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Bayesian inference of cancer driver genes using signatures of positive selection

View ORCID ProfileLuis Zapata, View ORCID ProfileHana Susak, View ORCID ProfileOliver Drechsel, View ORCID ProfileMarc R. Friedländer, View ORCID ProfileXavier Estivill, View ORCID ProfileStephan Ossowski
doi: https://doi.org/10.1101/059360
Luis Zapata
1Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain;
2Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003 Barcelona, Spain;
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Hana Susak
1Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain;
2Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003 Barcelona, Spain;
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Oliver Drechsel
1Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain;
2Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003 Barcelona, Spain;
5Institute of Molecular Biology gGmbH (IMB), Ackermannweg 4, 55128 Mainz, Germany
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Marc R. Friedländer
1Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain;
2Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003 Barcelona, Spain;
3Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, S-10691 Stockholm, Sweden;
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Xavier Estivill
1Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain;
2Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003 Barcelona, Spain;
4Experimental Genetics Division, Sidra Medical and Research Center, 26999 Doha, Qatar;
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Stephan Ossowski
1Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain;
2Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003 Barcelona, Spain;
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  • For correspondence: stephan.ossowski@crg.eu
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Abstract

Tumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian inference model for driver prediction. In an in-depth benchmark, we demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

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Posted April 13, 2017.
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Bayesian inference of cancer driver genes using signatures of positive selection
Luis Zapata, Hana Susak, Oliver Drechsel, Marc R. Friedländer, Xavier Estivill, Stephan Ossowski
bioRxiv 059360; doi: https://doi.org/10.1101/059360
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Bayesian inference of cancer driver genes using signatures of positive selection
Luis Zapata, Hana Susak, Oliver Drechsel, Marc R. Friedländer, Xavier Estivill, Stephan Ossowski
bioRxiv 059360; doi: https://doi.org/10.1101/059360

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