RT Journal Article SR Electronic T1 Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression JF bioRxiv FD Cold Spring Harbor Laboratory SP 027359 DO 10.1101/027359 A1 Giulio Caravagna A1 Alex Graudenzi A1 Daniele Ramazzotti A1 Rebeca Sanz-Pamplona A1 Luca De Sano A1 Giancarlo Mauri A1 Victor Moreno A1 Marco Antoniotti A1 Bud Mishra YR 2015 UL http://biorxiv.org/content/early/2015/09/23/027359.abstract AB The evolutionary nature of cancer relates directly to a renewed focus on the voluminous NGS (next generation sequencing) data, aiming at the identification of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional-omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly related to the dramatic heterogeneity and temporality of the disease. In this paper, we build on our recent works on “selectivity” relation among driver mutations in cancer progression and investigate their applicability to the modeling problem – both at the population and individual levels. On one hand, we devise an optimal, versatile and modular pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate this pipeline’s ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses. On the other hand, we prove that our framework can be applied, mutatis mutandis, in reconstructing the evolutionary history of cancer clones in single patients, as illustrated by an example with multiple biopsy data from clear cell renal carcinomas.