Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Abstract
Background. A 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. Results. We 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. Conclusions. We 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.
Cite
@article{arxiv.1709.01076,
title = {Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data},
author = {Daniele Ramazzotti and Alex Graudenzi and Luca De Sano and Marco Antoniotti and Giulio Caravagna},
journal= {arXiv preprint arXiv:1709.01076},
year = {2019}
}