{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:29:50Z","timestamp":1787009390881,"version":"build-2736575974"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":27,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["FU 356\/12-2"],"award-info":[{"award-number":["FU 356\/12-2"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["BIFOLD24B"],"award-info":[{"award-number":["BIFOLD24B"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["BIFOLD25B"],"award-info":[{"award-number":["BIFOLD25B"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["01IS18025A"],"award-info":[{"award-number":["01IS18025A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["01IS18037A"],"award-info":[{"award-number":["01IS18037A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information & Communications Technology Planning & Evaluation"},{"DOI":"10.13039\/501100014188","name":"MSIT","doi-asserted-by":"publisher","award":["2019-0-00079"],"award-info":[{"award-number":["2019-0-00079"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Artificial Intelligence Graduate School Program, Korea University","award":["2022-0-00984"],"award-info":[{"award-number":["2022-0-00984"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug\u2013gene interactions and identifies transcriptomic patterns linked with drug response. Trained on data from 546\u00a0646 drug perturbation experiments involving 1135 drugs and molecular profiles from 476 tumors, NeurixAI accurately predicted treatment responses for 272 targeted and 30 chemotherapeutic drugs in unseen tumor samples (Spearman\u2019s rho\u00a0&amp;gt;0.2), maintaining high performance on an external validation set. Additionally, NeurixAI identified the anticancer potential of 160 repurposed non-cancer drugs. Using explainable artificial intelligence (xAI), our framework uncovered key genes influencing drug response at the individual tumor level and revealed both known and novel mechanisms of drug resistance. These findings demonstrate the potential of integrating transcriptomics with xAI to optimize cancer treatment, enable drug repurposing, and identify new therapeutic targets.<\/jats:p>","DOI":"10.1093\/narcan\/zcaf029","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T11:26:41Z","timestamp":1754911601000},"source":"Crossref","is-referenced-by-count":6,"title":["Neural interaction explainable AI predicts drug response across cancers"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5472-4683","authenticated-orcid":false,"given":"Philipp","family":"Keyl","sequence":"first","affiliation":[{"name":"Institute of Pathology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin ,\u00a010117 Berlin,","place":["Germany"]},{"name":"Institute of Pathology, Faculty of Medicine, LMU Munich , 80337\u00a0 Munich ,","place":["Germany"]},{"name":"BIFOLD\u2014Berlin Institute for the Foundations of Learning and Data , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne , 50931 Cologne,\u00a0","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5617-091X","authenticated-orcid":false,"given":"Julius","family":"Keyl","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence in Medicine, University Hospital Essen (A\u00f6R) , Essen ,","place":["Germany"]},{"name":"Institute of Pathology, University Hospital Essen (A\u00f6R) , 45147\u00a0 Essen ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Mock","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Faculty of Medicine, LMU Munich , 80337\u00a0 Munich ,","place":["Germany"]},{"name":"German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , 80336 Munich,\u00a0","place":["Germany"]},{"name":", Munich partner site , 80336 Munich,\u00a0","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel","family":"Dernbach","sequence":"additional","affiliation":[{"name":"Machine Learning Group, Technical University of Berlin , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"Aignostics GmbH , 10555\u00a0 Berlin ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liliana\u00a0H","family":"Mochmann","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Faculty of Medicine, LMU Munich , 80337\u00a0 Munich ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niklas","family":"Kiermeyer","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Faculty of Medicine, LMU Munich , 80337\u00a0 Munich ,","place":["Germany"]},{"name":"German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , 80336 