{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T21:27:21Z","timestamp":1778016441664,"version":"3.51.4"},"reference-count":123,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA226537"],"award-info":[{"award-number":["1R01CA226537"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA226537"],"award-info":[{"award-number":["1R01CA226537"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA253865"],"award-info":[{"award-number":["1R01CA253865"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA226537"],"award-info":[{"award-number":["1R01CA226537"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA222007"],"award-info":[{"award-number":["1R01CA222007"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA253865"],"award-info":[{"award-number":["1R01CA253865"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA226537"],"award-info":[{"award-number":["1R01CA226537"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA222007"],"award-info":[{"award-number":["1R01CA222007"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01AI165372"],"award-info":[{"award-number":["1R01AI165372"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01DK132104"],"award-info":[{"award-number":["1R01DK132104"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["1R01DK133610"],"award-info":[{"award-number":["1R01DK133610"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1930583"],"award-info":[{"award-number":["DMS-1930583"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1930583"],"award-info":[{"award-number":["DMS-1930583"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-022-00377-z","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T17:31:51Z","timestamp":1671471111000},"page":"785-796","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Mathematical modeling of cancer immunotherapy for personalized clinical translation"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0608-2580","authenticated-orcid":false,"given":"Joseph D.","family":"Butner","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6722-7371","authenticated-orcid":false,"given":"Prashant","family":"Dogra","sequence":"additional","affiliation":[]},{"given":"Caroline","family":"Chung","sequence":"additional","affiliation":[]},{"given":"Renata","family":"Pasqualini","sequence":"additional","affiliation":[]},{"given":"Wadih","family":"Arap","sequence":"additional","affiliation":[]},{"given":"John","family":"Lowengrub","sequence":"additional","affiliation":[]},{"given":"Vittorio","family":"Cristini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6262-700X","authenticated-orcid":false,"given":"Zhihui","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"377_CR1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.immuni.2019.06.025","volume":"51","author":"FR Greten","year":"2019","unstructured":"Greten, F. R. & Grivennikov, S. I. Inflammation and cancer: triggers, mechanisms, and consequences. Immunity 51, 27\u201341 (2019).","journal-title":"Immunity"},{"key":"377_CR2","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1146\/annurev-immunol-031210-101324","volume":"29","author":"MD Vesely","year":"2011","unstructured":"Vesely, M. D., Kershaw, M. H., Schreiber, R. D. & Smyth, M. J. Natural innate and adaptive immunity to cancer. Annu. Rev. Immunol. 29, 235\u2013271 (2011).","journal-title":"Annu. Rev. Immunol."},{"key":"377_CR3","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1038\/s41577-020-0306-5","volume":"20","author":"AD Waldman","year":"2020","unstructured":"Waldman, A. D., Fritz, J. M. & Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat. Rev. Immunol. 20, 651\u2013668 (2020).","journal-title":"Nat. Rev. Immunol."},{"key":"377_CR4","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1053\/j.seminoncol.2014.09.004","volume":"41","author":"ML Disis","year":"2014","unstructured":"Disis, M. L. Mechanism of action of immunotherapy. Semin. Oncol. 41, S3\u2013S13 (2014).","journal-title":"Semin. Oncol."},{"key":"377_CR5","doi-asserted-by":"publisher","first-page":"9056173","DOI":"10.1155\/2018\/9056173","volume":"2018","author":"H Choudhry","year":"2018","unstructured":"Choudhry, H. et al. Prospects of IL-2 in cancer immunotherapy. BioMed. Res. Int. 2018, 9056173 (2018).","journal-title":"BioMed. Res. Int."},{"key":"377_CR6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/S1359-6101(01)00022-3","volume":"13","author":"F Belardelli","year":"2002","unstructured":"Belardelli, F., Ferrantini, M., Proietti, E. & Kirkwood, J. M. Interferon-alpha in tumor immunity and immunotherapy. Cytokine Growth Factor Rev. 13, 119\u2013134 (2002).","journal-title":"Cytokine Growth Factor Rev."},{"key":"377_CR7","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s41416-018-0328-y","volume":"120","author":"P Berraondo","year":"2019","unstructured":"Berraondo, P. et al. Cytokines in clinical cancer immunotherapy. Br. J. Cancer 120, 6\u201315 (2019).","journal-title":"Br. J. Cancer"},{"key":"377_CR8","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/nrc2355","volume":"8","author":"SA Rosenberg","year":"2008","unstructured":"Rosenberg, S. A., Restifo, N. P., Yang, J. C., Morgan, R. A. & Dudley, M. E. Adoptive cell transfer: a clinical path to effective cancer immunotherapy. Nat. Rev. Cancer 8, 299\u2013308 (2008).","journal-title":"Nat. Rev. Cancer"},{"key":"377_CR9","doi-asserted-by":"publisher","first-page":"738","DOI":"10.3390\/cancers12030738","volume":"12","author":"RK Vaddepally","year":"2020","unstructured":"Vaddepally, R. K., Kharel, P., Pandey, R., Garje, R. & Chandra, A. B. Review of indications of FDA-approved immune checkpoint inhibitors per NCCN guidelines with the level of evidence. Cancers 12, 738 (2020).","journal-title":"Cancers"},{"key":"377_CR10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1158\/2326-6066.CIR-15-0064","volume":"3","author":"LA Emens","year":"2015","unstructured":"Emens, L. A. & Middleton, G. The interplay of immunotherapy and chemotherapy: harnessing potential synergies. Cancer Immunol. Res. 3, 436\u2013443 (2015).","journal-title":"Cancer Immunol. Res."