Kronik2010 - Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models

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Model Identifier
MODEL2001130003
Short description
Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models

Natalie Kronik1¤, Yuri Kogan1, Moran Elishmereni1, Karin Halevi-Tobias1, Stanimir Vuk-Pavlovic ́2.,Zvia Agur1*.1Institute for Medical BioMathematics, Bene Ataroth, Israel,2College of Medicine, Mayo Clinic, Rochester, Minnesota, United States of America

Abstract
Background:Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients.We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simplemathematical models.Methodology/Principal Findings:We developed a general mathematical model encompassing the basic interactions of avaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cellvaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes inprostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were dividedinto each patient’s training set and his validation set. The training set, used for model personalization, contained thepatient’s initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized modelswere simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set.The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients(the coefficient of determination between the predicted and observed PSA values wasR2= 0.972). The model could notaccount for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validatedpersonalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccinationregimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Conclusions/Significance:Using a few initial measurements, we constructed robust patient-specific models of PCaimmunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value andfeasibility of individualized model-suggested immunotherapy protocols.
Format
SBML (L2V4)
Related Publication
  • Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics.
  • Kronik N, Kogan Y, Vainstein V, Agur Z
  • Cancer immunology, immunotherapy : CII , 3/ 2008 , Volume 57 , Issue 3 , pages: 425-439 , PubMed ID: 17823798
  • Institute for Medical BioMathematics (IMBM), 10 Hate'ena St., PO Box 282, Bene Ataroth 60991, Israel. natalie@imbm.org
  • Glioblastoma (GBM), a highly aggressive (WHO grade IV) primary brain tumor, is refractory to traditional treatments, such as surgery, radiation or chemotherapy. This study aims at aiding in the design of more efficacious GBM therapies. We constructed a mathematical model for glioma and the immune system interactions, that may ensue upon direct intra-tumoral administration of ex vivo activated alloreactive cytotoxic-T-lymphocytes (aCTL). Our model encompasses considerations of the interactive dynamics of aCTL, tumor cells, major histocompatibility complex (MHC) class I and MHC class II molecules, as well as cytokines, such as TGF-beta and IFN-gamma, which dampen or increase the pro-inflammatory environment, respectively. Computer simulations were used for model verification and for retrieving putative treatment scenarios. The mathematical model successfully retrieved clinical trial results of efficacious aCTL immunotherapy for recurrent anaplastic oligodendroglioma and anaplastic astrocytoma (WHO grade III). It predicted that cellular adoptive immunotherapy failed in GBM because the administered dose was 20-fold lower than required for therapeutic efficacy. Model analysis suggests that GBM may be eradicated by new dose-intensive strategies, e.g., 3 x 10(8) aCTL every 4 days for small tumor burden, or 2 x 10(9) aCTL, infused every 5 days for larger tumor burden. Further analysis pinpoints crucial bio-markers relating to tumor growth rate, tumor size, and tumor sensitivity to the immune system, whose estimation enables regimen personalization. We propose that adoptive cellular immunotherapy was prematurely abandoned. It may prove efficacious for GBM, if dose intensity is augmented, as prescribed by the mathematical model. Re-initiation of clinical trials, using calculated individualized regimens for grade III-IV malignant glioma, is suggested.
Contributors
Mohammad Umer Sharif Shohan

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hasTaxon
Taxonomy Homo sapiens
hasProperty
Mathematical Modelling Ontology Ordinary differential equation model
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Non-curated


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Kronik2010.xml SBML L2V4 Kronik2010 - Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models 42.61 KB Preview | Download

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Kronik2010.cps COPASI version 4.24 (Build 197) Kronik2010 - Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models 87.56 KB Preview | Download

  • Model originally submitted by : Mohammad Umer Sharif Shohan
  • Submitted: 13-Jan-2020 17:05:44
  • Last Modified: 13-Jan-2020 17:05:44
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  • Version: 1 public model Download this version
    • Submitted on: 13-Jan-2020 17:05:44
    • Submitted by: Mohammad Umer Sharif Shohan
    • With comment: Import of Kronik2010 - Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models
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