{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T05:01:31Z","timestamp":1761541291297,"version":"3.37.3"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2015,11,17]],"date-time":"2015-11-17T00:00:00Z","timestamp":1447718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100008746","name":"National Cancer Center","doi-asserted-by":"publisher","award":["CA016672"],"award-info":[{"award-number":["CA016672"]}],"id":[{"id":"10.13039\/100008746","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches.<\/jats:p>","DOI":"10.1111\/biom.12448","type":"journal-article","created":{"date-parts":[[2015,11,17]],"date-time":"2015-11-17T20:58:43Z","timestamp":1447793923000},"page":"575-583","source":"Crossref","is-referenced-by-count":16,"title":["Bayesian Predictive Modeling for Genomic Based Personalized Treatment Selection"],"prefix":"10.1093","volume":"72","author":[{"given":"Junsheng","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, Texas 77030 , U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco C.","family":"Stingo","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, Texas 77030 , U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian P.","family":"Hobbs","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, Texas 77030 , U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,11,17]]},"reference":[{"key":"2024010914433039400_biom12448-bib-0001","volume-title":"Molecular Biology of the Cell","author":"Alberts","year":"2002","edition":"4th edition"},{"key":"2024010914433039400_biom12448-bib-0002","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1002\/sim.4484","article-title":"L1 penalized continuation ratio models for ordinal response prediction using high-dimensional datasets","volume":"31","author":"Archer","year":"2012","journal-title":"Statistics in Medicine"},{"key":"2024010914433039400_biom12448-bib-0003","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1038\/nature12627","article-title":"Tumour heterogeneity in the clinic","volume":"501","author":"Bedard","year":"2013","journal-title":"Nature"},{"key":"2024010914433039400_biom12448-bib-0004","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1073\/pnas.0308531101","article-title":"Metagenes and molecular pattern discovery using matrix factorization","volume":"101","author":"Brunet","year":"2004","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2024010914433039400_biom12448-bib-0005","doi-asserted-by":"crossref","first-page":"e83875","DOI":"10.1371\/journal.pone.0083875","article-title":"The personalized advantage index: Translating research on prediction into individualized treatment recommendations. a demonstration","volume":"9","author":"DeRubeis","year":"2014","journal-title":"PloS One"},{"key":"2024010914433039400_biom12448-bib-0006","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1634\/theoncologist.2010-0025","article-title":"A review of the most promising biomarkers in colorectal cancer: one step closer to targeted therapy","volume":"15","author":"Deschoolmeester","year":"2010","journal-title":"The Oncologist"},{"key":"2024010914433039400_biom12448-bib-0007","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1056\/NEJMoa052933","article-title":"Concordance among gene-expression\u2013based predictors for breast cancer","volume":"355","author":"Fan","year":"2006","journal-title":"The New England Journal of Medicine"},{"key":"2024010914433039400_biom12448-bib-0008","first-page":"1","article-title":"Guidance for industry: Clinical trial endpoints for the approval of cancer drugs and biologics","volume-title":"Washington, DC, US Food and Drug Administration","author":"FDA","year":"2007"},{"key":"2024010914433039400_biom12448-bib-0009","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1002\/sim.4322","article-title":"Subgroup identification from randomized clinical trial data","volume":"30","author":"Foster","year":"2011","journal-title":"Statistics in Medicine"},{"key":"2024010914433039400_biom12448-bib-0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"Journal of Statistical Software"},{"key":"2024010914433039400_biom12448-bib-0011","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1186\/1471-2105-11-367","article-title":"A flexible R package for nonnegative matrix factorization","volume":"11","author":"Gaujoux","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2024010914433039400_biom12448-bib-0012","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/nbt.2786","article-title":"Clinical development success rates for investigational drugs","volume":"32","author":"Hay","year":"2014","journal-title":"Nature Biotechnology"},{"key":"2024010914433039400_biom12448-bib-0013","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1198\/016214503388619229","article-title":"On optimality properties of the power prior","volume":"98,","author":"Ibrahim","year":"2003","journal-title":"Journal of the American Statistical Association"},{"key":"2024010914433039400_biom12448-bib-0014","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond k-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognition Letters"},{"key":"2024010914433039400_biom12448-bib-0015","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1111\/biom.12191","article-title":"Combining biomarkers