Cancer is a disease characterized by the accumulation of somatic alterations to the genome, which selectively make cancer cells fitter to survive. The understanding of progression models for cancer, i.e., the identification of sequences of mutations that leads to the emergence of the disease, is still unclear. The problem of reconstructing such progression models is not new; in fact, several methods to extract progression models from cross-sectional samples have been developed since the late 90s.

Recently, we have proposed a number of algorithms to reconstruct cancer progression models both from aggregate, population level data -- i.e., collections of patients' data, as many TCGA datasets – and from individual level data -- i.e., multiple biopsies of single tumor, or even single cell data. We perform our reconstruction using a variety of algorithms based on a "probability raising" score that guarantees statistical dependencies on the inferred precedence relations.  Our methods are complementary to traditional phylogeny reconstructions ones.

In this setting, we have proven the correctness of our algorithms and characterized their performance. Our algorithms are collected in a R BioConductor package "TRanslational ONCOlogy" (TRONCO) that we have successfully used as part of our "Pipeline for Cancer Inference" (PiCnIc) to analyse Colorectal Cancer (CRC) data from TCGA, which highlighted possibly biologically significant patterns in the progressions inferred. Part of TRONCO is also the "Temporal oRder of Individual Tumors" (TRaIT) a new collection of algorithms that can be used for multi-region and single-cell progression analisys of cancers. 

Web Sites:

• http://bimib.disco.unimib.it
• http://troncopackage.org
• http://cdac2018.lakecomoschool.org

Marco Antoniotti is an Associate Professor of Computer Science in the DISCo of the Università Milano-Bicocca, Milan, Italy.  Before that he was Senior Research Scientist of the NYU Courant Bioinformatics Group in New York and a Research Scientist at PARADES EEIG in Rome, Italy. From 1996 to 1997 post Doctoral fellow at University of California at Berkeley PATH Institute. He received his Ph.D in Computer Science from Courant Institute of Mathematical Sciences of New York University. His main research topics are Bioinformatics and Systems Biology, Simulation, Verification and Language Design Issues in Hybrid Systems. Marco Antoniotti is the author of several journal and conference papers and of several software projects; he has received support for his research from the Regione Lombardia, NSF, the Elixir european network and the European Commission under the MCSA and COST actions.