},{"key":"377_CR11","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3389\/fphar.2018.00185","volume":"9","author":"Y Wang","year":"2018","unstructured":"Wang, Y. et al. Combining immunotherapy and radiotherapy for cancer treatment: current challenges and future directions. Front. Pharmacol. 9, 185 (2018).","journal-title":"Front. Pharmacol."},{"key":"377_CR12","unstructured":"A to Z List of Cancer Drugs (National Cancer Institute, 2021); https:\/\/www.cancer.gov\/about-cancer\/treatment\/drugs"},{"key":"377_CR13","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1038\/nrc4029","volume":"15","author":"PM Altrock","year":"2015","unstructured":"Altrock, P. M., Liu, L. L. & Michor, F. The mathematics of cancer: integrating quantitative models. Nat. Rev. Cancer 15, 730\u2013745 (2015).","journal-title":"Nat. Rev. Cancer"},{"key":"377_CR14","doi-asserted-by":"publisher","first-page":"20170150","DOI":"10.1098\/rsif.2017.0150","volume":"14","author":"A Konstorum","year":"2017","unstructured":"Konstorum, A., Vella, A. T., Adler, A. J. & Laubenbacher, R. C. Addressing current challenges in cancer immunotherapy with mathematical and computational modelling. J. R. Soc. Interface 14, 20170150 (2017).","journal-title":"J. R. Soc. Interface"},{"key":"377_CR15","doi-asserted-by":"publisher","first-page":"100534","DOI":"10.1016\/j.imu.2021.100534","volume":"23","author":"J Malinzi","year":"2021","unstructured":"Malinzi, J., Basita, K. B., Padidar, S. & Adeola, H. A. Prospect for application of mathematical models in combination cancer treatments. Inform. Med. Unlocked 23, 100534 (2021).","journal-title":"Inform. Med. Unlocked"},{"key":"377_CR16","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s11538-010-9526-3","volume":"73","author":"R Eftimie","year":"2011","unstructured":"Eftimie, R., Bramson, J. L. & Earn, D. J. Interactions between the immune system and cancer: a brief review of non-spatial mathematical models. Bull. Math. Biol. 73, 2\u201332 (2011).","journal-title":"Bull. Math. Biol."},{"key":"377_CR17","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.jtho.2015.09.006","volume":"11","author":"C Lim","year":"2016","unstructured":"Lim, C. et al. Patients with advanced non-small cell lung cancer: are research biopsies a barrier to participation in clinical trials? J. Thorac. Oncol. 11, 79\u201384 (2016).","journal-title":"J. Thorac. Oncol."},{"key":"377_CR18","first-page":"492","volume":"3","author":"M Artzrouni","year":"2011","unstructured":"Artzrouni, M. et al. The first international workshop on the role and impact of mathematics in medicine: a collective account. Am. J. Transl. Res 3, 492\u2013497 (2011).","journal-title":"Am. J. Transl. Res"},{"key":"377_CR19","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1038\/nrclinonc.2015.204","volume":"13","author":"D Barbolosi","year":"2016","unstructured":"Barbolosi, D., Ciccolini, J., Lacarelle, B., Barl\u00e9si, F. & Andr\u00e9, N. Computational oncology\u2014mathematical modelling of drug regimens for precision medicine. Nat. Rev. Clin. Oncol. 13, 242\u2013254 (2016).","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"377_CR20","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s12911-020-1039-x","volume":"20","author":"K Hoffmann","year":"2020","unstructured":"Hoffmann, K. et al. Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. BMC Med Inf. Decis. Mak. 20, 28 (2020).","journal-title":"BMC Med Inf. Decis. Mak."},{"key":"377_CR21","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1146\/annurev-bioeng-071910-124729","volume":"13","author":"TS Deisboeck","year":"2011","unstructured":"Deisboeck, T. S., Wang, Z., Macklin, P. & Cristini, V. Multiscale cancer modeling. Annu. Rev. Biomed. Eng. 13, 127\u2013155 (2011).","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"377_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1200\/CCI.18.00068","volume":"3","author":"S Hamis","year":"2019","unstructured":"Hamis, S., Powathil, G. G. & Chaplain, M. A. J. Blackboard to bedside: a mathematical modeling bottom-up approach toward personalized cancer treatments. JCO Clin. Cancer Inform. 3, 1\u201311 (2019).","journal-title":"JCO Clin. Cancer Inform."},{"key":"377_CR23","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1007\/s10439-016-1691-6","volume":"44","author":"TE Yankeelov","year":"2016","unstructured":"Yankeelov, T. E. et al. Multi-scale modeling in clinical oncology: opportunities and barriers to success. Ann. Biomed. Eng. 44, 2626\u20132641 (2016).","journal-title":"Ann. Biomed. Eng."},{"key":"377_CR24","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.semcancer.2014.04.001","volume":"30","author":"Z Wang","year":"2015","unstructured":"Wang, Z., Butner, J. D., Kerketta, R., Cristini, V. & Deisboeck, T. S. Simulating cancer growth with multiscale agent-based modeling. Semin. Cancer Biol. 30, 70\u201378 (2015).","journal-title":"Semin. Cancer Biol."},{"key":"377_CR25","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1002\/psp4.12450","volume":"8","author":"A Yin","year":"2019","unstructured":"Yin, A., Moes, D., van Hasselt, J. G. C., Swen, J. J. & Guchelaar, H. J. A review of mathematical models for tumor dynamics and treatment resistance evolution of solid tumors. CPT Pharmacomet. Syst. Pharm. 