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Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models

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  • Natalie Kronik
  • Yuri Kogan
  • Moran Elishmereni
  • Karin Halevi-Tobias
  • Stanimir Vuk-Pavlović
  • Zvia Agur

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 simple mathematical models. Methodology/Principal Findings: We developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient's training set and his validation set. The training set, used for model personalization, contained the patient's initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were 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 was R2 = 0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. 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 PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols.

Suggested Citation

  • Natalie Kronik & Yuri Kogan & Moran Elishmereni & Karin Halevi-Tobias & Stanimir Vuk-Pavlović & Zvia Agur, 2010. "Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-8, December.
  • Handle: RePEc:plo:pone00:0015482
    DOI: 10.1371/journal.pone.0015482
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    References listed on IDEAS

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    1. Zvia Agur & Refael Hassin & Sigal Levy, 2006. "Optimizing Chemotherapy Scheduling Using Local Search Heuristics," Operations Research, INFORMS, vol. 54(5), pages 829-846, October.
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    Cited by:

    1. Khajanchi, Subhas & Nieto, Juan J., 2019. "Mathematical modeling of tumor-immune competitive system, considering the role of time delay," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 180-205.
    2. Yoshito Hirata & Kai Morino & Koichiro Akakura & Celestia S Higano & Nicholas Bruchovsky & Teresa Gambol & Susan Hall & Gouhei Tanaka & Kazuyuki Aihara, 2015. "Intermittent Androgen Suppression: Estimating Parameters for Individual Patients Based on Initial PSA Data in Response to Androgen Deprivation Therapy," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.

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