Wang, Chen

5510 Background: Transcriptional profiling of ovarian cancers has proven to be complex. As such it has been difficult to validate existing signatures across studies. Cancer Genome Atlas (TCGA) researchers have identified four molecular subtypes of high-grade serous ovarian cancer (HGSOC). However, survival duration did not differ significantly for the TCGA subtypes. Potential limitations of the TCGA data include short clinical follow-up (45% were alive at the time of last follow-up) and the need to unify gene expression measures from multiple platforms. Methods: Clinically annotated stage-II–IV HGSOC samples (n = 175) with >70% tumor cell content were profiled using the Agilent Whole Human Genome 4 x 44K chip. To identify subtypes non-negative matrix factorization (NMF) of mRNA expression was performed using ~2000 genes with the highest variability across patients. In parallel differentially expressed genes were identified using the Rosetta Similarity Search Tool as well as analysis of variance based on genes known to be involved in epithelial to mesenchymal transition. Results: Median follow-up time was 35 months (range, 0–202 months, 12% were alive at the time of last follow-up). NMF clustering confirmed four HGSOC subtypes (immunoreactive, differentiated, proliferative, and mesenchymal) on the basis of gene content in the clusters. Pathway signatures with therapeutic potential were identified for individual or multiple subtypes. Survival differed significantly between the four molecular subgroups in univariate (Hazard Ratio [HR] 2.4, 95% CI 1.5-4.1, p = 0.007) and multivariate (HR 2.3, 95% CI 1.3-4.0, p = 0.003) analyses when accounting for age, stage, grade, and postoperative residual tumor. Using the supervised clustering approach two distinct molecular subtypes of HGSOC based on epithelial and mesenchymal gene expression signatures were identified with significantly different survival outcomes. Conclusions: Here we independently validate and expand upon the molecular TCGA classification of HGSOC. The potential of these two prognostic classifiers may lie in their ability to recognize categories of patients that are more likely to respond to particular therapies.

Abstract High grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with gene mutation, DNA methylation changes, genetic variation, and epigenetic processes. Variation in several susceptibility regions and cancer-typical global methylation patterns have been observed in HGSOC; however, this knowledge has not been compelling in understanding HGSOC intiation or progression. As ingetration of genomic, epigenomic, and transcriptomic data has increased mechanistic understanding in other cancers, we hypothesized that tumor methylation alone or in combination with germline genetic variation influences tumor gene expression in HGSOC. We examined three nested models using an Elastnic Net (ENET) penalized regression method while adjusting for somatic copy number (CNV): a) germline genotype and tumor DNA methylation (full model), b) genotype only, and c) DNA methylation only. We included 339 cases from The Cancer Genome Atlas (TCGA), 54 cases from Mayo Clinic, and 78 cases from the Australian Ovarian Cancer Study (AOCS). Genotyping and copy number calls on germline DNA, expression, methylation and copy number on somatic samples were collected and analyzed on different platforms separately at each study site. We excluded genes with low overall expression and thus analyzed a total of 11,922 genes available in three datasets ( Ensembl IDs, 500kb window up- and downstream). In general, combining genomic data in HGSOC did not reveal a role for germline genetic variation in altering gene expression. However, in methylation only models 79 genes were associated with differential expression in the TCGA cases (permutation multiple testing adjusted p-val <0.05), in the Mayo cases (unadjusted p-val <0.05) and AOCS cases (unadjusted p-val <0.05). A known tummor suppressor (FBXW7) was associated with differential expression in the three datasets at p-val <0.01. This work demonstrates the feasibility, utility, and statistical power of ENET gene-level analyses incoporating maximal genomic information. Citation Format: Yanina Natanzon, Madalene Earp, Julie M. Cunningham, Kimberly R. Kalli, Stacey J. Winham, Sebastian M. Armasu, Melissa C. Larson, Chen Wang, David D. Bowtell, Dale W. Garsed, Ellen Goode. Omics data integration analysis in high grade serous ovarian cancer: results from three studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3368. doi:10.1158/1538-7445.AM2017-3368

Abstract Purpose: Ovarian cancer (OC) patients received maximum benefits when complete dissection with no macroscopic disease is achieved. It has been suggested that surgical outcome is associated with tumor biology and therefore can be predicted from tumor molecular characteristics such as gene expression levels. Noticeably, several debulking-associated genes have been independently reported based on meta-data analysis and further validations in individual center's cohorts. Furthermore, a recent OC study of the cancer genome atlas (TCGA) project revealed the presence of four expression subtypes in high-grade serous ovarian cancer (HGSOC). We have independently confirmed this association with prognosis in our OC cohort. With the awareness that surgical practices and rates of complete resection vary among institutions, we sought to evaluate whether expression changes of previously reported debulking-associated genes are associated with complete dissection in a HGSOC cohort in a center with an aggressive surgical approach. Further, we also tested if the HGSOC four expression subtypes are associated with complete dissection. Experimental Design: We constructed a cohort of 262 stage III/IV HGSOC patient cases diagnosed in Mayo Clinic between 1993 and 2011. Cases were categorized into two groups based upon debulking outcome: 84 complete resection cases with no gross visible residual disease (RD0), 178 cases with residual disease greater than 0 cm (RD1). Tumor mRNA expression was previously measured using Agilent 4x44k platforms. Eight previously published debulking-associated genes were evaluated for their expression differences between RD0 and RD1 patient groups using t-test: FABP4, ADH1B, POSTN, CXCL14, FAP, NUAK1, PTCH1 and TGFBR2. For testing subtype-debulking associations, we first quantified molecular subtype activation scores of individual tumors using single-sample gene set enrichment analysis (ssGSEA), and computed standardized scores after permutation-based scaling adjustments to account for gene set size difference. Then, standardized subtype scores are associated with complete dissection (RD0) vs. other surgical outcome (RD1) using logistic regression. The independent subtype-associations were further evaluated by adjusting known debulking outcome factors, including age, ascites, tumor grade and stage. Results: For eight previously reported debulking-associated genes, only one demonstrated a significant expression difference between RD0 and RD1 groups: CXCL14, t-test p-value = 9.2x10-3. When comparing debulking vs. HGSOC subtypes, we observed a significantly reduced probability of complete resection for the mesenchymal subtype (p-value = 1.1x10-2, OR = 0.56, 95% CI [0.35, 0.87]). Consistent with this observation, we have previously described the mesenchymal subtype as a poor prognosis group. On the other hand, the immunoreactive subtype was associated with a higher likelihood of complete resection (p-value = 9.4 x 10-3, OR=2.19, 95% CI = [1.22, 4.01]). The significant associations between these two subtype activities and complete resection in advanced stage disease remained after adjusting for age, ascites, tumor grade and sub-stage: mesenchymal (p-value = 1.5 x 10-2 ,OR = 0.48, 95% CI = [0.26, 0.85]); immunoreactive subtype (p-value = 4.8 x 10-2 , OR = 2.11, 95% CI = [1.04, 4.53]). Conclusion: This single center HGSOC analysis demonstrates the important of validation of debulking signatures in centers with diverse rates of complete resection. We were only able to validate an association with complete resection for one of eight previously reported debulking-associated genes. We also showed that HGSOC subtypes have significant associations with likelihood of complete dissection. Further studies are warranted for confirming subtype associations in other institutional HGSOC cohorts. Citation Format: Chen Wang, Jamie Bakkum-Gamez, Andrea Mariani, William Cliby, Ellen Goode. Tumor expression subtypes of high-grade serous ovarian cancer demonstrate associations with likelihood of complete resection: a single-center study. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr B80.