Abstract LB-217: The cancer subtype ontology: Predicting response to therapy in clinical and translational research using 840 cancer subtypes
Authors: Ronen Roy , DeBoever Christopher , Kluge Boguslaw , Dutkowski Janusz 
Published: August 2019

Abstract
Background: Detailed understanding of cancer subtypes is key to precision oncology and instrumental for effective patient-to-therapy assignment and development of combination therapy regimens. While many molecular and pathologic cancer subtypes have been developed over the last decade, key challenges remain. Paramount among them is the ability to effectively track and compare cancer subtypes across studies, identify meaningful associations with clinical outcomes, and robustly assign new patients into subtypes in a clinical setting.
Approach: Here we report on the development of the Cancer Subtype Ontology (CSO) - a global resource for cancer subtype interrogation and patient-to-subtype assignment, constituting, to our knowledge, the largest collection of cancer subtypes to date. The foundational CSO resource currently catalogues 840 cancer subtypes which subcategorize ~40 histologies based on a diverse range of evidence types ranging from cancer genomics and transcriptomics, to epigenetics, proteomics and immune infiltration. CSO includes a wide collection of previously-published subtypes as well as a new model-based subtype collection reported here for the first time. CSO provides tools to compare across subtypes as well as characterize subtype enrichment for molecular, pathologic and clinical features, including response to therapy and patient survival. As an integrative part of the CSO, we develop a novel machine learning framework for robust assignment of new clinical and pre-clinical samples into the CSO subtypes. We first evaluate the CSO subtype collection using TCGA, measuring the ability to predict overall, progression-free, disease-specific, and disease-free survival. Next, using a novel curated collection of recent immune checkpoint inhibitor (ICI) trials, we demonstrate the power of the subtypes to predict ICI treatment responses and clinical outcomes.
Results: First using TCGA, we found that the CSO significantly expands the ability to uncover subtypes predictive of clinical outcomes. Notably, new model-based subtypes in CSO, while constituting less than 1/3 of the ontology, accounted for ~60% of the most significant outcome associations across genomic and molecular subtypings. Next, we applied the CSO patient assignment system to systematically assign patients from 10 ICI trials across 4 histologies into 103 CSO subtypes. We found that half of the trials had CSO assignments predictive of patient survival and/or response and most of them were novel. Specifically, assignment of IMvigor 210 urinary bladder cancer patients revealed two subtypes enriched for partial (P=0.0002, OR=3.87) and complete response (P =0.002, OR=6.86), respectively, only one of which was previously characterized (Mariathasan et al. 2018). For the Snyder et al. 2014 cohort we observed that while stratification using the cohort data alone did not yield predictive subtypes, CSO patient assignment revealed subtypes that were predictive of long term clinical benefit (P=0.003). Furthermore, across non-small cell lung cancer trials analyzed, CSO assignment was predictive of progression-free survival (P=0.048) in the Rizvi et al. 2018 cohort and showed suggestive associations with both progression-free survival (P=0.055) and response (durable clinical benefit, P=0.08) for the Rizvi et al. 2016 cohort.
Citation Format: Roy Ronen, Christopher DeBoever, Boguslaw Kluge, Janusz Dutkowski. The cancer subtype ontology: Predicting response to therapy in clinical and translational research using 840 cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-217.