Reference-free inference of tumor phylogenies from single-cell sequencing data

Abstract

Background: Effective management and treatment of cancer continues to be complicated by the rapid evolution and resulting heterogeneity of tumors. Phylogenetic study of cell populations in single tumors provides a way to delineate intra-tumoral heterogeneity and identify robust features of evolutionary processes. The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially with regard to chromosome abnormalities that typically dominate tumor evolution. Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression. Results: We propose a reference-free approach to mining single-cell genome sequence reads to allow predictive classification of tumors into heterogeneous cell types and reconstruct models of their evolution. The approach extracts k-mer counts from single-cell tumor genomic DNA sequences, and uses differences in normalized k-mer frequencies as a proxy for overall evolutionary distance between distinct cells. The approach computationally simplifies deriving phylogenetic markers, which normally relies on first aligning sequence reads to a reference genome and then processing the data to extract meaningful progression markers for constructing phylogenetic trees. The approach also provides a way to bypass some of the challenges that massive genome rearrangement typical of tumor genomes presents for reference-based methods. We illustrate the method on a publicly available breast tumor single-cell sequencing dataset.

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

“…Intuitively, J(S, C, C (observed) ) is a form of minimum evolution model on a phylogeny defined by S. While there are more sophisticated and realistic models for CNA distance (e.g., [5,4,10]), we favored L 1 distance here as a tractable approximation easily incorporated into the overall ILP framework. Similarly, while there are now a number of sophisticated methods available specifically for phylogenetics of single-cell sequences (c.f., [17]) these are largely focused on SNV rather than CNA phylogenetics (e.g., [15,29,45]) with limited exceptions [39,36]. More specifically, we modify the NMF objective function as follows:…”

Informatics for Cancer Immunotherapy

“…Recently, single-cell sequencing technologies have been employed to improve sample heterogeneity estimates [24][25][26] ; these approaches are reviewed in [27] .…”