Single-cell mutation identification via phylogenetic inference

Reconstructing the evolution of tumors is a key aspect towards the identification of appropriate cancer therapies. The task is challenging because tumors evolve as heterogeneous cell populations. Single-cell sequencing holds the promise of resolving the heterogeneity of tumors; however, it has its own challenges including elevated error rates, allelic drop-out, and uneven coverage. Here, we develop a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells. Our method, called SCIΦ, jointly calls mutations in individual cells and estimates the tumor phylogeny among these cells. Employing a Markov Chain Monte Carlo scheme enables us to reliably call mutations in each single cell even in experiments with high drop-out rates and missing data. We show that SCIΦ outperforms existing methods on simulated data and applied it to different real-world datasets, namely a whole exome breast cancer as well as a panel acute lymphoblastic leukemia dataset.

Abstract licence: CC BY-NC-ND

1. 8.

   SeqAn An efficient, generic C++ library for sequence analysis · 2008

2. 20.

   Cancer genomics: one cell at a time · 2014

3. 23.

   OncoNEM: inferring tumor evolution from single-cell sequencing data · 2016

4. 24.

   Tree inference for single-cell data · 2016

29 more references, whose wording the paper’s licence keeps private.

• Eleven grand challenges in single-cell data science · 2020

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.