{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T08:44:55Z","timestamp":1782722695463,"version":"3.54.5"},"posted":{"date-parts":[[2026,6,25]]},"group-title":"Bioinformatics","reference-count":30,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006093","name":"Patient-Centered Outcomes Research Institute","doi-asserted-by":"crossref","award":["ME-2023C1-32148"],"award-info":[{"award-number":["ME-2023C1-32148"]}],"id":[{"id":"10.13039\/100006093","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"crossref","award":["R00 HG011367"],"award-info":[{"award-number":["R00 HG011367"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Cancer Institute","award":["P50CA127001-16"],"award-info":[{"award-number":["P50CA127001-16"]}]},{"name":"National Cancer Institute","award":["P30CA016672-46"],"award-info":[{"award-number":["P30CA016672-46"]}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"crossref","award":["National Center for Advancing Translational Sciences (NIH)"],"award-info":[{"award-number":["National Center for Advancing Translational Sciences (NIH)"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Computational models that predict cancer drug response from genomic features are central to biomarker discovery, yet a recent audit found data leakage in 72% of 32 published methods, and complex models offer little interpretability while only modestly exceeding simple baselines under honest evaluation. Tissue lineage is a largely untapped source of legitimate inductive bias, but existing tissue-aware methods neither separate pan-cancer from lineage-specific signal nor report leakage-free performance.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    We introduce the Data Shared Elastic Net (DSEN), a tissue-aware regression that decomposes each drug\u2019s model into a shared coefficient block common to all lineages and tissue-specific deviation blocks. Under leakage-free cross-validation across 265 drugs, 1,462 cell lines and 31 tissue lineages, DSEN improved mean squared error over a standard elastic net for 92.5% of drugs (mean 4.95%) while selecting 58% fewer stable shared features. Shared coefficients generalized to held-out tissues (59% tissue-level win rate) and recurrently recovered transferable pathway modules (p53, MAPK), whereas tissue blocks captured lineage markers such as the skin\n                    <jats:italic>MITF<\/jats:italic>\n                    \/\n                    <jats:italic>S100B<\/jats:italic>\n                    program. The closest tissue-aware comparator, TG-LASSO, performed worse than the tissue-agnostic baseline (\u221213.8% mean MSE). Ablation shows tissue-aware modeling helps most when features are scarce, with no single modality dominating.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/AsiaeeLab\/tissue-aware-drug-response\">https:\/\/github.com\/AsiaeeLab\/tissue-aware-drug-response<\/jats:ext-link>\n                    .\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>\n                    <jats:email>amir.asiaeetaheri@vumc.org<\/jats:email>\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.06.21.733619","type":"posted-content","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T06:20:27Z","timestamp":1782454827000},"source":"Crossref","is-referenced-by-count":0,"title":["Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2855-4100","authenticated-orcid":false,"given":"Jared","family":"Strauch","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leila","family":"Azinfar","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heather H.","family":"Pua","sequence":"additional","affiliation":[{"name":"Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James P.","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin R.","family":"Coombes","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Data Science, and Epidemiology, Georgia Cancer Center, Augusta University, Augusta, Georgia, 30912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5317-9820","authenticated-orcid":false,"given":"Amir","family":"Asiaee","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"issue":"1","key":"2026062901000816000_2026.06.21.733619v1.1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s41698-020-0122-1","article-title":"Machine learning approaches to drug response prediction: challenges and recent progress","volume":"4","year":"2020","journal-title":"NPJ precision oncology"},{"key":"2026062901000816000_2026.06.21.733619v1.2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.102102699"},{"key":"2026062901000816000_2026.06.21.733619v1.3","article-title":"High dimensional data enrichment: Interpretable, fast, and data-efficient","year":"2018","journal-title":"arXiv preprint"},{"key":"2026062901000816000_2026.06.21.733619v1.4","unstructured":"A. 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