{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:11:41Z","timestamp":1777353101622,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor types that are optimally responsive. Open data on the gene expression of the NCI-60 cell lines, provides a unique opportunity to add another dimension to the preclinical development of such drugs by interrogating correlations with gene expression patterns. Machine learning can be used to reduce the complexity of whole genome gene expression patterns to derive manageable signatures of response. Application of machine learning in early phases of preclinical development is likely to allow a better positioning and ultimate clinical success of molecules. LP-184 is a highly potent novel alkylating agent where the preclinical development is being guided by a dedicated machine learning-derived response signature. We show the feasibility and the accuracy of such a signature of response by accurately predicting the response to LP-184 validated using wet lab derived IC50s on a panel of cell lines.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We applied our proprietary RADR\u00ae platform to an NCI-60 discovery dataset encompassing LP-184 IC50s and publicly available gene expression data. We used multiple feature selection layers followed by the XGBoost regression model and reduced the complexity of 20,000 gene expression values to generate a 16-gene signature leading to the identification of a set of predictive candidate biomarkers which form an LP-184 response gene signature. We further validated this signature and predicted response to an additional panel of cell lines. Considering fold change differences and correlation between actual and predicted LP-184 IC50 values as validation performance measures, we obtained 86% accuracy at four-fold cut-off, and a strong (r\u2009=\u20090.70) and significant (p value 1.36e\u221206) correlation between actual and predicted LP-184 sensitivity. In agreement with the perceived mechanism of action of LP-184, PTGR1 emerged as the top weighted gene.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Integration of a machine learning-derived signature of response with in vitro assessment of LP-184 efficacy facilitated the derivation of manageable yet robust biomarkers which can be used to predict drug sensitivity with high accuracy and clinical value.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-021-04040-8","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T11:04:10Z","timestamp":1614683050000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications"],"prefix":"10.1186","volume":"22","author":[{"given":"Umesh","family":"Kathad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Kulkarni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph Ryan","family":"McDermott","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jordan","family":"Wegner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Carr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neha","family":"Biyani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rama","family":"Modali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Philippe","family":"Richard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panna","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kishor","family":"Bhatia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,2]]},"reference":[{"key":"4040_CR1","doi-asserted-by":"publisher","first-page":"5433","DOI":"10.1016\/S0040-4020(01)89489-8","volume":"45","author":"TC McMorris","year":"1989","unstructured":"McMorris TC, Kelner MJ, Chadha RK, Siegel JS, Moon SS, Moya MM. Structure and reactivity of illudins. Tetrahedron. 1989;45:5433\u201340.","journal-title":"Tetrahedron."},{"key":"4040_CR2","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s002800050627","volume":"40","author":"MJ Kelner","year":"1997","unstructured":"Kelner MJ, McMorris TC, Montoya MA, Estes L, Rutherford M, Samson KM, et al. Characterization of cellular accumulation and toxicity of illudin S in sensitive and nonsensitive tumor cells. Cancer Chemother Pharmacol. 1997;40:65\u201371.","journal-title":"Cancer Chemother Pharmacol."