Probabilistic approaches for profiling copy number aberrations and loss of heterozygosity landscapes in cancer genomes

Genomic aberrations such as copy number alterations (CNA) and loss of heterozygosity (LOH) are hallmarks of human malignancies. These genomic abnormalities can have a measurable effect on the structure and dosage of chromosomal regions. Tumour suppressors and oncogenes altered by CNAs often contribute to a tumourigenic phenotype of increased proliferation. CNA and LOH can accrue through the process of branched evolution, resulting in the emergence of divergent clones with distinct aberrations present at diagnosis. Therefore, measuring and modeling how CNA/LOH distribute in cell populations can elucidate the abundance of specific clones and, ultimately, enable the study of clonal evolution. CNA/LOH events in tumours can be profiled using SNP genotyping arrays and whole genome sequencing (WGS). However, to maximize biological interpretability from these data, accurate and statistically robust computational methods for inferring CNA/LOH are necessary. I present three novel probabilistic approaches that apply hidden Markov models (HMM) to analyze CNA/LOH in tumour genomes. The first method is HMM-Dosage, which distinguishes somatic and germline copy number events. This tool was used to profile 2000 breast cancers, the largest study of this kind in the world. The second method is APOLLOH, which was one of the earliest methods developed to profile LOH in tumour WGS data. Its application to WGS of 23 triple negative breast cancers (TNBC) represents the first time that LOH and its effects on allelic expression were jointly analyzed from sequencing data. The third method is TITAN, which simultaneously infers CNA/LOH and the clonal population dynamics from tumour WGS data. This method provides an analytical route to studying the degree of clonal evolution driven by CNA/LOH. I applied TITAN to a novel set of primary breast tumours and corresponding mouse xenografts, presenting the results of distinct modes of temporal clonal selection patterns. In conclusion, this dissertation presents a suite of novel approaches and their application to real-world cancer datasets, contributing to significant discoveries in breast and ovarian cancers. Future applications of these approaches will further facilitate the elucidation of cancer evolution, the genetic basis of metastatic potential, and therapeutic response and resistance.Science, Faculty ofGraduat