Chromosomal rearrangements are generally a consequence of improperly repaired double-strand breaks in DNA. These genomic aberrations can be a driver of cancers. Here, we investigated the use of chromosomal rearrangements for classification of cancer tumors and the effect of inter- and intrachromosomal rearrangements in cancer classification. We used data from the Catalogue of Somatic Mutations in Cancer (COSMIC) for breast, pancreatic, and prostate cancers, for which the COSMIC dataset reports the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification. GraphChrom was developed using a graph neural network which models the complex structure of chromosomal aberrations (CA) and provides local connectivity between the aberrations. The proposed framework illustrates three important contributions to the field of cancers. Firstly, it successfully classifies cancer types and subtypes. Secondly, it evolved into a novel data extraction technique which can be used to extract more informative graphs (informative aberrations associated with a sample); and thirdly, it predicts that interCAs (rearrangements between two or more chromosomes) are more effective in cancer prediction than intraCAs (rearrangements within the same chromosome), although intraCAs are three times more likely to occur than intraCAs.
Keywords: cancer classification; chromosomal rearrangement; graph neural networks.