},{"key":"4040_CR3","first-page":"279","volume":"57","author":"JR MacDonald","year":"1997","unstructured":"MacDonald JR, Muscoplat CC, Dexter DL, Mangold GL, Chen SF, Kelner MJ, et al. Preclinical antitumor activity of 6-hydroxymethylacylfulvene, a semisynthetic derivative of the mushroom toxin illudin S. Cancer Res. 1997;57:279\u201383.","journal-title":"Cancer Res."},{"key":"4040_CR4","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1021\/np960450y","volume":"59","author":"TC McMorris","year":"1996","unstructured":"McMorris TC, Kelner MJ, Wang W, Yu J, Estes LA, Taetle R. (Hydroxymethyl)acylfulvene: an illudin derivative with superior antitumor properties. J Nat Prod. 1996;59:896\u20139.","journal-title":"J Nat Prod"},{"key":"4040_CR5","doi-asserted-by":"publisher","first-page":"5604","DOI":"10.1158\/1078-0432.CCR-04-0442","volume":"10","author":"F Koeppel","year":"2004","unstructured":"Koeppel F, Poindessous V, Lazar V, Raymond E, Sarasin A, Larsen AK. Irofulven cytotoxicity depends on transcription-coupled nucleotide excision repair and is correlated with XPG expression in solid tumor cells. Clin Cancer Res. 2004;10:5604\u201313.","journal-title":"Clin Cancer Res"},{"key":"4040_CR6","doi-asserted-by":"publisher","first-page":"2101","DOI":"10.1021\/ja0665951","volume":"129","author":"J Gong","year":"2007","unstructured":"Gong J, Vaidyanathan VG, Yu X, Kensler TW, Peterson LA, Sturla SJ. Depurinating acylfulvene-DNA adducts: Characterizing cellular chemical reactions of a selective antitumor agent. J Am Chem Soc. 2007;129:2101\u201311.","journal-title":"J Am Chem Soc"},{"key":"4040_CR7","unstructured":"Pietsch KE, Yu X, Neels JF, Gong J, Sturla SJ. Chemical aspects of acylfulvene bioactivation to a cytotoxic reactive intermediate. In: Abstract of Papers, 238th ACS National Meeting, Washington, DC, United States, August 16\u201320, 2009. 2009."},{"key":"4040_CR8","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/S0006-2952(02)01552-6","volume":"65","author":"MCS Herzig","year":"2003","unstructured":"Herzig MCS, Trevino AV, Liang H, Salinas R, Waters SJ, MacDonald JR, et al. Apoptosis induction by the dual-action DNA- and protein-reactive antitumor drug irofulven is largely Bcl-2-independent. Biochem Pharmacol. 2003;65:503\u201313.","journal-title":"Biochem Pharmacol"},{"key":"4040_CR9","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s10637-008-9113-8","volume":"26","author":"MJ Kelner","year":"2008","unstructured":"Kelner MJ, McMorris TC, Rojas RJ, Estes LA, Suthipinijtham P. Synergy of Irofulven in combination with various anti-metabolites, enzyme inhibitors, and miscellaneous agents in MV522 lung carcinoma cells: marked interaction with gemcitabine and 5-fluorouracil. Invest New Drugs. 2008;26:407\u201315.","journal-title":"Invest New Drugs"},{"key":"4040_CR10","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1158\/1078-0432.CCR-03-0162","volume":"10","author":"RA Dick","year":"2004","unstructured":"Dick RA, Yu X, Kensler TW. NADPH alkenal\/one oxidoreductase activity determines sensitivity of cancer cells to the chemotherapeutic alkylating agent irofulven. Clin Cancer Res. 2004;10:1492\u20139.","journal-title":"Clin Cancer Res"},{"key":"4040_CR11","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1021\/tx2000152","volume":"24","author":"X Liu","year":"2011","unstructured":"Liu X, Pietsch KE, Sturla SJ. Susceptibility of the antioxidant selenoenyzmes thioredoxin reductase and glutathione peroxidase to alkylation-mediated inhibition by anticancer acylfulvenes. Chem Res Toxicol. 2011;24:726\u201336.","journal-title":"Chem Res Toxicol"},{"key":"4040_CR12","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1124\/jpet.112.195768","volume":"343","author":"X Yu","year":"2012","unstructured":"Yu X, Erzinger MM, Pietsch KE, Cervoni-Curet FN, Whang J, Niederhuber J, et al. Up-regulation of human prostaglandin reductase 1 improves the efficacy of hydroxymethylacylfulvene, an antitumor chemotherapeutic agent. J Pharmacol Exp Ther. 2012;343:426\u201333.","journal-title":"J Pharmacol Exp Ther"},{"key":"4040_CR13","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1016\/S1568-7864(02)00166-0","volume":"1","author":"NGJ Jaspers","year":"2002","unstructured":"Jaspers NGJ, Raams A, Kelner MJ, Ng JMY, Yamashita YM, Takeda S, et al. Anti-tumour compounds illudin S and Irofulven induce DNA lesions ignored by global repair and exclusively processed by transcription- and replication-coupled repair pathways. DNA Repair (Amst). 