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

TL;DR

TRaIT is a versatile computational framework that reconstructs mutational graphs of tumour evolution from both single-cell and multi-region sequencing data, improving accuracy and robustness over existing methods.

Contribution

It introduces TRaIT, supporting both data types within a unified framework, capturing complex evolutionary phenomena with enhanced accuracy and computational efficiency.

Findings

Supports both data types within the same framework

Produces accurate models of tumour evolution

Quantifies intra-tumour heterogeneity

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.