Sellers, Thomas A.

Abstract Introduction: Identifying dysregulated gene expression in ovarian cancers has been limited by a deficit of available normal tissues. Here, we generated the largest set of high-grade serous ovarian cancer (HGSOC) tumors with normal precursor tissues for transcriptome analyses to identify lncRNA expression patterns, provide insight into their cellular contexts and functions, and assess their relevance to HGSOC pathogenesis and genetic susceptibility. Methods: We performed RNA sequencing on 220 primary HGSOCs and 116 benign epithelia (micro-dissected fallopian tube, ovarian surface, and inclusion cyst epithelia), and combined samples with 428 HGSOCs from TCGA, 60 HGSOCs from a prior study, and 180 bulk ovary tissues from GTEx. Reads were processed with a uniform bioinformatic and quality control pipeline, combined and batch corrected. Normalized TPM expression values were compared using the Wilcoxon rank sum test with >2-fold change and FDR <1% considered statistically significant. We computed weighted gene co-expression networks for each tissue type and tested for enrichment of differentially expressed genes (DEGs) and gene ontologies within co-expression modules. Results: Transcriptomes included 27,700 expressed genes (8,202 lncRNAs) across 706 HGSOCs, 180 bulk ovary, and 88 ovarian epithelia tissues. Most genes were expressed in all tissues, though 5% each showed HGSOC- or normal tissue-specific expression which comprised ≥50% lncRNA. Comparing HGSOCs to normal tissues identified 11,804 DEGs with 4,522 lncRNAs (DElncRNA) of which ~50% were tissue-specific. DEGs included MUC16 and multiple GWAS/TWAS implicated susceptibility genes including RAD51, BRIP1, BNC2, TIPARP-AS1, PRC1, KANSL1, ANKLE1, CHMP4C, ESRP2, and CCNE1. The most highly expressed DElncRNA in HGSOC were upregulated RMRP (P=1.4 × 10−39), SNHG1 (P=3.0 × 10−27), and HAGLR (P=2.0 × 10−24) at the HOXD risk locus. Lower proportions of lncRNA were seen in the HGSOC co-expression modules compared to those in normal tissues and less (3% vs. 15%) HGSOC modules were considered strongly lncRNA regulated (>50% lncRNA in module). Tissue-specific DElncRNA tended toward upregulation in HGSOC compared to ovarian epithelia with enriched modules associated with cell cycle regulation (P=1.5 × 10−38); and toward downregulation in HGSOC compared to bulk ovary with enriched modules associated with chemokine signaling/response (P=1.7 × 10−113). Conclusions: The lower proportion of lncRNA involved in HGSOC co-expression networks indicates a marked loss of regulation by lncRNA. Preliminary interrogation of HGSOC-dysregulated lncRNA expression revealed tissue-specific differences that highlight unique biological pathways in precursor epithelia and the ovarian microenvironment that contribute to HGSOC pathogenesis. Our results provide additional support for previously nominated risk genes. eQTL analysis has recently been completed and integration with GWAS are underway to elucidate lncRNA and lncRNA-regulated susceptibility genes and will be presented at the conference. Citation Format: Brett M. Reid, Ann Chen, Zhihua Chen, Florian A. Karreth, Peter Kanetsky, Jennifer B. Permuth, Ozlen Saglam, Jamie K. Teer, Xiaoqing Yu, Simon Gayther, Ellen Goode, Paul Pharoah, Thomas A. Sellers, Kate Lawrenson, Jonathan Tyrer. Patterns of lncRNA-regulated gene expression in high-grade serous ovarian carcinomas [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A004.

