Abstract 4259: Defining subtle cancer subtypes using the darkest DNA

Abstract

The confluence of deep sequencing and powerful machine learning is providing an unprecedented peek at the darkest of the dark genomic matter. While deep sequencing uncovers rare tumor variants, the heterogeneity of the disease confounds the best of machine learning (ML) algorithms. Here we set out to answer if the dark-matter of the genome encompass signals that can classify the fine subtypes of disease that are otherwise gnomically indistinguishable. We introduce a novel stochastic regularization, ReVeal , that empowers ML to classify subtle cancer subtypes even from the same ‘cell of origin’. Analogous to heritability, implicitly defined on whole genome, we use predictability (F 1 score) definable on portions of the genome. In an effort to classify cancer subtypes using dark-matter DNA, we applied ReVeal to a new WGS dataset from 727 patient samples with seven forms of hematological cancers and assessed the predictivity over several genomic regions including genic, non-dark, non-coding, non-genic, dark. ReVeal allowed the classification of all segments of the genome better than standard ML algorithms. The non-genic, non-coding and the dark-matter had the highest F1 scores with dark-matter having the highest level of predictability (F 1 = 0.78). Based on ReVeal’s predictability of different sectors of the genome, dark matter contains signal significant enough to classify fine subtypes of disease. The agglomeration of rare variants, even in the hitherto unannotated and ill-understood regions of the genome, may play a substantial role in the disease etiology and deserve much more attention. Citation Format: Laxmi Parida, Claudia Haferlach, Kahn Rhrissorrakrai, Filippo Utro, Chaya Levovitz, Kern Wolfgang, Niroshan Nadarajah, Stephan Hutter, Manja Meggendorfer, Wencke Walter, Constance Baer, Torsten Haferlach. Defining subtle cancer subtypes using the darkest DNA [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 4259.