{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:38:17Z","timestamp":1775522297661,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","award":["40-00812-98-16012"],"award-info":[{"award-number":["40-00812-98-16012"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>PRECISE and the scripts for running our experiments are available on our GitHub page (https:\/\/github.com\/NKI-CCB\/PRECISE).<\/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\/btz372","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T23:20:10Z","timestamp":1557444010000},"page":"i510-i519","source":"Crossref","is-referenced-by-count":70,"title":["PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors"],"prefix":"10.1093","volume":"35","author":[{"given":"Soufiane","family":"Mourragui","sequence":"first","affiliation":[{"name":"Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands"},{"name":"Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands"}]},{"given":"Marco","family":"Loog","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands"},{"name":"Department of Computer Science, University of Copenhagen, Copenhagen, Denmark"}]},{"given":"Mark A","family":"van de Wiel","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands"},{"name":"MRC Biostatistics Unit, University of Cambridge, Cambridge, UK"}]},{"given":"Marcel J T","family":"Reinders","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands"},{"name":"Computational Biology Center, Leiden University Medical Center, Leiden ZC, The Netherlands"}]},{"given":"Lodewyk F A","family":"Wessels","sequence":"additional","affiliation":[{"name":"Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands"},{"name":"Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands"}]}],"member":"286","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"2023062712323547100_btz372-B1","doi-asserted-by":"crossref","first-page":"e8124.","DOI":"10.15252\/msb.20178124","article-title":"Multi-Omics Factor Analysis\u2013a framework for unsupervised integration of multi-omics data sets","volume":"14","author":"Argelaguet","year":"2018","journal-title":"Mol. 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