Abstract 196: Redefining cancer subtypes using multi-omics and deep learning

Cancer is a heterogeneous collection of diseases traditionally classified by the tissue of origin. The diversity of the molecular profiles of cancers has a big impact on the way patients are diagnosed and treated, how they respond to their prescribed treatments, the duration of survival after diagnosis, and factors such as remission, recurrence, or spread (metastasis) of the disease. While such diagnostic and prognostic outcomes are potentially predictable by taking a closer look into the changes of the genome, epigenome, transcriptome, proteome, and various other omics platforms, the contemporary cancer treatments still predominantly don9t make the best use of such multi-omics profiling of patient samples. Therefore, multi-omics profiling of cancers holds great potential to define a molecularly coherent subtype definition of cancers in order to achieve the eventual goal of matching the best possible treatment to the subgroup of patients. However, the current subtypes from consortiums such as TCGA have been defined by heterogeneous methods and molecular markers by different teams. A subset of these studies have not attempted to characterize molecular subtypes, but rather taken histopathologically defined subtypes as the gold standard and tried to characterize molecular features of these subtypes. Here we evaluate TCGA cancer subtypes based on the molecular profile coherence score. This novel metric combines survival statistics, pathways information, tumor purity estimates, and mutational signatures. We expect that subtypes that are patient subgroups should display molecular signature homogeneity. We evaluate TCGA subtypes from 21 cancers using these criteria and compare the subtypes with our own definition using multi-omics data in a deep learning framework. We have refined the several subtypes from multiple cancers towards more molecularly coherent patient subgroups. Citation Format: Altuna Akalin, Bora Uyar, Jonathan Ronen, Vedran Franke. Redefining cancer subtypes using multi-omics and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 196.