RT Journal Article SR Electronic T1 CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data JF bioRxiv FD Cold Spring Harbor Laboratory SP 008110 DO 10.1101/008110 A1 Daniele Ramazzotti A1 Giulio Caravagna A1 Loes Olde Loohuis A1 Alex Graudenzi A1 Ilya Korsunsky A1 Giancarlo Mauri A1 Marco Antoniotti A1 Bud Mishra YR 2015 UL http://biorxiv.org/content/early/2015/05/07/008110.abstract AB 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 CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http://bimib.disco.unimib.it/index.php/TroncoContact daniele.ramazzotti@disco.unimib.it