Bowtell, David D.L.

AbstractBackgroundKnown risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort.MethodsSingle nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer–related cell types.ResultsWe identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types.ConclusionsCNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.

Abstract Ovarian carcinosarcoma (OCS) is an aggressive and rare tumor type with limited treatment options. OCS is hypothesized to develop via the combination theory, with a single progenitor resulting in carcinomatous and sarcomatous components, or alternatively via the conversion theory, with the sarcomatous component developing from the carcinomatous component through epithelial-to-mesenchymal transition (EMT). In this study, we analyzed DNA variants from isolated carcinoma and sarcoma components to show that OCS from 18 women is monoclonal. RNA sequencing indicated that the carcinoma components were more mesenchymal when compared with pure epithelial ovarian carcinomas, supporting the conversion theory and suggesting that EMT is important in the formation of these tumors. Preclinical OCS models were used to test the efficacy of microtubule-targeting drugs, including eribulin, which has previously been shown to reverse EMT characteristics in breast cancers and induce differentiation in sarcomas. Vinorelbine and eribulin more effectively inhibited OCS growth than standard-of-care platinum-based chemotherapy, and treatment with eribulin reduced mesenchymal characteristics and N-MYC expression in OCS patient-derived xenografts. Eribulin treatment resulted in an accumulation of intracellular cholesterol in OCS cells, which triggered a downregulation of the mevalonate pathway and prevented further cholesterol biosynthesis. Finally, eribulin increased expression of genes related to immune activation and increased the intratumoral accumulation of CD8+ T cells, supporting exploration of immunotherapy combinations in the clinic. Together, these data indicate that EMT plays a key role in OCS tumorigenesis and support the conversion theory for OCS histogenesis. Targeting EMT using eribulin could help improve OCS patient outcomes. Significance: Genomic analyses and preclinical models of ovarian carcinosarcoma support the conversion theory for disease development and indicate that microtubule inhibitors could be used to suppress EMT and stimulate antitumor immunity.

Abstract Background: There is suggestive evidence that inflammation is related to ovarian cancer survival. However, more research is needed to identify inflammation-related factors that are associated with ovarian cancer survival and to determine their combined effects. Methods: This analysis used pooled data on 8,147 women with invasive epithelial ovarian cancer from the Ovarian Cancer Association Consortium. The prediagnosis inflammation-related exposures of interest included alcohol use; aspirin use; other nonsteroidal anti-inflammatory drug use; body mass index; environmental tobacco smoke exposure; history of pelvic inflammatory disease, polycystic ovarian syndrome, and endometriosis; menopausal hormone therapy use; physical inactivity; smoking status; and talc use. Using Cox proportional hazards models, the relationship between each exposure and survival was assessed in 50% of the data. A weighted inflammation-related risk score (IRRS) was developed, and its association with survival was assessed using Cox proportional hazards models in the remaining 50% of the data. Results: There was a statistically significant trend of increasing risk of death per quartile of the IRRS [HR = 1.09; 95% confidence interval (CI), 1.03–1.14]. Women in the upper quartile of the IRRS had a 31% higher death rate compared with the lowest quartile (95% CI, 1.11–1.54). Conclusions: A higher prediagnosis IRRS was associated with an increased mortality risk after an ovarian cancer diagnosis. Further investigation is warranted to evaluate whether postdiagnosis exposures are also associated with survival. Impact: Given that pre- and postdiagnosis exposures are often correlated and many are modifiable, our study results can ultimately motivate the development of behavioral recommendations to enhance survival among patients with ovarian cancer.

Abstract In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved. In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages. In a cohort of 12 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic “scarring,” indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after six lines of therapy (four platinum-based), whereas another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neoadjuvant chemotherapy. As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy. Significance: Homozygous RAD51C methylation is a positive predictive biomarker for sensitivity to PARP inhibitors, whereas a single unmethylated gene copy is sufficient to confer resistance.