Munich,\u00a0","place":["Germany"]},{"name":", Munich partner site , 80336 Munich,\u00a0","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Jurmeister","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Faculty of Medicine, LMU Munich , 80337\u00a0 Munich ,","place":["Germany"]},{"name":"German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , 80336 Munich,\u00a0","place":["Germany"]},{"name":", Munich partner site , 80336 Munich,\u00a0","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9249-4292","authenticated-orcid":false,"given":"Michael","family":"Bockmayr","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin ,\u00a010117 Berlin,","place":["Germany"]},{"name":"University Medical Center Hamburg-Eppendorf Department of Pediatric Hematology and Oncology, , 20246 Hamburg,\u00a0","place":["Germany"]},{"name":"bAIome\u2014Center for Biomedical AI, University Medical Center Hamburg-Eppendorf ,\u00a020246 Hamburg,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland\u00a0F","family":"Schwarz","sequence":"additional","affiliation":[{"name":"BIFOLD\u2014Berlin Institute for the Foundations of Learning and Data , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne , 50931 Cologne,\u00a0","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7243-6186","authenticated-orcid":false,"given":"Gr\u00e9goire","family":"Montavon","sequence":"additional","affiliation":[{"name":"BIFOLD\u2014Berlin Institute for the Foundations of Learning and Data , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"Machine Learning Group, Technical University of Berlin , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"Freie Universit\u00e4t Berlin Department of Mathematics and Computer Science, , 14195 Berlin,\u00a0","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3861-7685","authenticated-orcid":false,"given":"Klaus-Robert","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"BIFOLD\u2014Berlin Institute for the Foundations of Learning and Data , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"Machine Learning Group, Technical University of Berlin , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"Korea University Department of Artificial Intelligence, , Seoul 136-713 ,","place":["South Korea"]},{"name":"MPI for Informatics , 66123\u00a0 Saarbr\u00fccken ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-2389","authenticated-orcid":false,"given":"Frederick","family":"Klauschen","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin ,\u00a010117 Berlin,","place":["Germany"]},{"name":"Institute of Pathology, Faculty of Medicine, LMU Munich , 80337\u00a0 Munich ,","place":["Germany"]},{"name":"BIFOLD\u2014Berlin Institute for the Foundations of Learning and Data , 10587\u00a0 Berlin ,","place":["Germany"]},{"name":"German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , 80336 Munich,\u00a0","place":["Germany"]},{"name":", Munich partner site , 80336 Munich,\u00a0","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"2025090405362820100_B1","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1016\/S1470-2045(21)00288-6","article-title":"Trastuzumab for early-stage, HER2-positive breast cancer: a meta-analysis of 13\u2008864 women in seven randomised trials","volume":"22","author":"Bradley","year":"2021","journal-title":"Lancet Oncol"},{"key":"2025090405362820100_B2","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1056\/NEJMoa022457","article-title":"Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia","volume":"348","author":"O\u2019Brien","year":"2003","journal-title":"N Engl J Med"},{"key":"2025090405362820100_B3","doi-asserted-by":"publisher","article-title":"Gene expression has more power for predictingin vitrocancer cell vulnerabilities than genomics","author":"Dempster","DOI":"10.1101\/2020.02.21.959627"},{"key":"2025090405362820100_B4","doi-asserted-by":"publisher","first-page":"e201900445","DOI":"10.26508\/lsa.201900445","article-title":"The cancer cell proteome and transcriptome predicts sensitivity to targeted and cytotoxic drugs","volume":"2","author":"Rydenfelt","year":"2019","journal-title":"Life Sci Alliance"},{"key":"2025090405362820100_B5","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1038\/s43018-019-0018-6","article-title":"Discovering the anticancer potential of non-oncology drugs by systematic viability profiling","volume":"1","author":"Corsello","year":"2020","journal-title":"Nat