Abstract Purpose: Identifying dysregulated gene expression in ovarian cancers has been limited by a deficit of available normal tissues. Here, we generated the largest set of high-grade serous ovarian cancer (HGSOC) tumors with normal precursor tissues for transcriptome analyses. Methods: We performed RNA sequencing on 220 primary HGSOCs and 116 benign epithelia (micro-dissected fallopian tube, ovarian surface, and inclusion cysts), and combined samples with 428 HGSOCs from TCGA, 60 HGSOCs from a prior study, and 180 bulk ovary tissues from GTEx. Raw reads were processed with a uniform bioinformatic and quality control pipeline; combined data were batch corrected. We tested for differences in median normalized CPM expression values using the Wilcoxon rank sum test with >2-fold change and a false discovery rate <1% considered statistically significant. We also conducted weighted gene co-expression network analysis in each tissue type. The hypergeometric test was used for enrichment of differentially expressed genes (DEGs) and gene ontologies within modules. Results: Transcriptomes comprised 27,700 expressed genes (8,202 lncRNAs) across 706 HGSOCs, 180 bulk ovary, and 88 ovarian epithelia tissues. Most (~90%) genes were expressed in all tissues; 5% each showed HGSOC- or normal tissue-specific expression and ≥50% were lncRNA. Comparing HGSOCs to ovarian epithelia and to bulk ovary identified 11,804 DEGs with 4,522 lncRNAs (DElncRNA) of which ~50% were tissue-specific. DEGs included MUC16 and multiple GWAS/TWAS implicated susceptibility genes including RAD51, BRIP1, BNC2, TIPARP-AS1, PRC1, KANSL1, ANKLE1, CHMP4C, ESRP2, and CCNE1. The most highly expressed DElncRNA in HGSOC were upregulated RMRP (P=1.4x10-39), SNHG1 (P=3.0x10-27), and HAGLR (P=2.0x10-24) at the HOXD risk locus. The highest expressed DElncRNA in precursor tissues was the HGSOC-downregulated MEG3 (P=1.7x10-67). DEGs were enriched in HGSOC co-expression modules associated with immune response, cell motility/localization, cell cycle regulation, angiogenesis, reproductive development, transcriptional regulation, and metabolic processes. Tissue-specific DElncRNA tended toward upregulation compared to ovarian epithelia with enriched modules associated with cell cycle regulation (hub=BUB1B; top DElncRNA TRPM2-AS, P=1.5x10-38); and toward downregulation compared to bulk ovary with enriched modules associated with chemokine signaling/response (hub= GADD45B, top DElncRNA RP11-87P3.1, P=1.7x10-113). Conclusion: HGSOC-dysregulated lncRNA expression revealed tissue-specific differences that highlight unique biological pathways in precursor epithelia and the ovarian microenvironment that contribute to HGSOC pathogenesis. Our results provide additional evidence to support previously nominated risk genes. Integration with eQTL and GWAS are underway to further elucidate novel HGSOC susceptibility genes. Citation Format: Brett M. Reid, Ann Chen, Zhihua Chen, Florian A. Karreth, Peter Kanetsky, Jennifer B. Permuth, Ozlen Saglam, Jamie Teer, Xiaoqing Yu, Simon Gayther, Ellen Goode, Paul Pharoah, Thomas A. Sellers, Kate Lawrenson. Patterns of dysregulated coding and noncoding gene expression in high-grade serous ovarian carcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6065.

Abstract In 1997, the American Institute for Cancer Research (AICR) published 14 recommendations related to diet for individuals to reduce cancer incidence on a global basis; smoking was also discouraged. We operationalized these into nine recommendations that are particularly relevant to western populations in a cohort of 29,564 women ages 55 to 69 years at baseline in 1986 who had no history of cancer or heart disease. The cohort was followed through 1998 for cancer incidence (n = 4,379), cancer mortality (n = 1,434), cardiovascular disease (CVD) mortality (n = 1,124), and total mortality (n = 3,398). The median number (range) of recommendations followed was 4 (0-8), and 33% of the cohort had ever smoked. Women who followed no or one recommendation compared with six to nine recommendations were at an increased risk of cancer incidence [relative risk (RR) 1.35, 95% confidence interval (CI) 1.15-1.58] and cancer mortality (RR 1.43, 95% CI 1.11-1.85), but there was no association with CVD mortality (RR 1.06, 95% CI 0.78-1.43). We calculated the population attributable risk (PAR) to estimate the proportion of cancer incidence, cancer mortality, and CVD mortality that theoretically would have been avoidable if the entire cohort had never smoked, had followed six to nine recommendations, or had done both. The PARs for smoking were 11% (95% CI 10-13) for cancer incidence, 21% (95% CI 17-24) for cancer mortality, and 20% (95% CI 16-23) for CVD mortality. The PARs for not following six to nine recommendations were 22% (95% CI 12-30) for cancer incidence, 11% (95% CI −5 to 24) for cancer mortality, and 4% (95% CI −20 to 19) for CVD mortality. When smoking and the operationalized AICR recommendations were combined together, the PARs were 31% (95% CI 19-37) for cancer incidence, 30% (95% CI 15-40) for cancer mortality, and 22% (95% CI 4-36) for CVD mortality. These data suggest that the adherence to the AICR recommendations, independently and in conjunction with not smoking, is likely to have a substantial public health impact on reducing cancer incidence and, to a lesser degree, cancer mortality at the population level.