8, 720\u2013737 (2019).","journal-title":"CPT Pharmacomet. Syst. Pharm."},{"key":"377_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.plrev.2021.10.001","volume":"39","author":"M Kuznetsov","year":"2021","unstructured":"Kuznetsov, M., Clairambault, J. & Volpert, V. Improving cancer treatments via dynamical biophysical models. Phys. Life Rev. 39, 1\u201348 (2021).","journal-title":"Phys. Life Rev."},{"key":"377_CR27","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1109\/JPROC.2021.3136715","volume":"110","author":"JA Bull","year":"2022","unstructured":"Bull, J. A. & Byrne, H. M. The hallmarks of mathematical oncology. Proc. IEEE 110, 523\u2013540 (2022).","journal-title":"Proc. IEEE"},{"key":"377_CR28","doi-asserted-by":"crossref","unstructured":"Cristini, V., Koay, E. & Wang, Z. An Introduction to Physical Oncology: How Mechanistic Mathematical Modeling Can Improve Cancer Therapy Outcomes (CRC Press, 2017).","DOI":"10.4324\/9781315374499"},{"key":"377_CR29","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.trecan.2018.02.005","volume":"4","author":"T Stylianopoulos","year":"2018","unstructured":"Stylianopoulos, T., Munn, L. L. & Jain, R. K. Reengineering the physical microenvironment of tumors to improve drug delivery and efficacy: from mathematical modeling to bench to bedside. Trends Cancer 4, 292\u2013319 (2018).","journal-title":"Trends Cancer"},{"key":"377_CR30","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jconrel.2015.06.026","volume":"212","author":"MB Ulmschneider","year":"2015","unstructured":"Ulmschneider, M. B. & Searson, P. C. Mathematical models of the steps involved in the systemic delivery of a chemotherapeutic to a solid tumor: from circulation to survival. J. Control. Release 212, 78\u201384 (2015).","journal-title":"J. Control. Release"},{"key":"377_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10544-019-0380-2","volume":"21","author":"P Dogra","year":"2019","unstructured":"Dogra, P. et al. Mathematical modeling in cancer nanomedicine: a review. Biomed. Microdevices 21, 40 (2019).","journal-title":"Biomed. Microdevices"},{"key":"377_CR32","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1038\/nrc.2017.93","volume":"17","author":"MW Dewhirst","year":"2017","unstructured":"Dewhirst, M. W. & Secomb, T. W. Transport of drugs from blood vessels to tumour tissue. Nat. Rev. Cancer 17, 738\u2013750 (2017).","journal-title":"Nat. Rev. Cancer"},{"key":"377_CR33","doi-asserted-by":"publisher","first-page":"278","DOI":"10.3389\/fonc.2013.00278","volume":"3","author":"M Kim","year":"2013","unstructured":"Kim, M., Gillies, R. J. & Rejniak, K. A. Current advances in mathematical modeling of anti-cancer drug penetration into tumor tissues. Front. Oncol. 3, 278 (2013).","journal-title":"Front. Oncol."},{"key":"377_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40139-020-00219-5","volume":"9","author":"N Sahai","year":"2021","unstructured":"Sahai, N., Gogoi, M. & Ahmad, N. Mathematical Modeling and simulations for developing nanoparticle-based cancer drug delivery systems: a review. Curr. Pathobiol. Rep. 9, 1\u20138 (2021).","journal-title":"Curr. Pathobiol. Rep."},{"key":"377_CR35","doi-asserted-by":"publisher","first-page":"e1628","DOI":"10.1002\/wnan.1628","volume":"12","author":"P Dogra","year":"2020","unstructured":"Dogra, P. et al. Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 12, e1628 (2020).","journal-title":"Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol."},{"key":"377_CR36","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.3389\/fimmu.2020.01376","volume":"11","author":"AP Tran","year":"2020","unstructured":"Tran, A. P. et al. Delicate balances in cancer chemotherapy: modeling immune recruitment and emergence of systemic drug resistance. Front. Immunol. 11, 1376 (2020).","journal-title":"Front. Immunol."},{"key":"377_CR37","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jtbi.2019.03.002","volume":"469","author":"GE Mahlbacher","year":"2019","unstructured":"Mahlbacher, G. E., Reihmer, K. C. & Frieboes, H. B. Mathematical modeling of tumor-immune cell interactions. J. Theor. Biol. 469, 47\u201360 (2019).","journal-title":"J. Theor. Biol."},{"key":"377_CR38","doi-asserted-by":"publisher","unstructured":"Metzcar, J., Wang, Y., Heiland, R. & Macklin, P. A review of cell-based computational modeling in cancer biology. JCO Clin. Cancer Inform. https:\/\/doi.org\/10.1200\/cci.18.00069 (2019).","DOI":"10.1200\/cci.18.00069"},{"key":"377_CR39","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10928-015-9403-7","volume":"42","author":"Z Wang","year":"2015","unstructured":"Wang, Z., Butner, J. D., Cristini, V. & Deisboeck, T. S. Integrated PK-PD and agent-based modeling in oncology. J. Pharmacokinet. Pharmacodyn. 42, 179\u2013189 (2015).","journal-title":"J. Pharmacokinet. Pharmacodyn."},{"key":"377_CR40","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1051\/mmnp\/20127312","volume":"7","author":"F Pappalardo","year":"2012","unstructured":"Pappalardo, F., Palladini, A., Pennisi, M., Castiglione, F. & Motta, S. J. M. M. N. P. Mathematical and computational models in tumor. Immunol. Math. Model Nat. Phenom. 7, 186\u2013203 (2012).","journal-title":"Immunol. Math. Model Nat. Phenom."},{"key":"377_CR41","doi-asserted-by":"crossref","unstructured":"Dr\u00e9au, D., Stanimirov, D., Carmichael, T. & Hadzikadic, M. An agent-based model of solid tumor progression. In Bioinformatics and Computational Biology, BICoB 2009, vol. 5462, 187\u2013198 (Springer, 2009).","DOI":"10.1007\/978-3-642-00727-9_19"},{"key":"377_CR42","doi-asserted-by":"publisher","first-page":"907171","DOI":"10.1155\/2014\/907171","volume":"2014","author":"F Chiacchio","year":"2014","unstructured":"Chiacchio, F., Pennisi, M., Russo, G., Motta, S. & Pappalardo, F. Agent-based modeling of the immune system: NetLogo, a promising framework. BioMed. Res. Int. 2014, 907171 (2014).","journal-title":"BioMed. Res. Int."