Cancer"},{"key":"2025090405362820100_B6","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/s41586-019-1186-3","article-title":"Next-generation characterization of the Cancer Cell Line Encyclopedia","volume":"569","author":"Ghandi","year":"2019","journal-title":"Nature"},{"key":"2025090405362820100_B7","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.cell.2017.06.010","article-title":"Defining a cancer dependency map","volume":"170","author":"Tsherniak","year":"2017","journal-title":"Cell"},{"key":"2025090405362820100_B8","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","article-title":"On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation","volume":"10","author":"Bach","year":"2015","journal-title":"PLoS One"},{"key":"2025090405362820100_B9","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","article-title":"Explaining nonlinear classification decisions with deep Taylor decomposition","volume":"65","author":"Montavon","year":"2017","journal-title":"Pattern Recognit"},{"key":"2025090405362820100_B10","doi-asserted-by":"publisher","first-page":"7581","DOI":"10.1109\/TPAMI.2021.3115452","article-title":"Higher-order explanations of graph neural networks via relevant walks","volume":"44","author":"Schnake","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025090405362820100_B11","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1146\/annurev-pathmechdis-051222-113147","article-title":"Toward explainable artificial intelligence for precision pathology","volume":"19","author":"Klauschen","year":"2024","journal-title":"Annu Rev Pathol"},{"key":"2025090405362820100_B12","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JPROC.2021.3060483","article-title":"Explaining deep neural networks and beyond: a review of methods and applications","volume":"109","author":"Samek","year":"2021","journal-title":"Proc IEEE"},{"key":"2025090405362820100_B13","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/TPAMI.2020.3020738","article-title":"Building and interpreting deep similarity models","volume":"44","author":"Eberle","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025090405362820100_B14","unstructured":"Landrum G, Tosco P, Kelley B \u00a0et al. \u00a0rdkit\/rdkit: 2020_03_1 (Q1 2020) Release. Zenodo(29 March 2020, date last accessed)10.5281\/zenodo.3732262."},{"key":"2025090405362820100_B15","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1145\/2939672.2939754","article-title":"node2vec: scalable feature learning for networks","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Grover","year":"2016"},{"key":"2025090405362820100_B16","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/s41698-022-00278-4","article-title":"Patient-level proteomic network prediction by explainable artificial intelligence","volume":"6","author":"Keyl","year":"2022","journal-title":"npj Precis Onc"},{"key":"2025090405362820100_B17","doi-asserted-by":"publisher","first-page":"e20","DOI":"10.1093\/nar\/gkac1212","article-title":"Single-cell gene regulatory network prediction by explainable AI","volume":"51","author":"Keyl","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2025090405362820100_B18","article-title":"A unified approach to interpreting model predictions","volume-title":"Advances in Neural Information Processing Systems","author":"Lundberg","year":"2017"},{"key":"2025090405362820100_B19","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Sundararajan","year":"2017"},{"key":"2025090405362820100_B20","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1002\/wics.147","article-title":"ggplot2","volume":"3","author":"Wickham","year":"2011","journal-title":"WIREs Comput Stats"},{"key":"2025090405362820100_B21","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2025090405362820100_B22","article-title":"DepMap 19Q4 Public","author":"DepMap","year":"2020"},{"key":"2025090405362820100_B23","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J Chem Inf Model"},{"key":"2025090405362820100_B24","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","article-title":"DeepSynergy: predicting anti-cancer drug synergy with deep learning","volume":"34","author":"Preuer","year":"2018","journal-title":"Bioinformatics"},{"key":"2025090405362820100_B25","doi-asserted-by":"publisher","first-page":"e201900445","DOI":"10.26508\/lsa.201900445","article-title":"The cancer cell proteome and transcriptome predicts sensitivity to targeted and cytotoxic