2002;1:1027\u201338.","journal-title":"DNA Repair (Amst)"},{"key":"4040_CR14","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1016\/j.bmcl.2016.02.028","volume":"26","author":"MD Staake","year":"2016","unstructured":"Staake MD, Kashinatham A, McMorris TC, Estes LA, Kelner MJ. Hydroxyurea derivatives of irofulven with improved antitumor efficacy. Bioorg Med Chem Lett. 2016;26:1836\u20138.","journal-title":"Bioorg Med Chem Lett"},{"key":"4040_CR15","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/S0968-0896(99)00016-4","volume":"7","author":"TC McMorris","year":"1999","unstructured":"McMorris TC. Discovery and development of sesquiterpenoid derived hydroxymethylacylfulvene: a new anticancer drug. Bioorganic Med Chem. 1999;7:881\u20136.","journal-title":"Bioorganic Med Chem"},{"key":"4040_CR16","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1023\/A:1006433528750","volume":"19","author":"JE Dowell","year":"2001","unstructured":"Dowell JE, Johnson DH, Rogers JS, Shyr Y, Mccullough N, Krozely P, et al. A phase II trial of 6-hydroxymethylacylfulvene (MGI-114, irofulven) in patients with advanced non-small cell cancer previously treated with chemotherapy. Invest New Drugs. 2001;19:85\u20138.","journal-title":"Invest New Drugs"},{"key":"4040_CR17","doi-asserted-by":"publisher","first-page":"14513","DOI":"10.1200\/jco.2006.24.18_suppl.14513","volume":"24","author":"L Hart","year":"2006","unstructured":"Hart L, Hainsworth J, Oudard S, Berger ER, Alexandre J, Chi KN, Ruether D, MacDonald JR, Cvitkovic ECT. Randomized phase II trial of irofulven (IROF)\/prednisone (P), IROF\/capecitabine (C)\/P or mitoxantrone (M)\/P in docetaxel-pretreated hormone refractory prostate cancer (HRPC) patients (pts). J Clin Oncol. 2006;24:14513\u201314513.","journal-title":"J Clin Oncol"},{"key":"4040_CR18","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1097\/01.coc.0000139019.17349.ed","volume":"28","author":"N Senzer","year":"2005","unstructured":"Senzer N, Arsenau J, Richards D, Berman B, MacDonald JR, Smith S. Irofulven demonstrates clinical activity against metastatic hormone-refractory prostate cancer in a phase 2 single-agent trial. Am J Clin Oncol Cancer Clin Trials. 2005;28:36\u201342.","journal-title":"Am J Clin Oncol Cancer Clin Trials"},{"key":"4040_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2164-7-278","volume":"7","author":"AM Glas","year":"2006","unstructured":"Glas AM, Floore A, Delahaye LJMJ, Witteveen AT, Pover RCF, Bakx N, et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics. 2006;7:1\u201310.","journal-title":"BMC Genomics"},{"key":"4040_CR20","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.ebiom.2018.05.010","volume":"32","author":"Q Hou","year":"2018","unstructured":"Hou Q, Bing ZT, Hu C, Li MY, Yang KH, Mo Z, et al. RankProd combined with genetic algorithm optimized artificial neural network establishes a diagnostic and prognostic prediction model that revealed C1QTNF3 as a biomarker for prostate cancer. EBioMedicine. 2018;32:234\u201344.","journal-title":"EBioMedicine"},{"key":"4040_CR21","first-page":"1377","volume":"41","author":"R Wang","year":"2018","unstructured":"Wang R, Cai Y, Zhang B, Wu Z. A 16-gene expression signature to distinguish stage I from stage II lung squamous carcinoma. Int J Mol Med. 2018;41:1377\u201384.","journal-title":"Int J Mol Med"},{"key":"4040_CR22","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1093\/jnci\/djt202","volume":"105","author":"W Wang","year":"2013","unstructured":"Wang W, Baggerly KA, Knudsen S, Askaa J, Mazin W, Coombes KR. Independent validation of a model using cell line chemosensitivity to predict response to therapy. J Natl Cancer Inst. 2013;105:1284\u201391.","journal-title":"J Natl Cancer Inst"},{"key":"4040_CR23","doi-asserted-by":"publisher","first-page":"e0176763","DOI":"10.1371\/journal.pone.0176763","volume":"12","author":"Y Qin","year":"2017","unstructured":"Qin Y, Conley AP, Grimm EA, Roszik J. A tool for discovering drug sensitivity and gene expression associations in cancer cells. PLoS ONE. 2017;12:e0176763.","journal-title":"PLoS ONE"},{"key":"4040_CR24","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.18632\/oncotarget.23511","volume":"9","author":"A Mohammed","year":"2018","unstructured":"Mohammed A, Biegert G, Adamec J, Helikar T. CancerDiscover: An integrative pipeline for cancer biomarker and cancer class prediction from high-throughput sequencing data. Oncotarget. 