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.

Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2019). Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. BMC BIOINFORMATICS, 20(1) [10.1186/s12859-019-2795-4].

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

Ramazzotti, D;Graudenzi, A
;
Antoniotti, M
Penultimo
;
2019

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.
Articolo in rivista - Articolo scientifico
Cancer evolution; Multi-region sequencing; Mutational graphs; Single-cell sequencing; Single-tumour evolution; Tumour phylogeny;
Single-tumour evolution; Single-cell sequencing; Multi-region sequencing; Mutational graphs; Cancer evolution; Tumour phylogeny
English
Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2019). Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. BMC BIOINFORMATICS, 20(1) [10.1186/s12859-019-2795-4].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/226364
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