to optimize patient treatment recommendations","volume":"70","author":"Kang","year":"2014","journal-title":"Biometrics"},{"key":"2024010914433039400_biom12448-bib-0016","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1038\/nrd3651","article-title":"Cancer biomarkers: Selecting the right drug for the right patient","volume":"11","author":"Kelloff","year":"2012","journal-title":"Nature Reviews Drug Discovery"},{"key":"2024010914433039400_biom12448-bib-0017","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1158\/2159-8274.CD-10-0010","article-title":"The BATTLE trial: Personalizing therapy for lung cancer","volume":"1","author":"Kim","year":"2011","journal-title":"Cancer Discovery"},{"key":"2024010914433039400_biom12448-bib-0018","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1186\/1475-2867-10-11","article-title":"From \u2018omics\u2019 to complex disease: A systems biology approach to gene-environment interactions in cancer","volume":"10","author":"Knox","year":"2010","journal-title":"Cancer Cell International"},{"key":"2024010914433039400_biom12448-bib-0019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/632030","article-title":"Survival analysis by penalized regression and matrix factorization","volume":"2013","author":"Lai","year":"2013","journal-title":"The Scientific World Journal"},{"key":"2024010914433039400_biom12448-bib-0020","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1177\/1740774510373120","article-title":"Bayesian adaptive randomization designs for targeted agent development","volume":"7","author":"Lee","year":"2010","journal-title":"Clinical Trials"},{"key":"2024010914433039400_biom12448-bib-0021","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1158\/0008-5472.CAN-08-2100","article-title":"Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes","volume":"69","author":"Li","year":"2009","journal-title":"Cancer Research"},{"key":"2024010914433039400_biom12448-bib-0022","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1002\/sim.1975","article-title":"On the efficiency of targeted clinical trials","volume":"24","author":"Maitournam","year":"2005","journal-title":"Statistics in Medicine"},{"key":"2024010914433039400_biom12448-bib-0023","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/nature12564","article-title":"Criteria for the use of omics-based predictors in clinical trials","volume":"502","author":"McShane","year":"2013","journal-title":"Nature"},{"key":"2024010914433039400_biom12448-bib-0024","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1038\/nrc1883","article-title":"Measuring response in a post-recist world: From black and white to shades of grey","volume":"6","author":"Michaelis","year":"2006","journal-title":"Nature Reviews Cancer"},{"key":"2024010914433039400_biom12448-bib-0025","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/A:1023949509487","article-title":"Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data","volume":"52","author":"Monti","year":"2003","journal-title":"Machine Learning"},{"key":"2024010914433039400_biom12448-bib-0026","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1038\/nm.3175","article-title":"A colorectal cancer classification system that associates cellular phenotype and responses to therapy","volume":"19","author":"Sadanandam","year":"2013","journal-title":"Nature Medicine"},{"key":"2024010914433039400_biom12448-bib-0027","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1111\/j.0006-341X.2004.00242.x","article-title":"Evaluating markers for selecting a patient's treatment","volume":"60","author":"Song","year":"2004","journal-title":"Biometrics"},{"key":"2024010914433039400_biom12448-bib-0028","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1093\/bioinformatics\/btq170","article-title":"Consensusclusterplus: a class discovery tool with confidence assessments and item tracking","volume":"26","author":"Wilkerson","year":"2010","journal-title":"Bioinformatics"},{"key":"2024010914433039400_biom12448-bib-0029","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1111\/j.1541-0420.2012.01763.x","article-title":"A robust method for estimating optimal treatment regimes","volume":"68","author":"Zhang","year":"2012","journal-title":"Biometrics"},{"key":"2024010914433039400_biom12448-bib-0030","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"}],"container-title":["Biometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2Fbiom.12448","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/72\/2\/575\/55181474\/biometrics_72_2_575.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/72\/2\/575\/55181474\/biometrics_72_2_575.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T18:02:59Z","timestamp":1706292179000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biometrics\/article\/72\/2\/575-583\/7511437"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,17]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,11,17]]},"published-print":{"date-parts":[[2016,6,1]]}},"URL":"https:\/\/doi.org\/10.1111\/biom.12448","relation":{},"ISSN":["0006-341X","1541-0420"],"issn-type":[{"type":"print","value":"0006-341X"},{"type":"electronic","value":"1541-0420"}],"subject":[],"published-other":{"date-parts":[[2016,6]]},"published":{"date-parts":[[2015,11,17]]}}}