Explainable deep learning on 7500 whole genomes elucidates cancer-specific patterns of chromosomal instability

Abstract

AbstractChromosomal instability (CIN) refers to an increased rate of chromosomal changes within cells. It is highly prevalent in cancer cells and leads to abnormalities in chromosome number (aneuploidy) and structure. CIN contributes to genetic diversity within a tumour, which facilitates tumour progression, drug resistance, and metastasis. Here, we present a deep learning method and an exploration of the chromosome copy aberrations (CNAs) resultant from CIN, across 7,500 high-depth, whole genome sequences, representing 13 cancer types. We found that the types of CNAs can act as a highly specific classifier for primary site. Using an explainable AI approach, we revealed both established and novel loci that contributed to cancer type, and focusing on highly significant chromosome loci within cancer types, we demonstrated prognostic relevance. We outline how the developed methodology can provide several applications for researchers, including drug target and biomarker discovery, as well as the identification of cancers of unknown primary site.