Background

Mucinous ovarian tumors represent a distinct histotype of epithelial ovarian cancer. The rarest (2-4 % of ovarian carcinomas) of the five major histotypes, their genomic landscape remains poorly described. We undertook hotspot sequencing of 50 genes commonly mutated in human cancer across 69 mucinous ovarian tumors. Our goals were to establish the overall frequency of cancer-hotspot mutations across a large cohort, especially those tumors previously thought to be "RAS-pathway alteration negative", using highly-sensitive next-generation sequencing as well as further explore a small number of cases with apparent heterogeneity in RAS-pathway activating alterations.

Methods

Using the Ion Torrent PGM platform, we performed next generation sequencing analysis using the v2 Cancer Hotspot Panel. Regions of disparate ERBB2-amplification status were sequenced independently for two mucinous carcinoma (MC) cases, previously established as showing ERBB2 amplification/overexpression heterogeneity, to assess the hypothesis of subclonal populations containing either KRAS mutation or ERBB2 amplification independently or simultaneously.

Results

We detected mutations in KRAS, TP53, CDKN2A, PIK3CA, PTEN, BRAF, FGFR2, STK11, CTNNB1, SRC, SMAD4, GNA11 and ERBB2. KRAS mutations remain the most frequently observed alteration among MC (64.9 %) and mucinous borderline tumors (MBOT) (92.3 %). TP53 mutation occurred more frequently in carcinomas than borderline tumors (56.8 % and 11.5 %, respectively), and combined IHC and mutation data suggest alterations occur in approximately 68 % of MC and as many as 20 % of MBOT. Proven and potential RAS-pathway activating changes were observed in all but one MC. Concurrent ERBB2 amplification and KRAS mutation were observed in a substantial number of cases (7/63 total), as was co-occurrence of KRAS and BRAF mutations (one case). Microdissection of ERBB2-amplified regions of tumors harboring KRAS mutation suggests these alterations are occurring in the same cell populations, while consistency of KRAS allelic frequency in both ERBB2 amplified and non-amplified regions suggests this mutation occurred in advance of the amplification event.

Conclusions

Overall, the prevalence of RAS-alteration and striking co-occurrence of pathway "double-hits" supports a critical role for tumor progression in this ovarian malignancy. Given the spectrum of RAS-activating mutations, it is clear that targeting this pathway may be a viable therapeutic option for patients with recurrent or advanced stage mucinous ovarian carcinoma, however caution should be exercised in selecting one or more personalized therapeutics given the frequency of non-redundant RAS-activating alterations.

Endometriosis is a significant risk factor for clear cell and endometrioid ovarian cancers and is often found contiguous with these cancers. Using whole-genome shotgun sequencing of seven clear cell ovarian carcinomas (CCC) and targeted sequencing in synchronous endometriosis, we have investigated how this carcinoma may evolve from endometriosis. In every case we observed multiple tumour-associated somatic mutations in at least one concurrent endometriotic lesion. ARID1A and PIK3CA mutations appeared consistently in concurrent endometriosis when present in the primary CCC. In several cases, one or more endometriotic lesions carried the near-complete complement of somatic mutations present in the index CCC tumour. Ancestral mutations were detected in both tumour-adjacent and -distant endometriotic lesions, regardless of any cytological atypia. These findings provide objective evidence that multifocal benign endometriotic lesions are clonally related and that CCCs arising in these patients progress from endometriotic lesions that may already carry sufficient cancer-associated mutations to be considered neoplasms themselves, albeit with low malignant potential. We speculate that genomically distinct classes of endometriosis exist and that ovarian endometriosis with high mutational burden represents one class at high risk for malignant transformation.

Purpose

Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features.

Experimental design

Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting.

Results

Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations.

Conclusions

We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.

Abstract: Accumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10−7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.

Background

Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers.

Methods

Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data.

Results

Genetic correlation analysis revealed significant genetic correlation between the two cancers (r_G = 0.43, P = 2.66 × 10^-5). We found seven loci associated with risk for both cancers (P _Bonferroni < 2.4 × 10^-9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10^-7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation.

Conclusions

Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis.

Impact

Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.