{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T18:49:48Z","timestamp":1767034188069,"version":"3.37.3"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["2G12MD007595","5P20GM103424-15","P01CA214091"],"award-info":[{"award-number":["2G12MD007595","5P20GM103424-15","P01CA214091"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"U.S. Army Research Laboratory's Army Research Office","doi-asserted-by":"crossref","award":["W911NF-15-1-0510"],"award-info":[{"award-number":["W911NF-15-1-0510"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P01CA214091","U19AG055373"],"award-info":[{"award-number":["P01CA214091","U19AG055373"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Somatic mutations in proto-oncogenes and tumor suppressor genes constitute a major category of causal genetic abnormalities in tumor cells. The mutation spectra of thousands of tumors have been generated by The Cancer Genome Atlas (TCGA) and other whole genome (exome) sequencing projects. A promising approach to utilizing these resources for precision medicine is to identify genetic similarity-based sub-types within a cancer type and relate the pinpointed sub-types to the clinical outcomes and pathologic characteristics of patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose two novel methods, ccpwModel and xGeneModel, for mutation-based clustering of tumors. In the former, binary variables indicating the status of cancer driver genes in tumors and the genes\u2019 involvement in the core cancer pathways are treated as the features in the clustering process. In the latter, the functional similarities of putative cancer driver genes and their confidence scores as the \u2018true\u2019 driver genes are integrated with the mutation spectra to calculate the genetic distances between tumors. We apply both methods to the TCGA data of 16 cancer types. Promising results are obtained when these methods are compared to state-of-the-art approaches as to the associations between the determined tumor clusters and patient race (or survival time). We further extend the analysis to detect mutation-characterized transcriptomic prognostic signatures, which are directly relevant to the etiology of carcinogenesis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>R codes and example data for ccpwModel and xGeneModel can be obtained from http:\/\/webusers.xula.edu\/kzhang\/ISMB2018\/ccpw_xGene_software.zip.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty232","type":"journal-article","created":{"date-parts":[[2018,4,13]],"date-time":"2018-04-13T06:58:15Z","timestamp":1523602695000},"page":"i404-i411","source":"Crossref","is-referenced-by-count":9,"title":["Driver gene mutations based clustering of tumors: methods and applications"],"prefix":"10.1093","volume":"34","author":[{"given":"Wensheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, USA"}]},{"given":"Erik K","family":"Flemington","sequence":"additional","affiliation":[{"name":"Department of Pathology, Tulane School of Medicine, Tulane Cancer Center, Tulane University, New Orleans, LA, USA"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"2023051604342917900_bty232-B1","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1101\/gr.134635.111","article-title":"MuSiC: identifying mutational significance in cancer genomes","volume":"22","author":"Dees","year":"2012","journal-title":"Genome Res"},{"key":"2023051604342917900_bty232-B2","doi-asserted-by":"crossref","first-page":"e169.","DOI":"10.1093\/nar\/gks743","article-title":"Functional impact bias reveals cancer drivers","volume":"40","author":"Gonzalez-Perez","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023051604342917900_bty232-B3","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1038\/nmeth.2651","article-title":"Network-based stratification of tumor mutations","volume":"10","author":"Hofree","year":"2013","journal-title":"Nat. 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