A regression based approach to phylogenetic reconstruction from multi-sample bulk DNA sequencing of tumors
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Motivation
DNA sequencing of multiple bulk samples from a tumor provides the opportunity to investigate tumor heterogeneity and reconstruct a phylogeny of a patient’s cancer. However, since bulk DNA sequencing of tumor tissue measures thousands of cells from a heterogeneous mixture of distinct sub-populations, accurate reconstruction of the tumor phylogeny requires simultaneous deconvolution of cancer clones and inference of ancestral relationships, leading to a challenging computational problem. Many existing methods for phylogenetic reconstruction from bulk sequencing data do not scale to large datasets, such as recent datasets containing upwards of ninety samples with dozens of distinct sub-populations.
Results
We develop an approach to reconstruct phylogenetic trees from multi-sample bulk DNA sequencing data by separating the reconstruction problem into two parts: a structured regression problem for a fixed tree 𝒯, and an optimization over tree space. We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. This algorithm has both asymptotic and empirical improvements over linear programming (LP) approaches to the problem. Using our algorithm for this regression sub-problem, we develop fastBE , a simple method for phylogenetic inference from multi-sample bulk DNA sequencing data. We demonstrate on simulated data with hundreds of samples and upwards of a thousand distinct sub-populations that fastBE outperforms existing approaches in terms of reconstruction accuracy, sample efficiency, and runtime. Owing to its scalability, fastBE also enables phylogenetic reconstruction directly from indvidual mutations without requiring the clustering of mutations into clones. On real data from fourteen B-progenitor acute lymphoblastic leukemia patients, fastBE infers similar phylogenies to the existing, state-of-the-art method, but with fewer violations of a widely used evolutionary constraint and better agreement to the observed mutational frequencies. Finally, we show that on two patient-derived colorectal cancer models, fastBE also infers phylogenies with less violation of a widely used evolutionary constraint compared to existing methods, and leads to distinct interpretations of the intra-tumor heterogeneity.
Availability
fastBE is implemented in C ++ and is available at: github.com/raphael-group/fastBE.
