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Algorithmic methods to infer the evolutionary trajectories in cancer progression.

Giulio Caravagna1, Alex Graudenzi2, Daniele Ramazzotti3

  • 1Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milan, Italy; School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, United Kingdom; giulio.caravagna@ed.ac.uk.

Proceedings of the National Academy of Sciences of the United States of America
|July 1, 2016
PubMed
Summary

This study introduces PiCnIc, a computational pipeline for cancer inference, to model cancer genomic evolution using next-generation sequencing data. It helps understand cancer initiation and progression by analyzing driver mutations and genomic alterations.

Keywords:
Bayesian structural inferencecancer evolutioncausalitynext generation sequencingselective advantage

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Cancer genomic evolution is complex, influenced by (epi)genomic events.
  • Tumor heterogeneity poses significant challenges to modeling cancer progression.
  • Next-generation sequencing and machine learning offer potential for understanding cancer development.

Purpose of the Study:

  • To develop a computational pipeline, PiCnIc, for inferring cancer progression models from genomic data.
  • To apply the 'selective advantage' principle to population-level cancer modeling.
  • To address challenges posed by cancer's heterogeneity in genomic analysis.

Main Methods:

  • Introduced PiCnIc, a modular and customizable pipeline for cancer inference.
  • Utilized ensemble-level modeling from cross-sectional cancer genome sequences.
  • Integrated techniques for sample stratification, driver selection, and identification of fitness-equivalent alterations.

Main Results:

  • PiCnIc successfully models cancer progression using genomic data.
  • The pipeline reproduced existing knowledge on colorectal cancer progression.
  • Identified novel, experimentally verifiable hypotheses for cancer development.

Conclusions:

  • PiCnIc provides a versatile tool for extracting cancer progression models.
  • The pipeline has translational implications for understanding and potentially treating cancer.
  • Ensemble-level modeling can overcome some limitations of analyzing heterogeneous cancer genomes.