Abstract A majority of patients with high-grade serous ovarian cancer (HGSC) develop progressive disease following primary treatment, with a five-year survival rate of approximately 40%, and less than 10% survive more than 10 years. Profound genomic instability, treatment with DNA-damaging drugs, and a large tumor burden promote the development of acquired resistance in a large proportion of patients, including multiple convergent resistant events within individuals. Despite the propensity of HGSC to develop drug resistance, a small proportion of patients with advanced disease at diagnosis become long-term survivors. These exceptional patients include those who had suboptimal surgical clearance, and therefore were not cured surgically, and others who have had no evidence of disease recurrence following initial surgery and adjuvant chemotherapy. Our study, which is part of the international collaborative Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG), seeks to identify molecular, immunologic, and epidemiologic factors that influence long-term survival in HGSC. This presentation describes our genomic and immunologic analysis of exceptional survival of this deadly disease. Patient characteristics and clinical histories were evaluated to identify patients diagnosed with advanced stage (Stage IIIC/IV) and histopathologically confirmed HGSC with greater than 10-year overall survival. Whole-genome sequencing (WGS) was performed on primary tumors (median 78x coverage) and germline samples (median 39x coverage) of 55 long-term survivors. Primary tumor samples were also characterized by RNA sequencing, DNA methylation profiling, and immunohistochemistry. Thirty-eight (69%) of long-term surviving patients had residual disease following surgery, suggestive of highly chemosensitive disease. Most patients (41, 75%) were alive at last follow-up and 26 (47%) were progression free. Somatic mutation burden was higher in primary tumors of long-term survivors relative to controls. Genome-wide mutational signatures were predominantly Signature 3 (associated with homologous recombination deficiency), Signature 1 (age related), and Signatures 5, 8, and 16 (unknown etiology). Inactivation of the tumor suppressor RB1 by structural rearrangements or homozygous deletion was frequent in long-term survivors, with 33% of tumors showing associated loss of RB1 protein expression by immunohistochemistry compared to 13% of unselected HGSC controls (n=207; P = 0.001). In an independent HGSC cohort (n=847), RB1 protein loss was associated with prolonged survival (HR: 0.75, P < 0.001) compared to patients with RB1-positive tumors. Furthermore, co-occurrence of germline mutations in BRCA1 or BRCA2 and RB1 loss was associated with a significantly longer overall survival compared to patients with retained RB1 protein expression and no germline BRCA mutation (HR: 0.44, P < 0.001). Multiplexed immunohistochemical analysis with immune cell panels demonstrated distinct associations with long-term survivors. This study delineates the full landscape of genomic alterations in HGSC of long-term survivors. Our findings indicate that specific mutations are associated with enhanced host immune responses and long-term survival. Citation Format: Dale Garsed, Ahwan Pandey, Sian Fereday, Kathryn Alsop, Maartje Wouters, Flurina Saner, Catherine Kennedy, Celeste Pearce, Malcolm Pike, Susan Ramus, Martin Kobel, Anna deFazio, Ellen Goode, Brad Nelson, David Bowtell. Molecular analysis of exceptional response in high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA05.

