Luca De Sano1, Giulio Caravagna2, Daniele Ramazzotti1, Alex Graudenzi3, Giancarlo Mauri1, Bud Mishra4, Marco Antoniotti5. 1. Department of Informatics, Systems and Communication, University of Milano-Bicocca. 2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, School of Informatics, University of Edinburgh, Edinburgh, UK. 3. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Milan, Italy. 4. Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and. 5. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan Center for Neuroscience, University of Milan-Bicocca, Milan, Italy.
Abstract
MOTIVATION: We introduce TRanslational ONCOlogy (TRONCO), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g. retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g. multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints for uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy. AVAILABILITY AND IMPLEMENTATION: TRONCO is released under the GPL license, is hosted at http://bimib.disco.unimib.it/ (Software section) and archived also at bioconductor.org. CONTACT: tronco@disco.unimib.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: We introduce TRanslational ONCOlogy (TRONCO), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g. retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancerpatients, when multiple samples, e.g. multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints for uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy. AVAILABILITY AND IMPLEMENTATION: TRONCO is released under the GPL license, is hosted at http://bimib.disco.unimib.it/ (Software section) and archived also at bioconductor.org. CONTACT: tronco@disco.unimib.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Ethan Cerami; Jianjiong Gao; Ugur Dogrusoz; Benjamin E Gross; Selcuk Onur Sumer; Bülent Arman Aksoy; Anders Jacobsen; Caitlin J Byrne; Michael L Heuer; Erik Larsson; Yevgeniy Antipin; Boris Reva; Arthur P Goldberg; Chris Sander; Nikolaus Schultz Journal: Cancer Discov Date: 2012-05 Impact factor: 39.397
Authors: Giulio Caravagna; Alex Graudenzi; Daniele Ramazzotti; Rebeca Sanz-Pamplona; Luca De Sano; Giancarlo Mauri; Victor Moreno; Marco Antoniotti; Bud Mishra Journal: Proc Natl Acad Sci U S A Date: 2016-06-28 Impact factor: 11.205
Authors: Jun Qian; Shilin Zhao; Yong Zou; S M Jamshedur Rahman; Maria-Fernanda Senosain; Thomas Stricker; Heidi Chen; Charles A Powell; Alain C Borczuk; Pierre P Massion Journal: Am J Respir Crit Care Med Date: 2020-03-15 Impact factor: 21.405
Authors: Samra Turajlic; Hang Xu; Kevin Litchfield; Andrew Rowan; Tim Chambers; Jose I Lopez; David Nicol; Tim O'Brien; James Larkin; Stuart Horswell; Mark Stares; Lewis Au; Mariam Jamal-Hanjani; Ben Challacombe; Ashish Chandra; Steve Hazell; Claudia Eichler-Jonsson; Aspasia Soultati; Simon Chowdhury; Sarah Rudman; Joanna Lynch; Archana Fernando; Gordon Stamp; Emma Nye; Faiz Jabbar; Lavinia Spain; Sharanpreet Lall; Rosa Guarch; Mary Falzon; Ian Proctor; Lisa Pickering; Martin Gore; Thomas B K Watkins; Sophia Ward; Aengus Stewart; Renzo DiNatale; Maria F Becerra; Ed Reznik; James J Hsieh; Todd A Richmond; George F Mayhew; Samantha M Hill; Catherine D McNally; Carol Jones; Heidi Rosenbaum; Stacey Stanislaw; Daniel L Burgess; Nelson R Alexander; Charles Swanton Journal: Cell Date: 2018-04-12 Impact factor: 41.582
Authors: Samra Turajlic; Hang Xu; Kevin Litchfield; Andrew Rowan; Stuart Horswell; Tim Chambers; Tim O'Brien; Jose I Lopez; Thomas B K Watkins; David Nicol; Mark Stares; Ben Challacombe; Steve Hazell; Ashish Chandra; Thomas J Mitchell; Lewis Au; Claudia Eichler-Jonsson; Faiz Jabbar; Aspasia Soultati; Simon Chowdhury; Sarah Rudman; Joanna Lynch; Archana Fernando; Gordon Stamp; Emma Nye; Aengus Stewart; Wei Xing; Jonathan C Smith; Mickael Escudero; Adam Huffman; Nik Matthews; Greg Elgar; Ben Phillimore; Marta Costa; Sharmin Begum; Sophia Ward; Max Salm; Stefan Boeing; Rosalie Fisher; Lavinia Spain; Carolina Navas; Eva Grönroos; Sebastijan Hobor; Sarkhara Sharma; Ismaeel Aurangzeb; Sharanpreet Lall; Alexander Polson; Mary Varia; Catherine Horsfield; Nicos Fotiadis; Lisa Pickering; Roland F Schwarz; Bruno Silva; Javier Herrero; Nick M Luscombe; Mariam Jamal-Hanjani; Rachel Rosenthal; Nicolai J Birkbak; Gareth A Wilson; Orsolya Pipek; Dezso Ribli; Marcin Krzystanek; Istvan Csabai; Zoltan Szallasi; Martin Gore; Nicholas McGranahan; Peter Van Loo; Peter Campbell; James Larkin; Charles Swanton Journal: Cell Date: 2018-04-12 Impact factor: 41.582
Authors: Marco Silvestri; Matteo Dugo; Marta Vismara; Loris De Cecco; Davide Lanzoni; Andrea Vingiani; Secondo Folli; Maria Carmen De Santis; Filippo de Braud; Giancarlo Pruneri; Serena Di Cosimo; Vera Cappelletti Journal: Sci Rep Date: 2022-01-27 Impact factor: 4.379