Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Abstract

Phylogenetic techniques quantify intra-tumor heterogeneity by deconvolving either clonal or mutational trees from multi-sample sequencing data of individual tumors. Most of these methods rely on the well-known infinite sites assumption, and are limited to process either multi-region or single-cell sequencing data. Here, we improve over those methods with TRaIT, a unified statistical framework for the inference of the accumulation order of multiple types of genomic alterations driving tumor development. TRaIT supports both multi-region and single-cell sequencing data, and output mutational graphs accounting for violations of the infinite sites assumption due to convergent evolution, and other complex phenomena that cannot be detected with phylogenetic tools. Our method displays better accuracy, performance and robustness to noise and small sample size than state-of-the-art phylogenetic methods. We show with single-cell data from breast cancer and multi-region data from colorectal cancer that TRaIT can quantify the extent of intra-tumor heterogeneity and generate new testable experimental hypotheses.

Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses

“…While B-SCITE (Malikic et al, 2017) is a clear improvement over SCITE, it combines single-cell data with bulk sequencing data -since we do not manage the latter kind of data, a fair comparison is not feasible. For the same reason, we do not compare against TRaIT (Ramazzotti et al, 2017) and PhISCS (Malikic et al, 2019). OncoNEM (Ross and Markowetz, 2016) was excluded because it is not able to complete the execution on datasets as large as the ones used in the simulations.…”

“…Various methods have been recently developed for this purpose (Jahn et al, 2016;Ross and Markowetz, 2016;Zafar et al, 2017Zafar et al, , 2019, some of them introducing a hybrid approach of combining both SCS and VAF (bulk sequencing) data (Ramazzotti et al, 2017;Malikic et al, 2017;Salehi et al, 2017). Most of these methods, however, rely on the Infinite Sites Assumption (ISA), which essentially states that each mutation is acquired at most once in the phylogeny and is never lost.…”

gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data

“…Various methods have been developed for this purpose [ 13 – 15 ], some of them introducing a hybrid approach of combining both SCS and VAF data [ 16 – 19 ]. As stated before, most of these methods rely on the Infinite Sites Assumption (ISA) [ 20 ], which states that a mutation is acquired at most once in the phylogeny and is never lost.…”

Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses

“…Various methods have been developed for this purpose [13,27,34], some of them introducing a hybrid approach of combining both SCS and VAF data [25,19,29]. Most of these methods, however, rely on the Infinite Sites Assumption (ISA), which states that a mutation is acquired at most once in the tree and is never lost.…”

“…Some recent methods such as TRaIT [25] and SiFit [34] permit violations of the ISA, in particular they allow deletions of mutations without specifying a particular model of evolution. While this is a start, there is a need to develop more general methods, based on a relaxation to the ISA -something that is not robust to even a single back-mutation.…”