title: Algorithmic methods to infer the evolutionary trajectories in cancer progression.
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.
authors.affiliation: Department 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.
authors.forename: Giulio
authors.identifier: 0000-0003-4240-3265
authors.initials: G
authors.lastname: Caravagna
authors.affiliation: Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milan, Italy; Institute of Molecular Bioimaging and Physiology, Italian National Research Council, 93-I-20090 Milan, Italy;
authors.forename: Alex
authors.initials: A
authors.lastname: Graudenzi
authors.affiliation: Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milan, Italy;
authors.forename: Daniele
authors.initials: D
authors.lastname: Ramazzotti
authors.affiliation: Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology, Hospitalet de Llobregat, 08908 Barcelona, Spain; Bellvitge Institute for Biomedical Research, Hospitalet de Llobregat, 08908 Barcelona, Spain; Biomedical Research Centre Network for Epidemiology and Public Health, Hospitalet de Llobregat, 08908 Barcelona, Spain;
authors.forename: Rebeca
authors.initials: R
authors.lastname: Sanz-Pamplona
authors.affiliation: Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milan, Italy;
authors.forename: Luca
authors.initials: L
authors.lastname: De Sano
authors.affiliation: Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milan, Italy; SYSBIO Centre of Systems Biology (SYSBIO), 20126 Milan, Italy;
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Mauri
authors.affiliation: Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology, Hospitalet de Llobregat, 08908 Barcelona, Spain; Bellvitge Institute for Biomedical Research, Hospitalet de Llobregat, 08908 Barcelona, Spain; Biomedical Research Centre Network for Epidemiology and Public Health, Hospitalet de Llobregat, 08908 Barcelona, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, 08007 Barcelona, Spain;
authors.forename: Victor
authors.initials: V
authors.lastname: Moreno
authors.affiliation: Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milan, Italy; Milan Center for Neuroscience, University of Milan-Bicocca, 20126 Milan, Italy;
authors.forename: Marco
authors.initials: M
authors.lastname: Antoniotti
authors.affiliation: Courant Institute of Mathematical Sciences, New York University, New York, NY 10003.
authors.forename: Bud
authors.initials: B
authors.lastname: Mishra
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D015179:Colorectal Neoplasms / Q000235:genetics*; D006801:Humans; D000069550:Machine Learning; D018895:Microsatellite Repeats; D008957:Models, Genetic
keywords: Bayesian structural inference; cancer evolution; causality; next generation sequencing; selective advantage