Summary Our knowledge of copy number evolution during the expansion of primary breast tumors is limited 1 , 2 . To investigate this process, we developed a single cell, single-molecule DNA sequencing method and performed copy number analysis of 16,178 single cells from 8 triple-negative breast cancers (TNBCs) and 4 cell lines. Our data shows that breast tumors and cell lines are comprised of a large milieu of subclones (7–22) that are organized into a few (3–5) major superclones. Evolutionary analysis suggests that after clonal TP53 mutations, multiple LOH events and genome doubling, there was a period of transient genomic instability followed by ongoing copy number evolution during the primary tumor expansion. By subcloning single daughter cells in culture, we show that tumor cells re-diversify their genomes and do not retain isogenic properties. These data show that TNBCs continue to evolve chromosome aberrations and maintain a reservoir of subclonal diversity during primary tumor growth.

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

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.