2018;9:2565.","journal-title":"Oncotarget"},{"key":"4040_CR25","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.jddst.2015.10.011","volume":"32","author":"L Di Marzio","year":"2016","unstructured":"Di Marzio L, Ventura CA, Cosco D, Paolino D, Di Stefano A, Stancanelli R, et al. Nanotherapeutics for anti-inflammatory delivery. J Drug Deliv Sci Technol. 2016;32:174\u201391.","journal-title":"J Drug Deliv Sci Technol"},{"key":"4040_CR26","doi-asserted-by":"publisher","first-page":"6151","DOI":"10.18632\/oncotarget.3152","volume":"6","author":"P Estevez-Garcia","year":"2015","unstructured":"Estevez-Garcia P, Rivera F, Molina-Pinelo S, Benavent M, G\u00f3mez J, Lim\u00f3n ML, et al. Gene expression profile predictive of response to chemotherapy in metastatic colorectal cancer. Oncotarget. 2015;6:6151.","journal-title":"Oncotarget"},{"key":"4040_CR27","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8\u201317.","journal-title":"Comput Struct Biotechnol J"},{"key":"4040_CR28","first-page":"4188","volume":"17","author":"R Ancuceanu","year":"2019","unstructured":"Ancuceanu R, Dinu M, Neaga I, Laszlo FG, Boda D. Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells. Oncol Lett. 2019;17:4188\u201396.","journal-title":"Oncol Lett"},{"key":"4040_CR29","doi-asserted-by":"publisher","first-page":"e81527","DOI":"10.1371\/journal.pone.0081527","volume":"8","author":"BA McKinney","year":"2013","unstructured":"McKinney BA, White BC, Grill DE, Li PW, Kennedy RB, Poland GA, et al. ReliefSeq: A gene-wise adaptive-k nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data. PLoS ONE. 2013;8:e81527.","journal-title":"PLoS ONE"},{"key":"4040_CR30","first-page":"77","volume":"9","author":"NC De","year":"2016","unstructured":"De NC, Rahman R, Zhao X, Pal R. Algorithms for drug sensitivity prediction Algorithms. 2016;9:77.","journal-title":"Algorithms for drug sensitivity prediction Algorithms"},{"key":"4040_CR31","doi-asserted-by":"publisher","first-page":"e1804","DOI":"10.7717\/peerj.1804","volume":"4","author":"M Shi","year":"2016","unstructured":"Shi M, He J. ColoFinder: a prognostic 9-gene signature improves prognosis for 871 stage II and III colorectal cancer patients. PeerJ. 2016;4:e1804.","journal-title":"PeerJ."},{"key":"4040_CR32","doi-asserted-by":"crossref","unstructured":"Kathad U, Kulkarni A, Richard JP, Lehman T, Modali R, Bhatia K, et al. Abstract 2090: Machine learning-derived gene signature predicts strong sensitivity of several solid tumors to the alkylating agent LP-184. In: 2020.","DOI":"10.1158\/1538-7445.AM2020-2090"},{"key":"4040_CR33","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.isci.2018.11.029","volume":"10","author":"VN Rajapakse","year":"2018","unstructured":"Rajapakse VN, Luna A, Yamade M, Loman L, Varma S, Sunshine M, et al. Cell MinerCDB for integrative cross-database genomics and pharmacogenomics analyses of cancer cell lines. iScience. 2018;10:247\u201364.","journal-title":"iScience"},{"key":"4040_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Rudnicki WR. Feature selection with the boruta package. J Stat Softw. 2010;36:1\u201313.","journal-title":"J Stat Softw"},{"key":"4040_CR35","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1021\/tx7001756","volume":"20","author":"JF Neels","year":"2007","unstructured":"Neels JF, Gong J, Yu X, Sturla SJ. Quantitative correlation of drug bioactivation and deoxyadenosine alkylation by acylfulvene. Chem Res Toxicol. 2007;20:1513\u20139.","journal-title":"Chem Res Toxicol"},{"key":"4040_CR36","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1021\/tx300430r","volume":"26","author":"KE Pietsch","year":"2013","unstructured":"Pietsch KE, Van Midwoud PM, Villalta PW, Sturla SJ. Quantification of acylfulvene- and illudin S-DNA adducts in cells with variable bioactivation capacities. Chem Res Toxicol. 