},{"key":"377_CR43","doi-asserted-by":"publisher","first-page":"20170320","DOI":"10.1098\/rsif.2017.0320","volume":"14","author":"C Gong","year":"2017","unstructured":"Gong, C. et al. A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition. J. R. Soc. Interface 14, 20170320 (2017).","journal-title":"J. R. Soc. Interface"},{"key":"377_CR44","doi-asserted-by":"publisher","first-page":"e1005991","DOI":"10.1371\/journal.pcbi.1005991","volume":"14","author":"A Ghaffarizadeh","year":"2018","unstructured":"Ghaffarizadeh, A., Heiland, R., Friedman, S. H., Mumenthaler, S. M. & Macklin, P. PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol. 14, e1005991 (2018).","journal-title":"PLoS Comput. Biol."},{"key":"377_CR45","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1186\/s12859-018-2510-x","volume":"19","author":"J Ozik","year":"2018","unstructured":"Ozik, J. et al. High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. BMC Bioinf. 19, 483 (2018).","journal-title":"BMC Bioinf."},{"key":"377_CR46","doi-asserted-by":"publisher","first-page":"7950","DOI":"10.1158\/0008-5472.CAN-05-0564","volume":"65","author":"LG de Pillis","year":"2005","unstructured":"de Pillis, L. G., Radunskaya, A. E. & Wiseman, C. L. A validated mathematical model of cell-mediated immune response to tumor growth. Cancer Res. 65, 7950\u20137958 (2005).","journal-title":"Cancer Res."},{"key":"377_CR47","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1007\/s10928-014-9386-9","volume":"41","author":"LG dePillis","year":"2014","unstructured":"dePillis, L. G., Eladdadi, A. & Radunskaya, A. E. Modeling cancer-immune responses to therapy. J. Pharmacokinet. Pharmacodyn. 41, 461\u2013478 (2014).","journal-title":"J. Pharmacokinet. Pharmacodyn."},{"key":"377_CR48","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1089\/nsm.2020.0002","volume":"3","author":"E Stalidzans","year":"2020","unstructured":"Stalidzans, E. et al. Mechanistic modeling and multiscale applications for precision medicine: theory and practice. Netw. Syst. Med. 3, 36\u201356 (2020).","journal-title":"Netw. Syst. Med."},{"key":"377_CR49","first-page":"917","volume":"24","author":"NV Stepanova","year":"1979","unstructured":"Stepanova, N. V. Course of the immune reaction during the development of a malignant tumour. Biophysics 24, 917\u2013923 (1979).","journal-title":"Biophysics"},{"key":"377_CR50","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1038\/nature24473","volume":"551","author":"M \u0141uksza","year":"2017","unstructured":"\u0141uksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517\u2013520 (2017).","journal-title":"Nature"},{"key":"377_CR51","doi-asserted-by":"publisher","first-page":"4931","DOI":"10.1158\/0008-5472.CAN-15-3567","volume":"76","author":"R Serre","year":"2016","unstructured":"Serre, R. et al. Mathematical modeling of cancer immunotherapy and Its synergy with radiotherapy. Cancer Res. 76, 4931\u20134940 (2016).","journal-title":"Cancer Res."},{"key":"377_CR52","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1016\/j.ijrobp.2021.11.008","volume":"112","author":"W Sung","year":"2022","unstructured":"Sung, W., Hong, T. S., Poznansky, M. C., Paganetti, H. & Grassberger, C. Mathematical modeling to simulate the effect of adding radiation therapy to immunotherapy and application to hepatocellular carcinoma. Int. J. Radiat. Oncol. Biol. Phys. 112, 1055\u20131062 (2022).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"377_CR53","doi-asserted-by":"publisher","first-page":"5171","DOI":"10.3390\/cancers13205171","volume":"13","author":"V Adhikarla","year":"2021","unstructured":"Adhikarla, V. et al. A mathematical modeling approach for targeted radionuclide and chimeric antigen receptor T cell combination therapy. Cancers 13, 5171 (2021).","journal-title":"Cancers"},{"key":"377_CR54","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.3934\/dcdsb.2020138","volume":"26","author":"N Elpiniki","year":"2021","unstructured":"Elpiniki, N., Steffen, E. E., Jana, L. G. & Yang, K. Mathematical modeling of an immune checkpoint inhibitor and its synergy with an immunostimulant. Discret. Continuous Dynamical Syst. B 26, 2133\u20132159 (2021).","journal-title":"Discret. Continuous Dynamical Syst. B"},{"key":"377_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1200\/CCI.18.00078","volume":"3","author":"J West","year":"2019","unstructured":"West, J. et al. The immune checkpoint kick start: optimization of neoadjuvant combination therapy using game theory. JCO Clin. Cancer Inform. 3, 1\u201312 (2019).","journal-title":"JCO Clin. Cancer Inform."},{"key":"377_CR56","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/psp4.12130","volume":"6","author":"A Lindauer","year":"2017","unstructured":"Lindauer, A. et al. Translational pharmacokinetic\/pharmacodynamic modeling of tumor growth inhibition supports dose-range selection of the anti-PD-1 antibody pembrolizumab. CPT Pharmacomet. Syst. Pharm. 6, 11\u201320 (2017).","journal-title":"CPT Pharmacomet. Syst. Pharm."},{"key":"377_CR57","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s10928-011-9232-2","volume":"39","author":"DK Shah","year":"2012","unstructured":"Shah, D. K. & Betts, A. M. Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human. J. Pharmacokinet. Pharmacodyn. 39, 67\u201386 (2012).","journal-title":"J. Pharmacokinet. Pharmacodyn."