drugs","volume":"2","author":"Rydenfelt","year":"2019","journal-title":"Life Sci Alliance"},{"key":"2025090405362820100_B26","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1038\/nature11005","article-title":"Systematic identification of genomic markers of drug sensitivity in cancer cells","volume":"483","author":"Garnett","year":"2012","journal-title":"Nature"},{"key":"2025090405362820100_B27","doi-asserted-by":"publisher","first-page":"e70056","DOI":"10.1111\/cts.70056","article-title":"Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development","volume":"17","author":"Ponce-Bobadilla","year":"2024","journal-title":"Clin Transl Sci"},{"key":"2025090405362820100_B28","doi-asserted-by":"publisher","first-page":"22571","DOI":"10.1038\/s41598-023-49676-z","article-title":"Inductive detection of influence operations via graph learning","volume":"13","author":"Gabriel","year":"2023","journal-title":"Sci Rep"},{"key":"2025090405362820100_B29","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/ICMLA52953.2021.00117","article-title":"Proxy model explanations for time series RNNs","volume-title":"20th IEEE International Conference on Machine Learning and Applications (ICMLA)","author":"Wood-Doughty","year":"2021"},{"key":"2025090405362820100_B30","doi-asserted-by":"publisher","first-page":"D447","DOI":"10.1093\/nar\/gku1003","article-title":"STRING v10: protein\u2013protein interaction networks, integrated over the tree of life","volume":"43","author":"Szklarczyk","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2025090405362820100_B31","doi-asserted-by":"publisher","first-page":"4762","DOI":"10.7150\/jca.25138","article-title":"Functional genome-wide screening identifies targets and pathways sensitizing pancreatic cancer cells to dasatinib","volume":"9","author":"Chien","year":"2018","journal-title":"J Cancer"},{"key":"2025090405362820100_B32","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1158\/1535-7163.MCT-13-0233","article-title":"IGFBP2\/FAK pathway is causally associated with dasatinib resistance in non-small cell lung cancer cells","volume":"12","author":"Lu","year":"2013","journal-title":"Mol Cancer Ther"},{"key":"2025090405362820100_B33","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1186\/s40478-023-01673-z","article-title":"EMP3 sustains oncogenic EGFR\/CDK2 signaling by restricting receptor degradation in glioblastoma","volume":"11","author":"Martija","year":"2023","journal-title":"Acta Neuropathol Commun"},{"key":"2025090405362820100_B34","doi-asserted-by":"publisher","first-page":"e23406","DOI":"10.1097\/MD.0000000000023406","article-title":"Human epidermal growth factor receptor 2-positive metastatic breast cancer with novel epidermal growth factor receptor-ZNF880 fusion and epidermal growth factor receptor E114K mutations effectively treated with pyrotinib: a case report","volume":"99","author":"Yue","year":"2020","journal-title":"Medicine"},{"key":"2025090405362820100_B35","doi-asserted-by":"publisher","first-page":"627","DOI":"10.4149\/neo_2014_078","article-title":"Annexin A2 is implicated in multi-drug-resistance in gastric cancer through p38MAPK and AKT pathway","volume":"61","author":"Zhang","year":"2014","journal-title":"Neoplasma"},{"key":"2025090405362820100_B36","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1016\/j.joca.2014.07.018","article-title":"Role of C-type natriuretic peptide signalling in maintaining cartilage and bone function","volume":"22","author":"Peake","year":"2014","journal-title":"Osteoarthritis Cartilage"},{"key":"2025090405362820100_B37","first-page":"3813","article-title":"Atrial natriuretic peptide and long acting natriuretic peptide inhibit MEK 1\/2 activation in human prostate cancer cells","volume":"27","author":"Sun","year":"2007","journal-title":"Anticancer Res"},{"key":"2025090405362820100_B38","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1080\/01443615.2022.2062225","article-title":"Loss of PFKFB4 induces cell cycle arrest and glucose metabolism inhibition by inactivating MEK\/ERK\/c-Myc pathway in cervical cancer cells","volume":"42","author":"Wu","year":"2022","journal-title":"J Obstet Gynaecol"},{"key":"2025090405362820100_B39","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41388-022-02540-2","article-title":"CCL18 signaling from tumor-associated macrophages activates fibroblasts to adopt a chemoresistance-inducing