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, Bud Mishra

Research output: Contribution to journalArticlepeer-review

Abstract

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing 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 work on the "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 because 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.

Original languageEnglish (US)
Pages (from-to)E4025-E4034
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number28
DOIs
StatePublished - Jul 12 2016

Keywords

  • Bayesian structural inference
  • Cancer evolution
  • Causality
  • Next generation sequencing
  • Selective advantage

ASJC Scopus subject areas

  • General

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