},{"key":"377_CR58","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1158\/0008-5472.CAN-03-2524","volume":"64","author":"M Simeoni","year":"2004","unstructured":"Simeoni, M. et al. Predictive pharmacokinetic\u2013pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res. 64, 1094\u20131101 (2004).","journal-title":"Cancer Res."},{"key":"377_CR59","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1002\/cpt.393","volume":"100","author":"X Chen","year":"2016","unstructured":"Chen, X. et al. Mechanistic projection of first-in-human dose for bispecific immunomodulatory P-cadherin LP-DART: an integrated PK\/PD modeling approach. Clin. Pharmacol. Ther. 100, 232\u2013241 (2016).","journal-title":"Clin. Pharmacol. Ther."},{"key":"377_CR60","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/s00228-001-0405-6","volume":"57","author":"BG Reigner","year":"2002","unstructured":"Reigner, B. G. & Blesch, K. S. Estimating the starting dose for entry into humans: principles and practice. Eur. J. Clin. Pharmacol. 57, 835\u2013845 (2002).","journal-title":"Eur. J. Clin. Pharmacol."},{"key":"377_CR61","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1124\/jpet.109.164129","volume":"333","author":"AM Betts","year":"2010","unstructured":"Betts, A. M. et al. The application of target information and preclinical pharmacokinetic\/pharmacodynamic modeling in predicting clinical doses of a Dickkopf-1 antibody for osteoporosis. J. Pharmacol. Exp. Ther. 333, 2\u201313 (2010).","journal-title":"J. Pharmacol. Exp. Ther."},{"key":"377_CR62","doi-asserted-by":"publisher","first-page":"734515","DOI":"10.1155\/2014\/734515","volume":"2014","author":"D Escors","year":"2014","unstructured":"Escors, D. Tumour immunogenicity, antigen presentation and immunological barriers in cancer immunotherapy. New J. Sci. 2014, 734515 (2014).","journal-title":"New J. Sci."},{"key":"377_CR63","doi-asserted-by":"publisher","first-page":"5155","DOI":"10.1158\/0008-5472.CAN-18-1126","volume":"78","author":"JN Kather","year":"2018","unstructured":"Kather, J. N. et al. High-throughput screening of combinatorial immunotherapies with patient-specific in silico models of metastatic colorectal cancer. Cancer Res. 78, 5155\u20135163 (2018).","journal-title":"Cancer Res."},{"key":"377_CR64","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1056\/NEJMoa1801005","volume":"378","author":"L Gandhi","year":"2018","unstructured":"Gandhi, L. et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N. Engl. J. Med. 378, 2078\u20132092 (2018).","journal-title":"N. Engl. J. Med."},{"key":"377_CR65","doi-asserted-by":"publisher","first-page":"e1010254","DOI":"10.1371\/journal.pcbi.1010254","volume":"18","author":"A Ruiz-Martinez","year":"2022","unstructured":"Ruiz-Martinez, A. et al. Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model. PLoS Comput. Biol. 18, e1010254 (2022).","journal-title":"PLoS Comput. Biol."},{"key":"377_CR66","doi-asserted-by":"publisher","first-page":"104702","DOI":"10.1016\/j.isci.2022.104702","volume":"25","author":"H Wang","year":"2022","unstructured":"Wang, H., Zhao, C., Santa-Maria, C. A., Emens, L. A. & Popel, A. S. Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer. iScience 25, 104702 (2022).","journal-title":"iScience"},{"key":"377_CR67","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.1158\/1078-0432.CCR-17-2953","volume":"24","author":"B Ribba","year":"2018","unstructured":"Ribba, B. et al. Prediction of the optimal dosing regimen using a mathematical model of tumor uptake for immunocytokine-based cancer immunotherapy. Clin. Cancer Res. 24, 3325\u20133333 (2018).","journal-title":"Clin. Cancer Res."},{"key":"377_CR68","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1158\/1535-7163.MCT-09-0195","volume":"8","author":"MM Schmidt","year":"2009","unstructured":"Schmidt, M. M. & Wittrup, K. D. A modeling analysis of the effects of molecular size and binding affinity on tumor targeting. Mol. Cancer Ther. 8, 2861\u20132871 (2009).","journal-title":"Mol. Cancer Ther."},{"key":"377_CR69","doi-asserted-by":"publisher","first-page":"16072","DOI":"10.1073\/pnas.1918937117","volume":"117","author":"JI Griffiths","year":"2020","unstructured":"Griffiths, J. I. et al. Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy. Proc. Natl Acad. Sci. USA 117, 16072\u201316082 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"377_CR70","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-019-2081-2","volume":"17","author":"N Tsur","year":"2019","unstructured":"Tsur, N. et al. Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm. J. Transl. Med 17, 338 (2019).","journal-title":"J. Transl. Med"},{"key":"377_CR71","doi-asserted-by":"publisher","first-page":"110033","DOI":"10.1016\/j.jtbi.2019.110033","volume":"485","author":"N Tsur","year":"2020","unstructured":"Tsur, N., Kogan, Y., Rehm, M. & Agur, Z. Response of patients with melanoma to immune checkpoint blockade\u2014insights gleaned from analysis of a new mathematical mechanistic model. J. Theor. Biol. 485, 110033 (2020).","journal-title":"J. Theor. Biol."},{"key":"377_CR72","doi-asserted-by":"publisher","first-page":"eaay6298","DOI":"10.1126\/sciadv.aay6298","volume":"6","author":"JD Butner","year":"2020","unstructured":"Butner, J. D. et al. Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy. Sci. Adv. 6, eaay6298 (2020).","journal-title":"Sci. Adv."},{"key":"377_CR73","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1038\/s41551-020-00662-0","volume":"5","author":"JD Butner","year":"2021","unstructured":"Butner, J. D. et al. A mathematical model for the quantification of a patient\u2019s sensitivity to checkpoint inhibitors and long-term tumour burden. Nat. Biomed. Eng. 5, 297\u2013308 (2021).","journal-title":"Nat. Biomed. Eng."