George, Joshy

Abstract Background: Ovarian high-grade serous carcinomas (HGSC) comprise four distinct molecular subtypes based on mRNA expression patterns, with differential survival. Understanding risk factor associations is important to elucidate the etiology of HGSC. We investigated associations between different epidemiologic risk factors and HGSC molecular subtypes. Methods: We pooled data from 11 case–control studies with epidemiologic and tumor gene expression data from custom NanoString CodeSets developed through a collaboration within the Ovarian Tumor Tissue Analysis consortium. The PrOTYPE-validated NanoString-based 55-gene classifier was used to assign HGSC gene expression subtypes. We examined associations between epidemiologic factors and HGSC subtypes in 2,070 cases and 16,633 controls using multivariable-adjusted polytomous regression models. Results: Among the 2,070 HGSC cases, 556 (27%) were classified as C1.MES, 340 (16%) as C5.PRO, 538 (26%) as C2.IMM, and 636 (31%) as C4.DIF. The key factors, including oral contraceptive use, parity, breastfeeding, and family history of ovarian cancer, were similarly associated with all subtypes. Heterogeneity was observed for several factors. Former smoking [OR = 1.25; 95% confidence interval (CI) = 1.03, 1.51] and genital powder use (OR = 1.42; 95% CI = 1.08, 1.86) were uniquely associated with C2.IMM. History of endometriosis was associated with C5.PRO (OR = 1.46; 95% CI = 0.98, 2.16) and C4.DIF (OR = 1.27; 95% CI = 0.94, 1.71) only. Family history of breast cancer (OR = 1.44; 95% CI = 1.16, 1.78) and current smoking (OR = 1.40; 95% CI = 1.11, 1.76) were associated with C4.DIF only. Conclusions: This study observed heterogeneous associations of epidemiologic and modifiable factors with HGSC molecular subtypes. Impact: The different patterns of associations may provide key information about the etiology of the four subtypes.

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

Abstract Glioma stem cells (GSC) and epithelial–mesenchymal transition (EMT) are strongly associated with therapy resistance and tumor recurrence, but the underlying mechanisms are incompletely understood. Here, we show that S100A4 is a novel biomarker of GSCs. S100A4+ cells in gliomas are enriched with cancer cells that have tumor-initiating and sphere-forming abilities, with the majority located in perivascular niches where GSCs are found. Selective ablation of S100A4-expressing cells was sufficient to block tumor growth in vitro and in vivo. We also identified S100A4 as a critical regulator of GSC self-renewal in mouse and patient-derived glioma tumorspheres. In contrast with previous reports of S100A4 as a reporter of EMT, we discovered that S100A4 is an upstream regulator of the master EMT regulators SNAIL2 and ZEB along with other mesenchymal transition regulators in glioblastoma. Overall, our results establish S100A4 as a central node in a molecular network that controls stemness and EMT in glioblastoma, suggesting S100A4 as a candidate therapeutic target. Cancer Res; 77(19); 5360–73. ©2017 AACR.

Abstract While growing evidence suggests that tumor microenvironment can modulate stemness and the mesenchymal phenotype, little is known about how these cell states are coordinately regulated. We report that S100A4 controls both stemness and mesenchymal transition in GBM cells. In a spontaneous mouse glioma model and in primary human GBM tumorspheres, S100A4 expression is enriched in tumor-initiating cells and is required for self-renewal and tumor growth in vivo. In addition, greater than 80 percent of S100A4+ cells are associated with the tumor vasculature, a known niche for glioma stem cells, and S100A4 promotes pro-angiogenic factor expression. Interestingly, acidosis induces expression of S100A4 and the EMT regulators SNAIL1/2 and acidosis-induced EMT regulator expression requires S100A4 function. Importantly, S100A4 functions as a molecular switch between the proneural and mesenchymal states in GBM as S100A4 knockdown results in down-regulation of the mesenchymal signature genes, including the “master transcriptional regulators of mesenchymal transition”, and concurrent up-regulation of proneural signature genes. Together, these results indicate that S100A4 sits in a critical node in the molecular network that controls both glioma stem cells and the mesenchymal phenotype. Citation Format: Kin-Hoe Chow, Hee Jung Park, Joshy George, Yuanxin Chen, Andrew Gallup, Wen Jiang, Betty Y. Kim, Kyuson Yun. Regulation of GBM stemness and mesenchymal transition [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 4783. doi:10.1158/1538-7445.AM2017-4783