2013;26:146\u201355.","journal-title":"Chem Res Toxicol"},{"key":"4040_CR37","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.prostaglandins.2018.06.003","volume":"137","author":"AT Panagopoulos","year":"2018","unstructured":"Panagopoulos AT, Gomes RN, Almeida FG, da Costa SF, Veiga JCE, Nicolaou A, et al. The prostanoid pathway contains potential prognostic markers for glioblastoma. Prostaglandins Other Lipid Mediat. 2018;137:52\u201362.","journal-title":"Prostaglandins Other Lipid Mediat"},{"key":"4040_CR38","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.biocel.2014.05.017","volume":"53","author":"R S\u00e1nchez-Rodr\u00edguez","year":"2014","unstructured":"S\u00e1nchez-Rodr\u00edguez R, Torres-Mena JE, De-La-Luz-Cruz M, Bernal-Ramos GA, Villa-Trevi\u00f1o S, Chagoya-Hazas V, et al. Increased expression of prostaglandin reductase 1 in hepatocellular carcinomas from clinical cases and experimental tumors in rats. Int J Biochem Cell Biol. 2014;53:186\u201394.","journal-title":"Int J Biochem Cell Biol"},{"key":"4040_CR39","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1093\/jnci\/djy149","volume":"111","author":"E Hatem","year":"2019","unstructured":"Hatem E, Azzi S, El Banna N, He T, Heneman-Masurel A, Vernis L, et al. Auranofin\/Vitamin C: A novel drug combination targeting triple-negative breast cancer. J Natl Cancer Inst. 2019;111:597\u2013608.","journal-title":"J Natl Cancer Inst"},{"key":"4040_CR40","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1002\/cam4.2642","volume":"9","author":"WJ Yang","year":"2020","unstructured":"Yang WJ, Wang HB, Da WW, Bai PY, Lu HX, Sun CH, et al. A network-based predictive gene expression signature for recurrence risks in stage II colorectal cancer. Cancer Med. 2020;9:179\u201393.","journal-title":"Cancer Med"},{"key":"4040_CR41","doi-asserted-by":"publisher","first-page":"3578","DOI":"10.1021\/cr2001367","volume":"112","author":"M Tanasova","year":"2012","unstructured":"Tanasova M, Sturla SJ. Chemistry and biology of acylfulvenes: sesquiterpene-derived antitumor agents. Chem Rev. 2012;112:3578\u2013610.","journal-title":"Chem Rev"},{"key":"4040_CR42","doi-asserted-by":"publisher","first-page":"1674","DOI":"10.1021\/tx400255f","volume":"26","author":"PM Van Midwoud","year":"2013","unstructured":"Van Midwoud PM, Sturla SJ. Improved efficacy of acylfulvene in colon cancer cells when combined with a nuclear excision repair inhibitor. Chem Res Toxicol. 2013;26:1674\u201382.","journal-title":"Chem Res Toxicol"},{"key":"4040_CR43","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1038\/nature10760","volume":"481","author":"CJ Lord","year":"2012","unstructured":"Lord CJ, Ashworth A. The DNA damage response and cancer therapy. Nature. 2012;481:287\u201394.","journal-title":"Nature"},{"key":"4040_CR44","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.bcp.2006.10.023","volume":"73","author":"Y Wang","year":"2007","unstructured":"Wang Y, Wiltshire T, Senft J, Reed E, Wang W. Irofulven induces replication-dependent CHK2 activation related to p53 status. Biochem Pharmacol. 2007;73:469\u201380.","journal-title":"Biochem Pharmacol"},{"key":"4040_CR45","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.molcel.2015.10.040","volume":"60","author":"MJ O\u2019Connor","year":"2015","unstructured":"O\u2019Connor MJ. Targeting the DNA damage response in cancer. Mol Cell. 2015;60:547\u201360.","journal-title":"Mol Cell"},{"key":"4040_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-016-2886-9","volume":"16","author":"S Amin","year":"2016","unstructured":"Amin S, Bathe OF. Response biomarkers: re-envisioning the approach to tailoring drug therapy for cancer. BMC Cancer. 2016;16:1\u201311.","journal-title":"BMC Cancer"},{"key":"4040_CR47","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1080\/17460441.2018.1479740","volume":"13","author":"E Ileana Dumbrava","year":"2018","unstructured":"Ileana Dumbrava E, Meric-Bernstam F, Yap TA. Challenges with biomarkers in cancer drug discovery and development. Expert Opin Drug Discov. 