Pharming: Joint Clonal Tree Reconstruction of SNV and CNA Evolution from Single-Cell DNA Sequencing of Tumors

Cancer arises through an evolutionary process in which somatic mutations, including single nucleotide variants (SNVs) and copy number aberrations (CNAs), drive the development of a malignant, heterogeneous tumor. Reconstructing this evolutionary history from sequencing data is critical for understanding the order in which mutations are acquired and the dynamic interplay between different types of alterations. Advances in modern whole genome single-cell sequencing now enable the accurate inference of copy number profiles in individual cells. However, the typically low sequencing coverage of these low-pass sequencing technologies poses a challenge for reliably inferring the presence or absence of SNVs within tumor cells, limiting the ability to simultaneously study the evolutionary relationships between SNVs and CNAs. In this work, we introduce a novel tumor phylogeny inference method, Pharming, that jointly infers the evolutionary histories of SNVs and CNAs. Our key insight is to leverage the high accuracy of copy number inference methods and the fact that SNVs co-occur in regions with CNAs in order to enable more precise tumor phylogeny reconstruction for both alteration types. We demonstrate via simulations that Pharming outperforms state-of-the-art single-modality tumor phylogeny inference methods. Additionally, we apply Pharming to a triple-negative breast cancer case, achieving high-resolution in the joint reconstruction of CNA and SNV evolution, including the de novo detection of a clonal whole-genome duplication event. Thus, Pharming offers the potential for more comprehensive and detailed tumor phylogeny inference for high-throughput, low-coverage single-cell DNA sequencing technologies compared to existing approaches.