Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
This provides a unified tool for cancer researchers to model tumour evolution from diverse data types, though it is incremental as it builds on existing algorithms.
The authors tackled the problem of reconstructing evolutionary models of individual tumours from sequencing data by introducing TRaIT, a computational framework that supports both multi-region and single-cell data, improving accuracy, robustness, and computational complexity compared to existing methods.
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.