Abstract
BackgroundA large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types.ResultsWe introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods.ConclusionsWe show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.
Highlights
A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data
In TRaIT we estimate the statistical association between a set of genomic events annotated in sequencing data by combining optimal graph-based algorithms with bootstrap, hypothesis testing and information theory (Fig. 2)
Inputs data in TRaIT is represent as binary vectors, which is the standard representation for single-cell sequencing (SCS) sequencing and is hereby used to define a unique framework for both multi-region bulk and SCS data (Fig. 1a–c)
Summary
A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Sequencing data from multiple samples of single tumours can be used to investigate Intra-Tumor Heterogeneity (ITH) in light of evolution [1,2,3] Motivated by this observation, several new methods have been developed to infer the “evolutionary history” of a tumour from sequencing data. With earlier works of us [13,14,15,16,17,18], Bulk sequencing of multiple spatially-separated tumour biopsies returns a noisy mixture of admixed lineages [19,20,21,22,23] We can analyse these data by first retrieving clonal prevalences in bulk samples (subclonal deconvolution), and by computing their evolutionary relations [24,25,26,27,28,29,30,31].
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