Abstract Improving risk prediction and prevention strategies through identifying new susceptibility genes for non-high-grade serous ovarian cancer (non-HGS) would represent an important advance in reducing incidence and mortality. Epithelial ovarian cancer (EOC) has five main histotypes that have distinct pathologies, molecular changes, clinical characteristics, and tissues of origin. They are classified as high-grade serous (HGS), low-grade serous (LGS), clear-cell (CCC), endometrioid (END), and mucinous (MUC). A family history of breast or ovarian cancer is the strongest single risk factor for EOC. Germline mutations in the high-penetrance susceptibility genes BRCA1 and BRCA2 confer EOC risks of 44% and 17% by age 80, respectively. Only 40% of the excess familial risk is known, suggesting there are many other susceptibility genes yet unidentified. The non-HGS histotypes are poorly studied due to limited sample size within individual studies. Thus, families of cases with non-HGS tumors have few risk prediction options. Whole-exome sequencing (WES) analysis was performed on 251 non-HGS cases (56 LGS, 55 CCC, 117 END, 23 MUC). Cases were screened negative for BRCA1/2 mutations and selected for a family history of ovarian or breast cancer, or young onset (<45 years) of ovarian cancer. WES identified 1,278 genes with rare predicted truncating mutation in at least one case. Predicted truncating mutations in cases were compared to 171,584 controls from the Exome Aggregation Consortium (ExAC) and the Genome Aggregation Database (gnomAD). Gene-set enrichment analysis showed enrichment for genes involved in the DNA repair pathway (p=3.08 × 10−7). Twenty-five candidate genes (including 4 DNA repair genes) were selected for validation by targeted sequencing. Five genes important in HGS (BRIP1, FANCM, PALB2, RAD51C, RAD51D) as well as two additional candidates (XRCC2 and XRCC3) were also sequenced. Targeted sequencing was performed on 1,779 cases (669 END, 356 CCC, 327 LGS, 427 MUC) and 1,863 controls from studies in the Ovarian Cancer Association Consortium (OCAC). Library preparation and target enrichment was performed using the Fluidigm Juno System and Illumina sequencing was performed on a NovaSeq 6000 Sequencing System. A case-control analysis of predicted truncating variants in the 32 genes was performed. Using a minimum alternate allele frequency of 30%, we identified a higher frequency of mutations in non-HGS cases than controls in ERCC6 (p=0.034) and IL31RA (p=0.034). ERCC6 is involved in DNA repair and IL31RA is a cytokine receptor. Sanger sequencing validation of variants to confirm these results is ongoing. Several other genes showed suggestive higher frequencies of mutations in cases than controls. A larger case control analysis will be performed to confirm those findings. Identifying novel susceptibility genes for non-HGS may have clinical impact by reducing disease-associated mortality through improving risk prediction, identifying prevention strategies, and developing new targeted treatments. Citation Format: Marina Pavanello, Ed Dicks, Honglin Song, Amir Ariff, Adelyn Bolithon, Maria P. Intermaggio, Mark Pinese, Kirsten Moysich, Kunle O. Odunsi, Ellen Goode, David D. Bowtell, Peter Fasching, Jennifer A. Doherty, Francesmary Modugno, Susanne K. Kjær, Penelope M. Webb, Anna Wu, Anna deFazio, Ovarian Cancer Association Consortium, Paul James, Deepak Subramanian, Ian Campbell, Simon A. Gayther, Paul D.P. Pharoah, Susan J. Ramus. Germline mutations in new susceptibility genes for non-high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B37.

Abstract Background: High-grade serous ovarian cancer (HGSOC) is the most common and aggressive epithelial ovarian cancer. HGSOC has poor survival, with five-year survival of less than 40%, as it is often diagnosed at a late stage. The median overall survival (OS) is approximately four years. Prognostic gene expression signatures for HGSOC have been inconsistent and have not been translated into clinical practice. The aim of this study was to develop a robust prognostic signature for HGSOC, in order to identify high-risk patients requiring alternative treatments. Methods: A meta-analysis of 1,455 tumors was performed and 200 prognostic genes were selected for expression profiling. An additional 315 candidate genes were selected from the literature, drug-targetable pathways, and other hypotheses. NanoString expression analysis of the 513 genes was performed on formalin-fixed, paraffin-embedded (FFPE) tumor tissue from 3,769 women with HGSOC from the Ovarian Tumor Tissue Analysis (OTTA) Consortium. A prognostic model for 5-year survival was developed using regression-based and machine learning methods. The model was trained on 2,702 tumors and evaluated on an independent set of 1,067 tumors. A NanoString expression analysis was performed on 2,692 additional tumors, for a subset of 218 of the genes, including the prognostic signature. Results: Expression of 276 genes was associated with OS (false discovery rate (FDR) < 0.05) in single-gene analyses adjusted for age, stage, and race. The top five genes were TAP1, ZFHX4, CXCL9, FBN1, and PTGER3 (p << 0.001). A 101-gene prognostic signature was established, where a gain of one standard deviation in the gene expression score conferred a greater than two-fold increase in risk of death (HR (hazard ratio) = 2.35 [2.02, 2.71]; p << 0.001). Median survival by quintile group was 9.5, 5.4, 3.8, 3.2, and 2.3 years. Sensitivity analyses, applying the signature to specific patient groups, showed that the prognostic power of the signature is not explained by residual disease, treatment, BRCA status, or CD8 score. The prognostic signature was enriched in pathways with treatment implications, such as PI3K, GPCR, and immune. This signature is suitable for use in FFPE tumor material and future cases can be assigned an HR from the gene expression score. The signature was developed in cases that did not receive neoadjuvant treatment (NACT); however, application to NACT cases was assessed in the second NanoString expression analysis. Conclusion: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium – Stratified Prognosis of Ovarian Tumors) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. Citation Format: Joshua Millstein, Timothy Budden, Ellen L. Goode, Michael S. Anglesio, Aline Talhouk, OTTA consortium, David G. Huntsman, David D. Bowtell, James D. Brenton, Jennifer A. Doherty, Paul P.D. Pharoah, Susan J. Ramus. The Ovarian Tumour Tissue Analysis Consortium: Stratified Prognosis of Ovarian Tumors (OTTA-SPOT) signature for high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA12.

