Raghavan, Rama

Abstract Background: Endometrioid carcinoma (EC) and clear cell carcinoma (CC) histotypes of epithelial ovarian cancer are understudied compared with the more common high-grade serous carcinomas (HGSC). We therefore sought to characterize EC and CC transcriptomes in relation to HGSC. Methods: Following bioinformatics processing and gene abundance normalization, differential expression analysis of RNA sequence data collected on fresh-frozen tumors was completed with nonparametric statistical analysis methods (55 ECs, 19 CCs, 112 HGSCs). Association of gene expression with progression-free survival (PFS) was completed with Cox proportional hazards models. Eight additional multi-histotype expression array datasets (N = 852 patients) were used for replication. Results: In the discovery set, tumors generally clustered together by histotype. Thirty-two protein-coding genes were differentially expressed across histotype (P < 1 × 10−10) and showed similar associations in replication datasets, including MAP2K6, KIAA1324, CDH1, ENTPD5, LAMB1, and DRAM1. Nine genes associated with PFS (P < 0.0001) showed similar associations in replication datasets. In particular, we observed shorter PFS time for CC and EC patients with high gene expression for CCNB2, CORO2A, CSNK1G1, FRMD8, LIN54, LINC00664, PDK1, and PEX6, whereas, the converse was observed for HGSC patients. Conclusions: The results suggest important histotype differences that may aid in the development of treatment options, particularly those for patients with EC or CC. Impact: We present replicated findings on transcriptomic differences and how they relate to clinical outcome for two of the rarer ovarian cancer histotypes of EC and CC, along with comparison with the common histotype of HGSC. Cancer Epidemiol Biomarkers Prev; 27(9); 1101–9. ©2018 AACR.

Abstract Background: Little transcriptomic research has compared epithelial ovarian cancer (EOC) histological subtypes. We set out to characterize the transcriptomes of high-grade serous carcinomas (HGSC) and endometrioid carcinomas (EC), which make up around 70% and 20% of EOC tumors, respectively, and have some histopathological similarities. Methods: Fresh frozen tumors from EOC patients seen at the Mayo Clinic (30 EC and 62 HGSC) were used. 1ug RNA riboZero was used for library preparation using the Illumina TruSeq kit and sequenced on a HiSeq 2000 machine. Reads were aligned using TopHat2 followed by quantification of abundances using RSEM and differential expression analysis with edgeR. We analyzed transcriptomes, conducted pathway analyses, and summarized key candidate gene sets. Expressed SNVs (eSNVs) from the RNA-seq data were determined using GATK and RVboost. Results: The analysis found 699 genes with FDR < 1×10-5 for differential expression between HGSC and EC, with most genes being up-regulated in EC. The top most associated genes were TPH1, MAP2K6, KLK2, ADAM23, TESC and TRAF3IP2 (p<10-22). Pathway analysis of the genes up-regulated in EC revealed enrichment of the “basal cell carcinoma signaling pathway” (p = 1.2×10-5). Within 1 Mb of the 25 known EOC risk loci, we observed higher expression in HGSC for RSPO1 and HPSE (p <5×10-7). For genes functionally related to EOC, we observed in HGSC up-regulation (*p < 10-5, ⁁p<0.003) for FOXM1⁁, CDKN2A⁁, CCNE1*, CCND2⁁, PIK3CA*, BRCA2⁁, BIRC5⁁, MMP9⁁, FANCD2⁁, and MAML2⁁. In contrast, we found up-regulation in EC for MDM2*, KLK4*, BCL2⁁, CCND1*, ANXA4*, CDH1⁁, MMP7⁁, and MAML3⁁. We also identified 204 eSNVs (44 non-synonymous) associated with EC v HGSC subtype (p<10-4); this included an exonic TRAF3IP2 eSNV (66% EC, 13% HGSC, p = 4×10-7, chr6:111877117). Discussion: Using one of the largest sets of identically processed fresh-frozen EOC tumors, some patterns emerged among the numerous EC v HGSC transcriptomic differences. TPH1, up-expressed in EC, is regulated by SOX4 which was also up-regulated in EC. Two sets of genes related to Kallikreins serine proteases were differentially expressed, including KLK2 which is known to regulate EGFR and pro-inflammatory cytokines and is regulated by MYC. Lastly, TRAF3IP2 encodes for a protein involved in regulating cytokines through members of the NFKB pathway. Conclusions: These findings suggest important biological insights into one of the rarer EOC histologies and may aid in the development of targeted treatment options. Research is on-going to incorporate additional features (e.g., DNA methylation, copy number) into a “systems biology” framework to better understand the molecular differences between EOC histologies. Citation Format: Brooke L. Fridley, Junqiang Dai, Rama Raghavan, Chen Wang, Pengcheng Lu, Stacey Winham, Madalene Earp, Kate Lawrenson, Simon A. Gayther, Kimberly R. Kalli, Ellen L. Goode. Transcriptome characterization of high grade serous and endometrioid epithelial ovarian cancer tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1833.

Abstract Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene–environment interactions related to hormone-related risk factors could differ between obese and non-obese women. Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case–control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10−6) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10−5). The most notable obesity–gene–hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10−6). Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions. Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780–90. ©2016 AACR.

Abstract Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5% of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, even with modern chemotherapy, most patients relapse and die with the five-year overall survival around 45%. In addition, those patients that initially respond to taxane-platinum therapy eventually develop platinum-resistant tumors and relapse. Thus, finding novel therapeutics for treating EOC is essential. One approach that has been used widely in cancer drug discovery is Connectivity Mapping (CMAP) using gene expression data and drug phenotype data assessed on cancer cell lines (CCL) (i.e. “sensitive” or “resistant” based on dose-response drug data). However, recent publications have highlighted some issues with the use of these standard CCL sets that are often used in these drug screening studies. Therefore, instead of CMAP analysis based on gene expression signatures developed on drug response data collected on a small set of EOC cell lines, we took a novel CMAP approach based on information collected on EOC patients and clinical endpoints. That is, we determined the gene expression signature (e.g., set of genes) that were associated with time to recurrence in data collected on EOC patients within TCGA (n = 518) and a Mayo Clinic study (n = 474). The set of genes associated with time to recurrence (with and without adjustment for additional clinical covariates) was completed separately for TCGA and Mayo Clinic studies, restricting all analyses to EOC tumor of high-grade serous histology. Each of these 4 sets of genes, where genes with hazard ratios (HR) > 1 coded as “positively” associated and genes with HR < 1 coded as “negatively” associated with the phenotype, were inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature “matches” the “reference” signature. The drugs that overlapped between the CMAP analyses on the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines. Of the 11 drugs carried forward, we a priori hypothesized that 5 of these drugs would kill EOC cells (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG), for which all 5 showed an ability to kill EOC cells in vitro (cell lines were treated using serial dilutions of the drugs (20-0 μM) for 72 h followed by measuring cell viability using the CellTiter-Blue assay). Further research is on-going to understand the functional mechanism by which these candidate drugs might treat EOC and to apply this bioinformatics approach to other cancer drug screening studies. Note: This abstract was not presented at the meeting. Citation Format: Brooke L. Fridley, Rama Raghavan, Gottfried E. Konecny, Chen Wang, Ellen L. Goode, Harsh B. Pathak, Stephen Hyter. A bioinformatics approach to drug discovery: patient based connectivity mapping. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4853. doi:10.1158/1538-7445.AM2015-4853