Subtypes Of High-grade Serous Ovarian Cancer Research Articles

Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

Abstract B80: Tumor expression subtypes of high-grade serous ovarian cancer demonstrate associations with likelihood of complete resection: a single-center study.

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.

Studying platinum sensitivity and resistance in high-grade serous ovarian cancer: Different models for different questions.

High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among all gynecological cancers. Patients are generally diagnosed in an advanced stage with the majority of cases displaying platinum resistant relapses. Recent genomic interrogation of large numbers of HGSOC patient samples indicated high complexity in terms of genetic aberrations, intra- and intertumor heterogeneity and underscored their lack of targetable oncogenic mutations. Sub-classifications of HGSOC based on expression profiles, termed 'differentiated', 'immunoreactive', 'mesenchymal' and 'proliferative', were shown to have prognostic value. In addition, in almost half of all HGSOC patients, a deficiency in homologous recombination (HR) was found that potentially can be targeted using PARP inhibitors. Developing precision medicine requires advanced experimental models. In the current review, we discuss experimental HGSOC models in which resistance to platinum therapy and the use of novel therapeutics can be carefully studied. Panels of better-defined primary cell lines need to be established to capture the full spectrum of HGSOC subtypes. Further refinement of cell lines is obtained with a 3-dimensional culture model mimicking the tumor microenvironment. Alternatively, ex vivo ovarian tumor tissue slices are used. For in vivo studies, larger panels of ovarian cancer patient-derived xenografts (PDXs) are being established, encompassing all expression subtypes. Ovarian cancer PDXs grossly retain tumor heterogeneity and clinical response to platinum therapy is preserved. PDXs are currently used in drug screens and as avatars for patient response. The role of the immune system in tumor responses can be assessed using humanized PDXs and immunocompetent genetically engineered mouse models. Dynamic tracking of genetic alterations in PDXs as well as patients during treatment and after relapse is feasible by sequencing circulating cell-free tumor DNA and analyzing circulating tumor cells. We discuss how various models and methods can be combined to delineate the molecular mechanisms underlying platinum resistance and to select HGSOC patients other than BRCA1/2-mutation carriers that could potentially benefit from the synthetic lethality of PARP inhibitors. This integrated approach is a first step to improve therapy outcomes in specific subgroups of HGSOC patients.

CD73 is associated with poor prognosis in high-grade serous ovarian cancer.

The cell surface nucleotidase CD73 is an immunosuppressive enzyme involved in tumor progression and metastasis. Although preclinical studies suggest that CD73 can be targeted for cancer treatment, the clinical impact of CD73 in ovarian cancer remains unclear. In this study, we investigated the prognostic value of CD73 in high-grade serous (HGS) ovarian cancer using gene and protein expression analyses. Our results demonstrate that high levels of CD73 are significantly associated with shorter disease-free survival and overall survival in patients with HGS ovarian cancer. Furthermore, high levels of CD73 expression in ovarian tumor cells abolished the good prognosis associated with intraepithelial CD8(+) cells. Notably, CD73 gene expression was highest in the C1/stromal molecular subtype of HGS ovarian cancer and positively correlated with an epithelial-to-mesenchymal transition gene signature. Moreover, in vitro studies revealed that CD73 and extracellular adenosine enhance ovarian tumor cell growth as well as expression of antiapoptotic BCL-2 family members. Finally, in vivo coinjection of ID8 mouse ovarian tumor cells with mouse embryonic fibroblasts showed that CD73 expression in fibroblasts promotes tumor immune escape and thereby tumor growth. In conclusion, our study highlights a role for CD73 as a prognostic marker of patient survival and also as a candidate therapeutic target in HGS ovarian cancers.

Abstract 1928: High-grade serous ovarian cancer subtypes are similar across diverse populations

Abstract The most common and lethal type of invasive epithelial ovarian cancer is high grade serous (HGSC). Three to four gene expression-based HGSC subtypes have been identified in prior studies. In contrast to most previous studies, which have assessed the performance of survival classifiers in validation sets, we sought to determine the degree of similarity of gene expression patterns in subtypes between populations using systematic unsupervised clustering within populations. We analyzed publically-available mRNA expression data from studies with >200 HGSC tumors: The Cancer Genome Atlas (TCGA, US, n = 519, Affymetrix HT U133a), Tothill et al. (GSE9891, Australia, n = 242, Affymetrix U133 Plus 2.0) and Yoshihara et al. (GSE32062, Japan, n = 258, Agilent G4112a). We restricted analyses to the 12,249 genes shared across all datasets and selected from these the union of the 1,500 most variant genes per population (2,824). Using these datasets, we performed k-means clustering within each population for k = 3 and k = 4. We compared each cluster to all other clusters using Significance Analysis of Microarrays, which outputs an F score for all 12,249 genes, measuring cluster-specific differential expression. We then calculated the correlation of the resulting F score vectors across populations and within populations across both numbers of centroids (k = 3 or k = 4). We identified analogous clusters by high F score correlations and determined each cluster's similarity to the TCGA subtypes based on cluster-specific differentially expressed genes. We observed high concordance of gene expression patterns for clusters across populations and across k-means runs, suggesting that analogous clusters exist in most analyses. For k = 3, F score correlations across populations for clusters 1, 2 and 3, respectively, ranged between 0.77-0.85, 0.80-0.90, and 0.66-0.72. For k = 4, F score correlations for clusters 1-4 were, respectively: 0.76-0.85, 0.82-0.85, 0.65-0.78, and 0.52-0.78. Across k = 3 and k = 4, correlations for cluster 1 within TCGA, Tothill, and Yoshihara were 0.99, 1.00 and 1.00, and correlations for cluster 2 were 0.96, 0.98, and 0.95, respectively. Correlations for cluster 3 were less strong: 0.56, 0.88, and 0.60, respectively. For k = 4, cluster 4 was composed mainly of samples that belonged to cluster 3 for k = 3; 88% for TCGA, 54% for Tothill, and 95% for Yoshihara. When compared to TCGA subtypes, cluster 1 corresponded most strongly to mesenchymal, cluster 2 to proliferative, cluster 3 to differentiated, and cluster 4 to immunoreactive. Our observation of highly correlated gene expression patterns between clusters across populations, across platforms, and across the number of centroids provides strong evidence that at least three biological HGSC subtypes exist. The mesenchymal-like and proliferative-like subtypes are particularly consistent across populations, and could be uniquely targeted for treatment. Citation Format: Gregory P. Way, James Rudd, Casey S. Greene, Jennifer A. Doherty. High-grade serous ovarian cancer subtypes are similar across diverse populations. [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 1928. doi:10.1158/1538-7445.AM2015-1928

Prognostic relevance of gene signatures in high-grade serous ovarian carcinoma.

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.