},{"key":"377_CR74","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1158\/1078-0432.CCR-16-1432","volume":"23","author":"C Tang","year":"2017","unstructured":"Tang, C. et al. Ipilimumab with stereotactic ablative radiation therapy: phase I results and immunologic correlates from peripheral T cells. Clin. Cancer Res. 23, 1388\u20131396 (2017).","journal-title":"Clin. Cancer Res."},{"key":"377_CR75","doi-asserted-by":"publisher","first-page":"e70130","DOI":"10.7554\/eLife.70130","volume":"10","author":"JD Butner","year":"2021","unstructured":"Butner, J. D. et al. Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling. eLife 10, e70130 (2021).","journal-title":"eLife"},{"key":"377_CR76","doi-asserted-by":"publisher","first-page":"2782","DOI":"10.3390\/cancers13112782","volume":"13","author":"A Mueller-Schoell","year":"2021","unstructured":"Mueller-Schoell, A. et al. Early survival prediction framework in CD19-specific CAR-T cell immunotherapy using a quantitative systems pharmacology model. Cancers 13, 2782 (2021).","journal-title":"Cancers"},{"key":"377_CR77","doi-asserted-by":"publisher","first-page":"110789","DOI":"10.1016\/j.chaos.2021.110789","volume":"145","author":"P Das","year":"2021","unstructured":"Das, P. et al. Optimal control strategy for cancer remission using combinatorial therapy: a mathematical model-based approach. Chaos Solitons Fractals 145, 110789 (2021).","journal-title":"Chaos Solitons Fractals"},{"key":"377_CR78","doi-asserted-by":"publisher","first-page":"e6277","DOI":"10.1073\/pnas.1703355114","volume":"114","author":"S Barish","year":"2017","unstructured":"Barish, S., Ochs, M. F., Sontag, E. D. & Gevertz, J. L. Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy. Proc. Natl Acad. Sci. USA 114, e6277\u2013e6286 (2017).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"377_CR79","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.jconrel.2022.03.008","volume":"345","author":"F Mpekris","year":"2022","unstructured":"Mpekris, F. et al. Normalizing tumor microenvironment with nanomedicine and metronomic therapy to improve immunotherapy. J. Control. Release 345, 190\u2013199 (2022).","journal-title":"J. Control. Release"},{"key":"377_CR80","first-page":"118","volume":"349","author":"DS Rodrigues","year":"2019","unstructured":"Rodrigues, D. S., Mancera, P. F. A., Carvalho, T. & Gon\u00e7alves, L. F. A mathematical model for chemoimmunotherapy of chronic lymphocytic leukemia. Appl. Math. Comput. 349, 118\u2013133 (2019).","journal-title":"Appl. Math. Comput."},{"key":"377_CR81","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.1073\/pnas.1919764117","volume":"117","author":"F Mpekris","year":"2020","unstructured":"Mpekris, F. et al. Combining microenvironment normalization strategies to improve cancer immunotherapy. Proc. Natl Acad. Sci. USA 117, 3728\u20133737 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"377_CR82","doi-asserted-by":"publisher","first-page":"9063","DOI":"10.1038\/s41598-020-65590-0","volume":"10","author":"R Coletti","year":"2020","unstructured":"Coletti, R., Leonardelli, L., Parolo, S. & Marchetti, L. A QSP model of prostate cancer immunotherapy to identify effective combination therapies. Sci. Rep. 10, 9063 (2020).","journal-title":"Sci. Rep."},{"key":"377_CR83","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1080\/19466315.2018.1437071","volume":"10","author":"Q Liu","year":"2018","unstructured":"Liu, Q., Yin, X., Languino, L. R. & Altieri, D. C. Evaluation of drug combination effect using a Bliss independence dose-response surface model. Stat. Biopharm. Res. 10, 112\u2013122 (2018).","journal-title":"Stat. Biopharm. Res."},{"key":"377_CR84","doi-asserted-by":"publisher","first-page":"104","DOI":"10.3389\/fbioe.2019.00104","volume":"7","author":"MA Benchaib","year":"2019","unstructured":"Benchaib, M. A., Bouchnita, A., Volpert, V. & Makhoute, A. Mathematical modeling reveals that the administration of EGF can promote the elimination of lymph node metastases by PD-1\/PD-L1 blockade. Front. Bioeng. Biotechnol. 7, 104 (2019).","journal-title":"Front. Bioeng. Biotechnol."},{"key":"377_CR85","doi-asserted-by":"publisher","first-page":"2906282","DOI":"10.1155\/2017\/2906282","volume":"2017","author":"HC Wei","year":"2017","unstructured":"Wei, H. C., Yu, J. L. & Hsu, C. Y. Periodically pulsed immunotherapy in a mathematical model of tumor, CD4+ T cells, and antitumor cytokine interactions. Comput. Math. Methods Med. 2017, 2906282 (2017).","journal-title":"Comput. Math. Methods Med."},{"key":"377_CR86","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/s12918-019-0706-y","volume":"13","author":"X Lai","year":"2019","unstructured":"Lai, X. & Friedman, A. How to schedule VEGF and PD-1 inhibitors in combination cancer therapy? BMC Syst. Biol. 13, 30 (2019).","journal-title":"BMC Syst. Biol."},{"key":"377_CR87","doi-asserted-by":"publisher","first-page":"111172","DOI":"10.1016\/j.jtbi.2022.111172","volume":"547","author":"G Pozzi","year":"2022","unstructured":"Pozzi, G. et al. T cell therapy against cancer: a predictive diffuse-interface mathematical model informed by pre-clinical studies. J. Theor. Biol. 547, 111172 (2022).","journal-title":"J. Theor. Biol."},{"key":"377_CR88","first-page":"537","volume":"10","author":"J Welsh","year":"2020","unstructured":"Welsh, J. et al. Abscopal effect following radiation therapy in cancer patients: a new look from the immunological point of view. J. Biomed. Phys. Eng. 10, 537\u2013542 (2020).","journal-title":"J. Biomed. Phys. Eng."},{"key":"377_CR89","doi-asserted-by":"publisher","first-page":"5458","DOI":"10.1158\/0008-5472.CAN-14-1258","volume":"74","author":"SJ Dovedi","year":"2014","unstructured":"Dovedi, S. J. et al. Acquired resistance to fractionated radiotherapy can be overcome by concurrent PD-L1 blockade. Cancer Res. 74, 5458\u20135468 (2014).","journal-title":"Cancer Res."