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Algorithmic methods to infer the evolutionary trajectories in cancer progression

Research output: Contribution to journalArticle

  • Giulio Caravagna
  • Alex Graudenzi
  • Daniele Ramazzotti
  • Rebeca Sanz-Pamplona
  • Luca De Sano
  • Giancarlo Mauri
  • Victor Moreno
  • Marco Antoniotti
  • Bud Mishra

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http://www.pnas.org/content/early/2016/06/27/1520213113
Original languageEnglish
Pages (from-to)E4025-E4034
Number of pages10
JournalProceedings of the National Academy of Sciences
Volume113
Issue number28
Early online date28 Jun 2016
DOIs
Publication statusPublished - 12 Jul 2016

Abstract

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference 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 stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on “selective advantage” relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIc’s ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses.

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