Cancer chromosomal instability (CIN) results from dynamic changes to chromosome number and structure. The resulting diversity in somatic copy number alterations (SCNA) may provide the variation necessary for cancer evolution. Multi-sample phasing and SCNA analysis of 1421 samples from 394 tumours across 24 cancer types revealed ongoing CIN resulting in pervasive SCNA heterogeneity. Parallel evolutionary events, causing disruption to the same genes, such as BCL9, ARNT/HIF1B, TERT and MYC, within separate subclones were present in 35% of tumours. Most recurrent losses occurred prior to whole genome doubling (WGD), a clonal event in 48% of tumours. However, loss of heterozygosity at the human leukocyte antigen locus and loss of 8p to a single haploid copy recurred at significant subclonal frequencies, even in WGD tumours, likely reflecting ongoing karyotype remodeling. Focal amplifications affecting 1q21 (BCL9, ARNT), 5p15.33 (TERT), 11q13.3 (CCND1), 19q12 (CCNE1) and 8q24.1 (MYC) were frequently subclonal and exhibited an illusion of clonality within single samples. Analysis of an independent series of 1024 metastatic samples revealed enrichment for 14 focal SCNAs in metastatic samples, including late gains of 8q24.1 (MYC) in clear cell renal carcinoma and 11q13.3 (CCND1) in HER2-positive breast cancer. CIN may enable ongoing selection of SCNAs, manifested as ordered events, often occurring in parallel, throughout tumour evolution.

Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.