Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence

Abstract

Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient's tumour composition and evolutionary history is key for personalised therapies. Single-cell sequencing now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the uncertainty of the underlying evolutionary process. By modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour single-cell sequencing data.

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

“…Like all other progression models, however, TreeMHN currently does not consider back mutations, i.e . situations in which a mutation is acquired at first but subsequently lost [57, 58]. A possible extension along this line is to include additional parameters and use as input phylogenetic trees inferred by methods such as SCARLET [59], which views a decrease in copy numbers that overlap a mutated locus as evidence of back mutations.…”

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data

“…A feasible strategy to alleviate this problem is to integrate tree reconstruction with variant calling [12], where phylogenetic information on cell ancestry is used to obtain more reliable variant calls. Recently developed methods for scDNA-seq data approach this strategy from different perspectives [15, 31]. However, those methods do not operate within the statistical phylogenetic framework, in particular do not infer branch lengths of the tree.…”