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National Academy of Sciences, Proceedings of the National Academy of Sciences, 28(113), 2016

DOI: 10.1073/pnas.1520213113

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Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

Journal article published in 2015 by Giulio Caravagna ORCID, Alex Graudenzi, Daniele Ramazzotti ORCID, Rebeca Sanz-Pamplona, Luca De Sano, Giancarlo Mauri, Victor Moreno, Marco Antoniotti, Bud Mishra
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Significance A causality-based machine learning Pipeline for Cancer Inference (PiCnIc) is introduced to infer the underlying somatic evolution of ensembles of tumors from next-generation sequencing data. PiCnIc combines techniques for sample stratification, driver selection, and identification of fitness-equivalent exclusive alterations to exploit an algorithm based on Suppes’ probabilistic causation. The accuracy and translational significance of the results are studied in detail, with an application to colorectal cancer. The PiCnIc pipeline has been made publicly accessible for reproducibility, interoperability, and future enhancements.