Multi-modal immune profiling of mucinous ovarian carcinoma: analysis from the Ovarian Tumor Tissue Analysis/Multidisciplinary Ovarian Cancer Outcomes Group consortia

Mucinous ovarian carcinoma (MOC) is a rare histotype of ovarian cancer. Women with advanced disease (FIGO stage III/IV) have very poor outcomes, with 5-year survival of <20%. Little is known about the anti-tumor immune response in MOC, limiting potential immunotherapeutic options for patients.

Immune data from 3 platforms were analysed. A NanoString mRNA expression panel with 39 immune-related genes (n=60 MOC, n=4198 other ovarian histotypes), a NanoString mRNA plexset with PD-1, PD-L1 (n=241 MOC, n=115 upper and lower gastrointestinal (GI) cancers), and 2 multicolor immunofluorescence (mcIF) panels (n=121 MOC) for CD68, PD-L1, PD-1, CD8 and FOXP3. Data overlapped all platforms for 22 MOC. Immune marker expression by ovarian cancer histotype was measured. Uni- and multivariable Cox Proportional Hazards assessed overall survival (OS) within MOC. Hierarchical clustering looked for subgroups of patients based on co-expression of immune genes.

Compared to more common high-grade serous ovarian cancer (HGSC) and GI cancers, MOC were less immunologically active. High IGHM expression was associated with poorer OS (HR 1.31 (95% CI 1.02–1.69), p=0.033). High tumor densities of FOXP3 and PD-1 positive cells conferred poorer OS in a model with age, stage and site; however, these and PD-L1 and CD68 were associated with grade. The 39 gene NanoString and mcIF datasets revealed 4 clusters, roughly translating to immune cold, moderate and hot (groups 3,4). mcIF analysis revealed a subset with PD-L1 expression in the absence of T-regulatory cell infiltrates.

This study provides novel insights into the MOC immune landscape. Despite relatively low immune activity overall, immunotherapy could be explored for a subset of patients with MOC.

Supported by NSW Ministry of Health and UNSW Sydney, through the NSW Health PhD Scholarship Program 2017-2022; Translational Cancer Research Network top-up scholarship 2021

Abstract A03: 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

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.