Abstract 1650: Deconvolution of copy number alterations combining bulk and single-cell genomic data

• Published in: Cancer research (Chicago, Ill.) Vol. 79; no. 13_Supplement; p. 1650
• Main Authors: Lei, Haoyun, Lyu, Bochuan, Gertz, E. Michael, Schaeffer, Alejandro A., Shi, Xulian, Wu, Kui, Li, Guibo, Xu, Liqin, Hou, Yong, Dean, Michael, Schwartz, Russell
• Format: Journal Article
• Language: English
• Published: 01-07-2019
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2019-1650

Abstract
Characterizing the evolution of clonal cell populations in tumor progression is a challenging task given pervasive intratumor heterogeneity (ITH). Bulk DNA sequencing remains the dominant technology for large cohorts, but requires computationally deconvolving clonal substructure from bulk data, an error-prone process with limited resolution. Single-cell sequencing (SCS) is a promising alternative but is not yet practical at the scales needed for large cohorts. To address limitations of both bulk and SCS approaches, we developed strategies for deconvolving ITH by combining bulk sequencing with limited SCS data with specific focus on copy number alterations (CNAs), which are amenable to low-depth SCS and particularly challenging for deconvolution. We introduce two methods based on non-negative matrix factorization (NMF) of bulk data. One method extends the NMF optimization objective with a penalty for deviation of inferred clones from small numbers of observed SCS samples. The other combines clonal deconvolution with tumor phylogeny inference, balancing deconvolution quality against a minimum evolution cost for incorporating inferred and observed single cells into a reconstruction of the history of clonal evolution. We validated the methods on a set of semi-synthetic data derived from true low-depth SCS data consisting of 393 single cells derived from three regions each of two human glioblastoma cases. These data were used to call mean copy numbers at 9934 genomic loci. We artificially mixed the data to generate synthetic bulk samples of known clonal composition and applied each method to correctly reconstruct unobserved single cells and their clonal structures from three, six, or nine semi-synthetic bulk samples plus six single cells each per trial. For three/six/nine bulk samples, we achieved mean RMSD of copy number inference of 1.507/1.483/1.406 for pure NMF without SCS, 0.657/0.641/0.588 for the phylogeny-free SCS method, and 0.479/0.497/0.462 for the phylogeny-based SCS method. RMSD of mixture fractions describing the clonal composition were 0.245/0.242/0.243 for pure NMF without SCS, 0.215/0.198/0.206 for the phylogeny-free SCS method, and 0.215/0.215/0.224 for the phylogeny-based SCS method. The results show that we can substantially improve on bulk CNA deconvolution using limited SCS data, providing a way to balance advantages of pure bulk and pure SCS. The work also supports the value of a principled evolutionary model in inferring accurate clonal structure.
Citation Format: Haoyun Lei, Bochuan Lyu, E. Michael Gertz, Alejandro A. Schaeffer, Xulian Shi, Kui Wu, Guibo Li, Liqin Xu, Yong Hou, Michael Dean, Russell Schwartz. Deconvolution of copy number alterations combining bulk and single-cell genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1650.