title: An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer.
abstract: The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such "driver" mutations from innocuous "passenger" events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery.
authors.affiliation: Department of Mathematics, University of Minnesota Twin Cities, St. Paul, Minnesota, United States of America.
authors.forename: Jasmine
authors.initials: J
authors.lastname: Foo
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Lin L
authors.initials: LL
authors.lastname: Liu
authors.affiliation: Industrial and Systems Engineering, University of Minnesota Twin Cities, St. Paul, Minnesota, United States of America.
authors.forename: Kevin
authors.initials: K
authors.lastname: Leder
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Markus
authors.initials: M
authors.lastname: Riester
authors.affiliation: Department of Biology, Kyushu University, Fukuoka, Japan.
authors.forename: Yoh
authors.initials: Y
authors.lastname: Iwasa
authors.affiliation: Blueprint Medicines, Cambridge, Massachusetts, United States of America.
authors.forename: Christoph
authors.initials: C
authors.lastname: Lengauer
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Franziska
authors.initials: F
authors.lastname: Michor
mesh_terms: D015179:Colorectal Neoplasms / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D003430:Cross-Sectional Studies; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation / Q000235:genetics*; D011157:Population Dynamics