Abstract 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 (rG = 0.43, P = 2.66 × 10−5). We found seven loci associated with risk for both cancers (PBonferroni < 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.

Abstract Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3–T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.

CMML is a lethal myeloid neoplasm with no therapies that improve its dismal prognosis. Inhibition of BET family members has been proposed as a therapeutic strategy based on preclinical data identifying BRD4 as a therapeutic target in acute myeloid leukemia. However, despite potent on-target transcriptional remodeling, early phase clinical trials have demonstrated only modest activity secondary to a variety of resistance mechanisms. In ovarian cancer BET inhibitor (BETi) treated cells, compensatory upregulation and addiction to pro-survival kinase networks have been observed. Given that over 50% of CMML cases have mutations upregulating kinase signaling, we hypothesized that BETi resistance is mediated by these networks in CMML and can be targeted therapeutically. We tested this hypothesis by performing a limited screen of kinase inhibitors alone and in combination with the IC20 of the BETi INCB54329 in 8 human leukemia cell lines. This screen revealed that the IC50 of the PIM inhibitor (PIMi) INCB53914 decreased after co-treatment with BETi in a majority of the leukemia cell lines tested. Synergy was validated chemically in U937, TF1 and SKM1 leukemia cells using other selective inhibitors of BET and PIM. We next assessed the activity of the BET-PIM combination in 14-day colony formation assays with 10 unique CMML bone marrow mononuclear cell (BM-MNCs) patient samples(Fig. 1A). These studies revealed that combination therapy significantly suppressed clonogenicity versus BMNCs treated with vehicle or single drug alone. Finally, this synergy was validated in vivo in 36 patient derived xenografts (PDX) from 3 CMML patients, as manifest by reduced leukemic burden/engraftment in CMML PDX treated with combination therapy(Fig. 1B). To explore the mechanism by which BETi and PIMi therapeutically synergize we treated U937 and SKM1 leukemia cells with INCB54329 and measured mRNA and protein levels for all PIM isoforms. Surprisingly, we identified that PIM1 was increased following treatment with INCB54329, other BETi, or a JQ1-derived PROTAC (Fig. 1C). PIM1 upregulation was also manifest in INCB54329 persistor U937 leukemia cells generated by daily BETi treatment for 6 weeks. Testing across a broader panel of leukemia cell lines revealed an inverse correlation between PIM1 induction and decrease in the IC50 of PIMi following BETi treatment, suggesting PIM1 upregulation confers sensitivity to combination therapy. Consistent with this, isogenic SKM1 leukemia cells engineered to overexpress PIM1 were resistant to INCB54329 and were more sensitive to INCB53914 versus controls cells. Recent studies have demonstrated that inhibitory miRNAs, especially those located near super-enhancers, are suppressed by BET inhibition. Given that several miRNAs are known to control PIM1 expression, we hypothesized that paradoxical PIM1 upregulation following BETi treatment was due to down-regulation of select miRNAs. To test this, we treated our leukemia cell models with broad inhibitors of miRNA activity (i.e., AGO and Dicer inhibitors) and observed a dose dependent increase in PIM1 levels similar to that seen with BET inhibition(Fig. 1Di). Further, integrating public H3K27 CHIP-seq and miRNA super enhancer datasets and using computational prediction algorithms, we identified 6 candidate miRNAs that could regulate PIM1 and were predicted to be controlled by BET inhibitors. Of these, only miR-33a levels were reduced in a dose dependent manner in SKM1 cells by BETi treatment(Fig. 1Dii). This was confirmed by genetically silencing all BET proteins, which suppressed miR-33a levels in SKM1 leukemia cells. Finally, miR-33a mimics (but not control miRNAs) abolished BETi-induced upregulation of PIM1(Fig. 1Diii). Collectively, these studies established BET and PIM inhibition as a novel and potent combination therapy for CMML that is mediated by miR-33a-dependent upregulation of PIM1(Fig. 1E). Disclosures Liu: Incyte Corporation: Employment. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees. Lancet:Daiichi Sankyo: Consultancy, Other: fees for non-CME/CE services ; Agios, Biopath, Biosight, Boehringer Inglheim, Celator, Celgene, Janssen, Jazz Pharmaceuticals, Karyopharm, Novartis: Consultancy; Pfizer: Consultancy, Research Funding. Komrokji:Novartis: Speakers Bureau; JAZZ: Speakers Bureau; JAZZ: Consultancy; Agios: Consultancy; Incyte: Consultancy; DSI: Consultancy; pfizer: Consultancy; celgene: Consultancy. Epling-Burnette:Incyte Corporation: Research Funding. List:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Haura:Incyte Corporation: Research Funding. Reuther:Incyte Corporation: Research Funding. Koblish:Incyte Corporation: Employment.