Abstract Background: Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC) are distinguished by differential immune and stromal infiltration and may provide opportunities for targeted therapies. Integration of molecular subtypes into clinical trials has been hindered by inconsistent subtyping methodology. Methods: Adopting two independent approaches, we derived and internally validated algorithms for molecular subtype prediction from gene-expression array data in 1,650 tumors. We applied resulting models to assign labels to 3829 HGSOCs from the Ovarian Tumor Tissue Analysis (OTTA) consortium evaluated on NanoString. Using the labeled NanoString data, we developed, confirmed, and validated a minimal gene set, clinical-grade test and prediction tool. We also used the OTTA dataset to evaluate associations between molecular subtype, biologic, and clinical features. Findings: The locked-down test included a model with 55 genes that predicted HGSOC molecular subtype with >95% accuracy. Subtype varied between primary and metastatic site taken at the time of primary surgery, and was significantly associated with age, stage, CD8+ lymphocyte infiltration, residual disease, and outcome. In multivariable models, molecular subtypes lose their prognostic significance in the presence of risk factors such as residual disease and BRCA1/2 germline mutations. Interpretation: We validated the Predictor of high-grade-serous Ovarian carcinoma molecular subTYPE, or PrOTYPE, following the Institute of Medicine guidelines for the development of omics-based tests. This simple-to-use, cost-effective, fully defined, and locked-down clinical-grade assay will facilitate molecular subtype stratification into clinical trial design. PrOTYPE will allow for objective assessment of HGSOC molecular subtype predictive value in precision medicine applications. Citation Format: Aline Talhouk, Joshy George, Chen Wang, Ellen Goode, Susan Ramus, Jennifer Doherty, David Bowtell, Michael Anglesio, OTTA Consortium. PrOTYPE (Predictor of high-grade-serous Ovarian carcinoma molecular subTYPE): The development and validation of a clinical-grade consensus classifier for the molecular subtypes of high-grade serous tubo-ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A03.

Abstract 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

Purpose The known epithelial ovarian cancer (EOC) susceptibility genes account for less than 50% of the heritable risk of ovarian cancer suggesting that other susceptibility genes exist. The aim of this study was to evaluate the contribution to ovarian cancer susceptibility of rare deleterious germline variants in a set of candidate genes. Methods We sequenced the coding region of 54 candidate genes in 6385 invasive EOC cases and 6115 controls of broad European ancestry. Genes with an increased frequency of putative deleterious variants in cases versus controls were further examined in an independent set of 14 135 EOC cases and 28 655 controls from the Ovarian Cancer Association Consortium and the UK Biobank. For each gene, we estimated the EOC risks and evaluated associations between germline variant status and clinical characteristics. Results The ORs associated for high-grade serous ovarian cancer were 3.01 for PALB2 (95% CI 1.59 to 5.68; p=0.00068), 1.99 for POLK (95% CI 1.15 to 3.43; p=0.014) and 4.07 for SLX4 (95% CI 1.34 to 12.4; p=0.013). Deleterious mutations in FBXO10 were associated with a reduced risk of disease (OR 0.27, 95% CI 0.07 to 1.00, p=0.049). However, based on the Bayes false discovery probability, only the association for PALB2 in high-grade serous ovarian cancer is likely to represent a true positive. Conclusions We have found strong evidence that carriers of PALB2 deleterious mutations are at increased risk of high-grade serous ovarian cancer. Whether the magnitude of risk is sufficiently high to warrant the inclusion of PALB2 in cancer gene panels for ovarian cancer risk testing is unclear; much larger sample sizes will be needed to provide sufficiently precise estimates for clinical counselling.