Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet

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dc.citation.articleNumbere1008234en_US
dc.citation.issueNumber10en_US
dc.citation.journalTitlePLoS Computational Biologyen_US
dc.citation.volumeNumber16en_US
dc.contributor.authorKozłowska, Emiliaen_US
dc.contributor.authorSuwiński, Rafałen_US
dc.contributor.authorGiglok, Monikaen_US
dc.contributor.authorŚwierniak, Andrzejen_US
dc.contributor.authorKimmel, Mareken_US
dc.contributor.orgBioengineeringen_US
dc.contributor.orgStatisticsen_US
dc.date.accessioned2020-11-10T21:22:24Zen_US
dc.date.available2020-11-10T21:22:24Zen_US
dc.date.issued2020en_US
dc.description.abstractWe developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004-2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.en_US
dc.identifier.citationKozłowska, Emilia, Suwiński, Rafał, Giglok, Monika, et al.. "Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet." <i>PLoS Computational Biology,</i> 16, no. 10 (2020) Public Library of Science: https://doi.org/10.1371/journal.pcbi.1008234.en_US
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1008234en_US
dc.identifier.urihttps://hdl.handle.net/1911/109542en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleMathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doubleten_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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