Abstract Epithelial ovarian cancer (EOC) is the most common gynecological cancer, and ranks fifth in cancer deaths among women in the United States. Previous studies have identified several circulating proteins biomarkers for EOC risk, such as CA125 antigen and insulin-like growth factor-1 (IGF-1). However, previous studies on protein biomarkers for EOC were limited with small sample sizes, and only a few proteins investigated. In this study, we integrated genomics and proteomics data to search for novel circulating protein biomarkers for EOC risk. We constructed study instruments using genetic variants for protein quantitative trait loci (pQTL) for over 1,400 circulating proteins. Beta coefficients and standard errors of the selected pQTL variants in relation to EOC risk were obtained from 25,509 EOC cases and 40,941 controls from the Ovarian Cancer Association Consortium (OCAC). An inverse-variance weighted method was used to evaluate the association of these genetically predicted protein levels and risk of overall and serous EOC. We identified 35 protein biomarkers, for which their genetically predicted levels were significantly associated with the risk of either overall or serous EOC (false discovery rate <0.05). There are 25 proteins showing significant associations with both overall and serous EOC; most of the associations were stronger for serous EOC than overall EOC. The association for 31 proteins could be attributed, in part, to variants at ABO 9q34.2, a known ovarian cancer susceptibility locus. Five ABO locus-related proteins are in the PI3K-Akt signaling pathway, including insulin-like growth factor 1 receptor, hepatocyte growth factor receptor, interleukin-3 receptor subunit alpha, insulin receptor, and vascular endothelial growth factor receptor 2. Four of these proteins are also in the Rap1 and Ras signaling pathway. These five proteins are all inversely associated with the risk of overall EOC, with odds ratios ranging from 0.64 to 0.93 per unit increase in genetic risk scores (p values: 7.55×10-7 to 5.66×10-2). Four EOC associated proteins which were not influenced by variants in 9q34.2 are microfibrillar-associated protein 2, protein delta homolog 1, semenogelin-2, and netrin-G1, all of which showed significant associations with overall EOC, but not the serous subtype. Our study identifies potential novel biomarkers for ovarian cancer and suggests that genetic variants at locus 9q34.2 may play a role in the relation between levels of these proteins and risk of ovarian cancer. Citation Format: Guochong Jia, Xiang Shu, Xiao-Ou Shu, Jirong Long, Qiuyin Cai, Kirsten B. Moysich, Ellen L. Goode, Thomas A. Sellers, Wei Zheng. Genetically predicted concentration of circulating proteins and epithelial ovarian cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 598.

