Olson Sara H SH McGuire Valerie V Rothstein Joseph H JH Sieh Weiva W Whittemore Alice S AS Cook Linda S LS Le Nhu D ND Blake Gilks C C Gronwald Jacek J Jakubowska Anna A Lubiński Jan J Kluz Tomasz T Song Honglin H Tyrer Jonathan P JP Wentzensen Nicolas N Brinton Louise L Trabert Britton B Lissowska Jolanta J McLaughlin John R JR Narod Steven A SA Phelan Catherine C Anton-Culver Hoda H Ziogas Argyrios A Eccles Diana D Campbell Ian I Gayther Simon A SA Gentry-Maharaj Aleksandra A Menon Usha U Ramus Susan J SJ Wu Anna H AH Dansonka-Mieszkowska Agnieszka A Kupryjanczyk Jolanta J Timorek Agnieszka A Szafron Lukasz L Cunningham Julie M JM Fridley Brooke L BL Winham Stacey J SJ Bandera Elisa V EV Poole Elizabeth M EM Morgan Terry K TK Goode Ellen L EL Schildkraut Joellen M JM Pearce Celeste L CL Berchuck Andrew A Pharoah Paul D P PD Webb Penelope M PM Chenevix-Trench Georgia G Risch Harvey A HA MacGregor Stuart S

Human genetics 20160413 7

Epithelial ovarian cancer (EOC) is one of the deadliest common cancers. The five most common types of disease are high-grade and low-grade serous, endometrioid, mucinous and clear cell carcinoma. Each of these subtypes present distinct molecular pathogeneses and sensitivities to treatments. Recent studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC sub ...[more]

PMID: 27075448

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Project description:An increasing body of evidence shows that miR-34 family has tumor suppressive properties mediating apoptosis, cell cycle arrest and senescence. In ovarian cancer, miR34 family members were found to be under expressed. Particularly miR-34a has been revealed to be a direct transcriptional target of p53 which is frequently mutated in epithelial ovarian carcinomas especially in high grade serous cancer. Moreover, methylation of miR-34a CpG Islands was found to down-regulate miR-34a expression. The aim of this study was to investigate the clinical relevance of mir34a as well as its promoter methylation in a subset of 133 ovarian cancers with a special focus on the p53 mutation status, the dualistic type I and type II ovarian cancer model and the different histotypes.One hundred thirty-three epithelial ovarian cancers and 8 samples of healthy ovarian surface epithelium were retrospectively analysed for miR-34a expression with quantitative real-time reverse transcription PCR (qRT-PCR). Gene-specific DNA methylation was evaluated with MethyLight technique.Significantly lower miR-34a expression was found in ovarian cancers than in healthy ovarian epithelium (p = 0.002). The expression of miR-34a was found lower in type II than in type I cancers (p = 0.037), in p53 mutated as compared to p53 wild type cancers (p = 0.003) and in high grade compared to in low grade cancers (p = 0.028). In multivariate COX regression model low expressing miR-34a cancers exhibited a reduced PFS (p = 0.039) and OS (p = 0.018). In serous cancers low miR-34a levels showed a worse OS confirmed also in multivariate analysis (p = 0.022). miR-34a promoter methylation was found higher in type II cancers than in type I (p = 0.006). mir34a expression and promoter methylation showed an inverse correlation in cancer samples (p = 0.05).We demonstrated a clinical independent role of miR-34a in epithelial ovarian cancers. Moreover, we corroborated the correlation between miR-34a expression and its promoter methylation in a large set of ovarian cancers. The inverse association between miR-34a expression and grading, p53 mutation status and dualistic tumor type classification, together with its prognostic relevance may underline the tumor-suppressive character of miR-34a in ovarian cancer.

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Assessing the genetic architecture of epithelial ovarian cancer histological subtypes.

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Assessing the genetic architecture of epithelial ovarian cancer histological subtypes.

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