},{"key":"377_CR90","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40425-018-0327-9","volume":"6","author":"Y Kosinsky","year":"2018","unstructured":"Kosinsky, Y. et al. Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model. J. Immunother. Cancer 6, 17 (2018).","journal-title":"J. Immunother. Cancer"},{"key":"377_CR91","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1002\/wcms.1240","volume":"6","author":"AB Raies","year":"2016","unstructured":"Raies, A. B. & Bajic, V. B. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip. Rev. Comput. Mol. Sci. 6, 147\u2013172 (2016).","journal-title":"Wiley Interdiscip. Rev. Comput. Mol. Sci."},{"key":"377_CR92","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.2217\/imt-2021-0209","volume":"13","author":"JD Butner","year":"2021","unstructured":"Butner, J. D. & Wang, Z. Predicting immune checkpoint inhibitor response with mathematical modeling. Immunotherapy 13, 1151\u20131155 (2021).","journal-title":"Immunotherapy"},{"key":"377_CR93","doi-asserted-by":"publisher","unstructured":"Reticker-Flynn, N. E. & Engleman, E. G. Cancer systems immunology. eLife https:\/\/doi.org\/10.7554\/eLife.53839 (2020).","DOI":"10.7554\/eLife.53839"},{"key":"377_CR94","first-page":"117","volume":"101","author":"KL Kiran","year":"2010","unstructured":"Kiran, K. L. & Lakshminarayanan, S. Global sensitivity analysis and model-based reactive scheduling of targeted cancer immunotherapy. Bio Syst. 101, 117\u2013126 (2010).","journal-title":"Bio Syst."},{"key":"377_CR95","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.csbj.2020.02.014","volume":"18","author":"P Dogra","year":"2020","unstructured":"Dogra, P. et al. A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery. Comput. Struct. Biotechnol. J. 18, 518\u2013531 (2020).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"377_CR96","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/s11095-022-03176-3","volume":"39","author":"P Dogra","year":"2022","unstructured":"Dogra, P. et al. Translational modeling identifies synergy between nanoparticle-delivered miRNA-22 and standard-of-care drugs in triple-negative breast cancer. Pharm. Res. 39, 511\u2013528 (2022).","journal-title":"Pharm. Res."},{"key":"377_CR97","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1038\/s41416-020-01188-7","volume":"124","author":"M Shirasawa","year":"2021","unstructured":"Shirasawa, M. et al. Prognostic impact of peripheral blood neutrophil to lymphocyte ratio in advanced-stage pulmonary large cell neuroendocrine carcinoma and its association with the immune-related tumour microenvironment. Br. J. Cancer 124, 925\u2013932 (2021).","journal-title":"Br. J. Cancer"},{"key":"377_CR98","doi-asserted-by":"publisher","first-page":"14264","DOI":"10.21873\/anticanres.14264","volume":"40","author":"R Tanaka","year":"2020","unstructured":"Tanaka, R. et al. Preoperative neutrophil-to-lymphocyte ratio predicts tumor-infiltrating CD8+ T cells in biliary tract cancer. Anticancer Res. 40, 14264 (2020).","journal-title":"Anticancer Res."},{"key":"377_CR99","doi-asserted-by":"publisher","first-page":"e143","DOI":"10.1016\/S1470-2045(17)30074-8","volume":"18","author":"L Seymour","year":"2017","unstructured":"Seymour, L. et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 18, e143\u2013e152 (2017).","journal-title":"Lancet Oncol."},{"key":"377_CR100","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/ng.3205","volume":"47","author":"P Jiang","year":"2015","unstructured":"Jiang, P. & Liu, X. S. Big data mining yields novel insights on cancer. Nat. Genet. 47, 103\u2013104 (2015).","journal-title":"Nat. Genet."},{"key":"377_CR101","doi-asserted-by":"publisher","first-page":"4781","DOI":"10.3390\/ijms20194781","volume":"20","author":"M Olivier","year":"2019","unstructured":"Olivier, M., Asmis, R., Hawkins, G. A., Howard, T. D. & Cox, L. A. The need for multi-omics biomarker signatures in precision medicine. Int. J. Mol. Sci. 20, 4781 (2019).","journal-title":"Int. J. Mol. Sci."},{"key":"377_CR102","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1074\/mcp.O116.059253","volume":"15","author":"KH Yu","year":"2016","unstructured":"Yu, K. H. & Snyder, M. Omics profiling in precision oncology. Mol. Cell. Proteom. 15, 2525\u20132536 (2016).","journal-title":"Mol. Cell. Proteom."},{"key":"377_CR103","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-017-0029-7","volume":"1","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Chien, J., Yong, J. & Kuang, R. Network-based machine learning and graph theory algorithms for precision oncology. npj Precis. Oncol. 1, 25 (2017).","journal-title":"npj Precis. Oncol."},{"key":"377_CR104","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2018.06.002","volume":"90","author":"AN Richter","year":"2018","unstructured":"Richter, A. N. & Khoshgoftaar, T. M. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif. Intell. Med. 90, 1\u201314 (2018).","journal-title":"Artif. Intell. Med."},{"key":"377_CR105","doi-asserted-by":"publisher","first-page":"e14095","DOI":"10.1200\/JCO.2020.38.15_suppl.e14095","volume":"38","author":"V Cuplov","year":"2020","unstructured":"Cuplov, V., Sicard, G., Barbolosi, D., Ciccolini, J. & Barlesi, F. Harnessing tumor immunity with chemotherapy: mathematical modeling for decision-making in combinatorial regimen with immune-oncology drugs. J. Clin. Oncol. 38, e14095\u2013e14095 (2020).","journal-title":"J. Clin. Oncol."