phenotype","volume":"42","author":"Zeng","year":"2023","journal-title":"Oncogene"},{"key":"2025090405362820100_B40","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1186\/s13045-020-01000-2","article-title":"Targeting autophagy to overcome drug resistance: further developments","volume":"13","author":"Chang","year":"2020","journal-title":"J Hematol Oncol"},{"key":"2025090405362820100_B41","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1093\/procel\/pwae018","article-title":"Discovery of CCDC188 gene as a novel genetic target for human acephalic spermatozoa syndrome","volume":"15","author":"Wang","year":"2024","journal-title":"Protein Cell"},{"key":"2025090405362820100_B42","doi-asserted-by":"publisher","first-page":"674616","DOI":"10.3389\/fendo.2021.674616","article-title":"Tumor mutation burden predicts relapse in papillary thyroid carcinoma with changes in genes and immune microenvironment","volume":"12","author":"Guo","year":"2021","journal-title":"Front Endocrinol"},{"key":"2025090405362820100_B43","doi-asserted-by":"publisher","first-page":"401","DOI":"10.7150\/jca.89665","article-title":"Identification of basement membrane-related signatures for estimating prognosis, immune infiltration landscape and drug candidates in pancreatic adenocarcinoma","volume":"15","author":"Zhou","year":"2024","journal-title":"J Cancer"},{"key":"2025090405362820100_B44","doi-asserted-by":"publisher","first-page":"12513","DOI":"10.3390\/ijms252312513","article-title":"Proteomic analysis of biomarkers predicting treatment response in patients with head and neck cancers","volume":"25","author":"Zebene","year":"2024","journal-title":"Int J Mol Sci"},{"key":"2025090405362820100_B45","doi-asserted-by":"publisher","first-page":"112230","DOI":"10.1016\/j.celrep.2023.112230","article-title":"FAM193A is a positive regulator of p53 activity","volume":"42","author":"Szwarc","year":"2023","journal-title":"Cell Rep"},{"key":"2025090405362820100_B46","doi-asserted-by":"publisher","first-page":"170","DOI":"10.3390\/cancers10060170","article-title":"The cellular p53 inhibitor MDM2 and the growth factor receptor FLT3 as biomarkers for treatment responses to the MDM2-inhibitor idasanutlin and the MEK1 inhibitor cobimetinib in acute myeloid leukemia","volume":"10","author":"Seipel","year":"2018","journal-title":"Cancers"},{"key":"2025090405362820100_B47","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s41420-021-00465-5","article-title":"MDM2 inhibitor APG-115 exerts potent antitumor activity and synergizes with standard-of-care agents in preclinical acute myeloid leukemia models","volume":"7","author":"Fang","year":"2021","journal-title":"Cell Death Discov"},{"key":"2025090405362820100_B48","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1200\/JCO.1994.12.1.115","article-title":"MDR-1 expression and response to vincristine, doxorubicin, and dexamethasone chemotherapy in multiple myeloma refractory to alkylating agents","volume":"12","author":"Cornelissen","year":"1994","journal-title":"J Clin Oncol"},{"key":"2025090405362820100_B49","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/0006-2952(92)90666-7","article-title":"The role of the MDR1 (P-glycoprotein) gene in multidrug resistance in vitro and in vivo","volume":"43","author":"Roninson","year":"1992","journal-title":"Biochem Pharmacol"},{"key":"2025090405362820100_B50","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1016\/S0024-3205(00)00798-0","article-title":"Inhibition of P-glycoprotein expression and reversal of drug resistance of human hepatoma HepG2 cells by multidrug resistance gene (mdr1) antisense RNA","volume":"67","author":"Chan","year":"2000","journal-title":"Life Sci"},{"key":"2025090405362820100_B51","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1002\/jso.1129","article-title":"Multidrug resistance gene (MDR-1) expression as a useful prognostic factor in patients with human hepatocellular carcinoma after surgical resection","volume":"78","author":"Kato","year":"2001","journal-title":"J Surg Oncol"},{"key":"2025090405362820100_B52","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.canlet.2017.03.011","article-title":"Regorafenib overcomes chemotherapeutic multidrug resistance mediated by ABCB1 transporter in colorectal cancer: in vitro and in vivo study","volume":"396","author":"Wang","year":"2017","journal-title":"Cancer Lett"},{"key":"2025090405362820100_B53","first-page":"1977","article-title":"Modulation of vinblastine resistance in metastatic