New Paper: Algorithmic methods to infer the evolutionary trajectories in cancer progression
Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti, Rebeca Sanz-Pamplona, Luca De Sano, Giancarlo Mauri, Victor Moreno, Marco Antoniotti, and Bud Mishra
PNAS, June 28th, 2016, doi: 10.1073/pnas.1520213113
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.
Written by Marco Antoniotti