Daniele Ramazzotti1, Giulio Caravagna1, Loes Olde Loohuis1, Alex Graudenzi1, Ilya Korsunsky1, Giancarlo Mauri2, Marco Antoniotti1, Bud Mishra1. 1. Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy. 2. Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy, Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, USA, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA and SYSBIO Centre of Systems Biology, Milano, Italy.
Abstract
UNLABELLED: We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. MOTIVATION: Several cancer-related genomic data have become available (e.g. The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer 'progression' models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of 'selectivity' relations, where a mutation in a gene A 'selects' for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. RESULTS: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events. AVAILABILITY AND IMPLEMENTATION: CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http://bimib.disco.unimib.it/index.php/Tronco CONTACT: daniele.ramazzotti@disco.unimib.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. MOTIVATION: Several cancer-related genomic data have become available (e.g. The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer 'progression' models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of 'selectivity' relations, where a mutation in a gene A 'selects' for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. RESULTS: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events. AVAILABILITY AND IMPLEMENTATION: CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http://bimib.disco.unimib.it/index.php/Tronco CONTACT: daniele.ramazzotti@disco.unimib.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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: Clarissa Gerhauser; Francesco Favero; Thomas Risch; Ronald Simon; Lars Feuerbach; Yassen Assenov; Doreen Heckmann; Nikos Sidiropoulos; Sebastian M Waszak; Daniel Hübschmann; Alfonso Urbanucci; Etsehiwot G Girma; Vladimir Kuryshev; Leszek J Klimczak; Natalie Saini; Adrian M Stütz; Dieter Weichenhan; Lisa-Marie Böttcher; Reka Toth; Josephine D Hendriksen; Christina Koop; Pavlo Lutsik; Sören Matzk; Hans-Jörg Warnatz; Vyacheslav Amstislavskiy; Clarissa Feuerstein; Benjamin Raeder; Olga Bogatyrova; Eva-Maria Schmitz; Claudia Hube-Magg; Martina Kluth; Hartwig Huland; Markus Graefen; Chris Lawerenz; Gervaise H Henry; Takafumi N Yamaguchi; Alicia Malewska; Jan Meiners; Daniela Schilling; Eva Reisinger; Roland Eils; Matthias Schlesner; Douglas W Strand; Robert G Bristow; Paul C Boutros; Christof von Kalle; Dmitry Gordenin; Holger Sültmann; Benedikt Brors; Guido Sauter; Christoph Plass; Marie-Laure Yaspo; Jan O Korbel; Thorsten Schlomm; Joachim Weischenfeldt Journal: Cancer Cell Date: 2018-12-10 Impact factor: 31.743
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: Simone Rubinacci; Alex Graudenzi; Giulio Caravagna; Giancarlo Mauri; James Osborne; Joe Pitt-Francis; Marco Antoniotti Journal: Cancer Inform Date: 2015-09-01
Authors: E Michael Gertz; Salim Akhter Chowdhury; Woei-Jyh Lee; Darawalee Wangsa; Kerstin Heselmeyer-Haddad; Thomas Ried; Russell Schwartz; Alejandro A Schäffer Journal: PLoS One Date: 2016-06-30 Impact factor: 3.240