TY - JOUR T1 - Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression JF - bioRxiv DO - 10.1101/027359 SP - 027359 AU - Giulio Caravagna AU - Alex Graudenzi AU - Daniele Ramazzotti AU - Rebeca Sanz-Pamplona AU - Luca De Sano AU - Giancarlo Mauri AU - Victor Moreno AU - Marco Antoniotti AU - Bud Mishra Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/09/24/027359.abstract N2 - 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. ER -