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However, the overall response rate does not exceed 50% and, considering the high costs and low life expectancy of nonresponding patients, there is a need to select potential responders before therapy. Our aim was to develop a new personalization algorithm which could be beneficial in the clinical setting for predicting time to disease progression under pembrolizumab treatment.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We developed a simple mathematical model for the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. We implemented the model in an algorithm which, in conjunction with clinical pretreatment data, enables prediction of the personal patient response to the drug. To develop the algorithm, we retrospectively collected clinical data of 54 patients with advanced melanoma, who had been treated by pembrolizumab, and correlated personal pretreatment measurements to the mathematical model parameters. Using the algorithm together with the longitudinal tumor burden of each patient, we identified the personal mathematical models, and simulated them to predict the patient\u2019s time to progression. We validated the prediction capacity of the algorithm\u00a0by the Leave-One-Out cross-validation methodology.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Among the analyzed clinical parameters, the baseline tumor load, the Breslow tumor thickness, and the status of nodular melanoma were significantly correlated with the activation rate of CD8+ T cells\u00a0and the net tumor growth rate. Using the measurements of these correlates to personalize the mathematical model, we predicted the time to progression of individual patients (Cohen\u2019s \u03ba\u2009=\u20090.489). Comparison of the predicted and the clinical time to progression in patients progressing during the follow-up period showed moderate accuracy (R<jats:sup>2<\/jats:sup>\u2009=\u20090.505).<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our results show for the first time that a relatively simple mathematical mechanistic model, implemented in a personalization algorithm, can be personalized by clinical data, evaluated before immunotherapy onset. The algorithm, currently yielding moderately accurate predictions of individual patients\u2019 response to pembrolizumab, can be improved by training on a larger number of patients. Algorithm validation by an independent clinical dataset will enable its use as a tool for treatment personalization.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12967-019-2081-2","type":"journal-article","created":{"date-parts":[[2019,10,7]],"date-time":"2019-10-07T13:19:37Z","timestamp":1570454377000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm"],"prefix":"10.1186","volume":"17","author":[{"given":"Neta","family":"Tsur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuri","family":"Kogan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evgenia","family":"Avizov-Khodak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D\u00e9sir\u00e9e","family":"Vaeth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nils","family":"Vogler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jochen","family":"Utikal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michal","family":"Lotem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zvia","family":"Agur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,7]]},"reference":[{"key":"2081_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21442","volume":"68","author":"RL Siegel","year":"2018","unstructured":"Siegel RL, Miller KD, Jemal A. 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It was approved and registered as a retrospective clinical trial (ClinicalTrials gov identifier: NCT02581228), according to the requirements of the Helsinki Committee at HMC (0403-15-HMO), and upon signing a secrecy declaration prior to data retrieval at UMM.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"ZA holds 15% shares in Optimata. Other authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"338"}}