Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

Abstract

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but mixes many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations --e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets --have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization (NMF) balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming (MILP) model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semi-simulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

“…In addition, SCS data capture branching events, despite of the lack of the temporal order of mutations. Integrating both SCS and bulk sequencing data presents a great potential in effectively and accurately addressing the clonal reconstruction problem; however, there is only a handful of available tools that integrate both types of data [52,53,55]…”

“…Even though SVs in evolving bacterial populations are often neglected, a general computational framework for clonal reconstruction that adequately address the biological significance of CNVs in such populations is still desirable. As CNVs evolve due to segmental duplications, reconstructing the clonal composition of CNVs from pool‐seq data requires different problem formulations as the clonal reconstruction problem solely on SNVs, e.g ., to minimize the number of duplication events in the clonal phylogeny, as proposed recently by Eaton et al [54] and Lei et al [55]. RCK [56] on the other hand addresses chromosomal aberrations and attempts to reconstruct the clonal haplotypes from bulk sequencing data.…”

“…Lei et al . [55] proposed and formulated a mixed membership model integrating both SCS data and pool‐seq data for clonal decomposition based only on CNV data using non‐negative matrix factorization. In their approach, the clonal deconvolution from pool‐seq data is gauged and optimized by its similarity to SCS data via a coordinate descent algorithm.…”