renal cell carcinoma with cyclosporine A or tamoxifen: a cancer and leukemia group B study","volume":"3","author":"Samuels","year":"1997","journal-title":"Clin Cancer Res"},{"key":"2025090405362820100_B54","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.3892\/ijmm.2017.3124","article-title":"MicroRNA-132 induces temozolomide resistance and promotes the formation of cancer stem cell phenotypes by targeting tumor suppressor candidate 3 in glioblastoma","volume":"40","author":"Cheng","year":"2017","journal-title":"Int J Mol Med"},{"key":"2025090405362820100_B55","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1056\/NEJMoa0804385","article-title":"K-ras mutations and benefit from cetuximab in advanced colorectal cancer","volume":"359","author":"Karapetis","year":"2008","journal-title":"N Engl J Med"},{"key":"2025090405362820100_B56","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1056\/NEJMoa1305275","article-title":"Panitumumab\u2013FOLFOX4 treatment and RAS mutations in colorectal cancer","volume":"369","author":"Douillard","year":"2013","journal-title":"N Engl J Med"},{"key":"2025090405362820100_B57","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1056\/NEJMc0904160","article-title":"BRAF mutation in metastatic colorectal cancer","volume":"361","author":"Tol","year":"2009","journal-title":"N Engl J Med"},{"key":"2025090405362820100_B58","doi-asserted-by":"publisher","first-page":"2619","DOI":"10.1056\/NEJMc060020","article-title":"HER2 mutation and response to trastuzumab therapy in non-small-cell lung cancer","volume":"354","author":"Cappuzzo","year":"2006","journal-title":"N Engl J Med"},{"key":"2025090405362820100_B59","doi-asserted-by":"publisher","first-page":"2487","DOI":"10.1016\/j.cell.2021.03.030","article-title":"Synthetic lethality-mediated precision oncology via the tumor transcriptome","volume":"184","author":"Lee","year":"2021","journal-title":"Cell"},{"key":"2025090405362820100_B60","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.medj.2022.11.001","article-title":"Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome","volume":"4","author":"Dinstag","year":"2023","journal-title":"Med"},{"key":"2025090405362820100_B61","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1038\/s42256-021-00303-4","article-title":"Morphological and molecular breast cancer profiling through explainable machine learning","volume":"3","author":"Binder","year":"2021","journal-title":"Nat Mach Intell"},{"key":"2025090405362820100_B62","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1038\/s43018-024-00891-1","article-title":"Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence","volume":"6","author":"Keyl","year":"2025","journal-title":"Nat Cancer"},{"key":"2025090405362820100_B63","doi-asserted-by":"publisher","first-page":"139","DOI":"10.3390\/cancers15010139","article-title":"Role of patient-derived models of cancer in translational oncology","volume":"15","author":"Idrisova","year":"2022","journal-title":"Cancers"},{"key":"2025090405362820100_B64","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1038\/s41392-023-01419-2","article-title":"Patient-derived xenograft models in cancer therapy: technologies and applications","volume":"8","author":"Liu","year":"2023","journal-title":"Signal Transduct Target Ther"},{"key":"2025090405362820100_B65","doi-asserted-by":"publisher","first-page":"e11017","DOI":"10.15252\/msb.202211017","article-title":"Computational estimation of quality and clinical relevance of cancer cell lines","volume":"18","author":"Trastulla","year":"2022","journal-title":"Mol Syst Biol"}],"container-title":["NAR Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/narcancer\/article-pdf\/7\/3\/zcaf029\/64201080\/zcaf029.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/narcancer\/article-pdf\/7\/3\/zcaf029\/64201080\/zcaf029.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T09:36:39Z","timestamp":1756978599000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/narcancer\/article\/doi\/10.1093\/narcan\/zcaf029\/8246930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"references-count":65,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,8,7]]}},"URL":"https:\/\/doi.org\/10.1093\/narcan\/zcaf029","relation":{},"ISSN":["2632-8674"],"issn-type":[{"value":"2632-8674","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,9]]},"published":{"date-parts":[[2025,8,7]]},"article-number":"zcaf029"}}