SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models

Hamim Zafar,Ken Chen,Anthony Tzen,Nicholas Navin,Luay Nakhleh

doi:10.1186/s13059-017-1311-2

Hamim Zafar, Ken Chen + Show 3 more

Open Access

https://doi.org/10.1186/s13059-017-1311-2

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Abstract

Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

Highlights

Intra-tumor heterogeneity, which is caused by a combination of mutation and selection [1,2,3,4], poses significant challenges to the diagnosis and clinical therapy of cancer [5,6,7,8]
Overview of SiFit We start with a brief explanation of how SiFit infers a tumor phylogeny from noisy genotype data obtained from single-cell DNA sequencing (SCS)
These lineages have direct applications in clinical oncology, for both diagnostic applications in measuring the amount of intra-tumor heterogeneity in tumors and for improving targeted therapy by helping oncologists identify mutations that are present in the majority of tumor cells

Summary

Introduction

Intra-tumor heterogeneity, which is caused by a combination of mutation and selection [1,2,3,4], poses significant challenges to the diagnosis and clinical therapy of cancer [5,6,7,8] This heterogeneity can be readily elucidated and understood if the evolutionary history of the tumor cells is known. This ambiguity makes it difficult to identify the lineage of the tumor from the mixture In such cases, phylogenetic reconstruction requires a deconvolution of the admixture signal to Single-cell DNA sequencing: promises and challenges With the advent of single-cell DNA sequencing (SCS) technologies, high-resolution data are becoming available, which promises to resolve intra-tumor heterogeneity to a single-cell level [14, 18, 22,23,24,25]. The high error rates associated with SCS data significantly complicate this task

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