},{"key":"377_CR106","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","volume":"25","author":"D Ardila","year":"2019","unstructured":"Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, 954\u2013961 (2019).","journal-title":"Nat. Med."},{"key":"377_CR107","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017).","journal-title":"Nature"},{"key":"377_CR108","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019).","journal-title":"Nat. Med."},{"key":"377_CR109","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1038\/d41586-020-01128-8","volume":"580","author":"E Landhuis","year":"2020","unstructured":"Landhuis, E. Deep learning takes on tumours. Nature 580, 551\u2013553 (2020).","journal-title":"Nature"},{"key":"377_CR110","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-019-0078-1","volume":"3","author":"F Azuaje","year":"2019","unstructured":"Azuaje, F. Artificial intelligence for precision oncology: beyond patient stratification. npj Precis. Oncol. 3, 6 (2019).","journal-title":"npj Precis. Oncol."},{"key":"377_CR111","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1200\/CCI.20.00049","volume":"4","author":"M Nagy","year":"2020","unstructured":"Nagy, M., Radakovich, N. & Nazha, A. Machine learning in oncology: what should clinicians know? JCO Clin. Cancer Inform. 4, 799\u2013810 (2020).","journal-title":"JCO Clin. Cancer Inform."},{"key":"377_CR112","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206\u2013215 (2019).","journal-title":"Nat. Mach. Intell."},{"key":"377_CR113","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","volume":"3","author":"GE Karniadakis","year":"2021","unstructured":"Karniadakis, G. E. et al. Physics-informed machine learning. Nat. Rev. Phys. 3, 422\u2013440 (2021).","journal-title":"Nat. Rev. Phys."},{"key":"377_CR114","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019).","journal-title":"J. Comput. Phys."},{"key":"377_CR115","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.coisb.2021.03.005","volume":"25","author":"F-G Wieland","year":"2021","unstructured":"Wieland, F.-G., Hauber, A. L., Rosenblatt, M., T\u00f6nsing, C. & Timmer, J. On structural and practical identifiability. Curr. Opin. Syst. Biol. 25, 60\u201369 (2021).","journal-title":"Curr. Opin. Syst. Biol."},{"key":"377_CR116","doi-asserted-by":"crossref","first-page":"e1019","DOI":"10.1002\/cso2.1019","volume":"1","author":"K Okuneye","year":"2021","unstructured":"Okuneye, K. et al. A validated mathematical model of FGFR3-mediated tumor growth reveals pathways to harness the benefits of combination targeted therapy and immunotherapy in bladder cancer. Comput Syst. Oncol. 1, e1019 (2021).","journal-title":"Comput Syst. Oncol."},{"key":"377_CR117","doi-asserted-by":"publisher","first-page":"101198","DOI":"10.1016\/j.jocs.2020.101198","volume":"46","author":"S Bekisz","year":"2020","unstructured":"Bekisz, S. & Geris, L. Cancer modeling: from mechanistic to data-driven approaches, and from fundamental insights to clinical applications. J. Comput. Sci. 46, 101198 (2020).","journal-title":"J. Comput. Sci."},{"key":"377_CR118","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1038\/s41568-020-0258-x","volume":"20","author":"MA Clarke","year":"2020","unstructured":"Clarke, M. A. & Fisher, J. Executable cancer models: successes and challenges. Nat. Rev. Cancer 20, 343\u2013354 (2020).","journal-title":"Nat. Rev. Cancer"},{"key":"377_CR119","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3389\/fimmu.2020.00027","volume":"11","author":"V Roudko","year":"2020","unstructured":"Roudko, V., Greenbaum, B. & Bhardwaj, N. Computational prediction and validation of tumor-associated neoantigens. Front. Immunol. 11, 27 (2020).","journal-title":"Front. Immunol."},{"key":"377_CR120","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1038\/s41577-019-0271-z","volume":"20","author":"H Garner","year":"2020","unstructured":"Garner, H. & de Visser, K. E. Immune crosstalk in cancer progression and metastatic spread: a complex conversation. Nat. Rev. Immunol. 20, 483\u2013497 (2020).","journal-title":"Nat. Rev. Immunol."},{"key":"377_CR121","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/s41577-019-0269-6","volume":"20","author":"DJ Irvine","year":"2020","unstructured":"Irvine, D. J. & Dane, E. L. Enhancing cancer immunotherapy with nanomedicine. Nat. Rev. Immunol. 20, 321\u2013334 (2020).","journal-title":"Nat. Rev. Immunol."},{"key":"377_CR122","doi-asserted-by":"publisher","first-page":"e005107","DOI":"10.1136\/jitc-2022-005107","volume":"10","author":"RA Bekker","year":"2022","unstructured":"Bekker, R. A. et al. Rethinking the immunotherapy numbers game. J. Immunother. Cancer 10, e005107 (2022).","journal-title":"J. Immunother. Cancer"},{"key":"377_CR123","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1142\/S0218339006001702","volume":"14","author":"U Fory\u015a","year":"2006","unstructured":"Fory\u015a, U., Waniewski, J. & Zhivkov, P. Anti-tumor immunity and tumor anti-immunity in a mathematical model of tumor immunotherapy. J. Biol. Syst. 14, 13\u201330 (2006).","journal-title":"J. Biol. Syst."}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00377-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00377-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00377-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T08:00:18Z","timestamp":1701590418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00377-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,19]]},"references-count":123,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["377"],"URL":"https:\/\/doi.org\/10.1038\/s43588-022-00377-z","relation":{},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,19]]},"assertion":[{"value":"19 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}