2018;13:685\u201390.","journal-title":"Expert Opin Drug Discov"},{"key":"4040_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1208\/s12248-017-0161-x","volume":"20","author":"VM Lauschke","year":"2018","unstructured":"Lauschke VM, Milani L, Ingelman-Sundberg M. Pharmacogenomic biomarkers for improved drug therapy\u2014recent progress and future developments. AAPS J. 2018;20:1\u201316.","journal-title":"AAPS J"},{"key":"4040_CR49","doi-asserted-by":"crossref","unstructured":"Vougas K, Krochmal M, Jackson T, Polyzos A, Aggelopoulos A, Pateras I, et al. Deep learning and association rule mining for predicting drug response in cancer. A personalised medicine approach. bioRxiv. 2016;","DOI":"10.1101\/070490"},{"key":"4040_CR50","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.ymgme.2019.08.005","volume":"128","author":"PK Rogan","year":"2019","unstructured":"Rogan PK. Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning. Mol Genet Metab. 2019;128:45\u201352.","journal-title":"Mol Genet Metab"},{"key":"4040_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms6901","volume":"6","author":"M Gerstung","year":"2015","unstructured":"Gerstung M, Pellagatti A, Malcovati L, Giagounidis A, Della Porta MG, J\u00e4dersten M, et al. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nat Commun. 2015;6:1\u201311.","journal-title":"Nat Commun"},{"key":"4040_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-19313-8","volume":"11","author":"JH Kong","year":"2020","unstructured":"Kong JH, Lee H, Kim D, Han SK, Ha D, Shin K, et al. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat Commun. 2020;11:1\u201313.","journal-title":"Nat Commun"},{"key":"4040_CR53","doi-asserted-by":"publisher","unstructured":"Uhl\u00e9n M, Fagerberg L, Hallstr\u00f6m BM, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419. https:\/\/doi.org\/10.1126\/science.1260419.","DOI":"10.1126\/science.1260419"},{"key":"4040_CR54","doi-asserted-by":"publisher","first-page":"133","DOI":"10.5582\/bst.2016.01045","volume":"10","author":"L Xue","year":"2016","unstructured":"Xue L, Zhu Z, Wang Z, Li H, Zhang P, Wang Z, et al. Knockdown of prostaglandin reductase 1 (PTGR1) suppresses prostate cancer cell proliferation by inducing cell cycle arrest and apoptosis. Biosci Trends. 2016;10:133\u20139.","journal-title":"Biosci Trends"},{"key":"4040_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-59656-2","volume":"10","author":"R Kurilov","year":"2020","unstructured":"Kurilov R, Haibe-Kains B, Brors B. Assessment of modelling strategies for drug response prediction in cell lines and xenografts. Sci Rep. 2020;10:1\u201311.","journal-title":"Sci Rep"},{"key":"4040_CR56","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.3389\/fgene.2019.01077","volume":"10","author":"W Li","year":"2019","unstructured":"Li W, Yin Y, Quan X, Zhang H. Gene expression value prediction based on XGBoost Algorithm. Front Genet. 2019;10:1077.","journal-title":"Front Genet"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04040-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-021-04040-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04040-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:25:33Z","timestamp":1724559933000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04040-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["4040"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04040-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,2]]},"assertion":[{"value":"25 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Financial competing interests: U.K., A.K., J.R.M., J.W., N.B., P.S. and K.B. are or have been salaried employees of or consultants to the pharmaceutical company Lantern Pharma Inc. U.K., A.K., P.S., and K.B. hold options to purchase common stock of Lantern Pharma Inc. and are also included as inventors on patent applications filed by Lantern Pharma Inc. J.-P.R. and R.M. are employees of REPROCELL USA, Inc., a commercial contract research organization providing services to Lantern Pharma Inc. Non-financial competing interests: The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"102"}}