AbstractAn association between genetic variants in the vitamin D receptor (VDR) gene and epithelial ovarian cancer (EOC) was previously reported in women of African ancestry (AA). We sought to examine associations between genetic variants in VDR and additional genes from vitamin D biosynthesis and pathway targets (EGFR, UGT1A, UGT2A1/2, UGT2B, CYP3A4/5, CYP2R1, CYP27B1, CYP24A1, CYP11A1, and GC). Genotyping was performed using the custom‐designed 533,631 SNP Illumina OncoArray with imputation to the 1,000 Genomes Phase 3 v5 reference set in 755 EOC cases, including 537 high‐grade serous (HGSOC), and 1,235 controls. All subjects are of African ancestry (AA). Logistic regression was performed to estimate odds ratios (OR) and 95% confidence intervals (CI). We further evaluated statistical significance of selected SNPs using the Bayesian False Discovery Probability (BFDP). A significant association with EOC was identified in the UGT2A1/2 region for the SNP rs10017134 (per allele OR = 1.4, 95% CI = 1.2‐1.7, P = 1.2 × 10−6, BFDP = 0.02); and an association with HGSOC was identified in the EGFR region for the SNP rs114972508 (per allele OR = 2.3, 95% CI = 1.6‐3.4, P = 1.6 × 10−5, BFDP = 0.29) and in the UGT2A1/2 region again for rs1017134 (per allele OR = 1.4, 95% CI = 1.2‐1.7, P = 2.3 × 10−5, BFDP = 0.23). Genetic variants in the EGFR and UGT2A1/2 may increase susceptibility of EOC in AA women. Future studies to validate these findings are warranted. Alterations in EGFR and UGT2A1/2 could perturb enzyme efficacy, proliferation in ovaries, impact and mark susceptibility to EOC.

AbstractBackground:Germline DNA copy number variation (CNV) is a ubiquitous source of genetic variation and remains largely unexplored in association with epithelial ovarian cancer (EOC) risk.Methods:CNV was quantified in the DNA of approximately 3,500 cases and controls genotyped with the Illumina 610k and HumanOmni2.5M arrays. We performed a genome-wide association study of common (>1%) CNV regions (CNVRs) with EOC and high-grade serous (HGSOC) risk and, using The Cancer Genome Atlas (TCGA), performed in silico analyses of tumor-gene expression.Results:Three CNVRs were associated (P < 0.01) with EOC risk: two large (∼100 kb) regions within the 610k set and one small (<5 kb) region with the higher resolution 2.5M data. Large CNVRs included a duplication at LILRA6 (OR = 2.57; P = 0.001) and a deletion at CYP2A7 (OR = 1.90; P = 0.007) that were strongly associated with HGSOC risk (OR = 3.02; P = 8.98 × 10−5). Somatic CYP2A7 alterations correlated with EGLN2 expression in tumors (P = 2.94 × 10−47). An intronic ERBB4/HER4 deletion was associated with reduced EOC risk (OR = 0.33; P = 9.5 × 10−2), and somatic deletions correlated with ERBB4 downregulation (P = 7.05 × 10−5). Five CNVRs were associated with HGSOC, including two reduced-risk deletions: one at 1p36.33 (OR = 0.28; P = 0.001) that correlated with lower CDKIIA expression in TCGA tumors (P = 2.7 × 10−7), and another at 8p21.2 (OR = 0.52; P = 0.002) that was present somatically where it correlated with lower GNRH1 expression (P = 5.9 × 10−5).Conclusions:Though CNV appears to not contribute largely to EOC susceptibility, a number of low-to-common frequency variants may influence the risk of EOC and tumor-gene expression.Impact:Further research on CNV and EOC susceptibility is warranted, particularly with CNVs estimated from high-density arrays.

Abstract DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10−7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. Significance: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.