Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models

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

Research output: Contribution to journalArticle

62 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere15482
JournalPLoS One
Volume5
Issue number12
DOIs
StatePublished - 2010

Fingerprint

prostate-specific antigen
immunotherapy
prostatic neoplasms
Prostate-Specific Antigen
Immunotherapy
Prostatic Neoplasms
Theoretical Models
mathematical models
Mathematical models
Vaccination
vaccination
Tumor Burden
Tumors
clinical trials
Vaccines
Clinical Trials
vaccines
therapeutics
neoplasms
Immune system

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Kronik, N., Kogan, Y., Elishmereni, M., Halevi-Tobias, K., Vuk-Pavlović, S., & Agur, Z. (2010). Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. PLoS One, 5(12), [e15482]. https://doi.org/10.1371/journal.pone.0015482

Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. / Kronik, Natalie; Kogan, Yuri; Elishmereni, Moran; Halevi-Tobias, Karin; Vuk-Pavlović, Stanimir; Agur, Zvia.

In: PLoS One, Vol. 5, No. 12, e15482, 2010.

Research output: Contribution to journalArticle

Kronik, N, Kogan, Y, Elishmereni, M, Halevi-Tobias, K, Vuk-Pavlović, S & Agur, Z 2010, 'Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models', PLoS One, vol. 5, no. 12, e15482. https://doi.org/10.1371/journal.pone.0015482
Kronik N, Kogan Y, Elishmereni M, Halevi-Tobias K, Vuk-Pavlović S, Agur Z. Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. PLoS One. 2010;5(12). e15482. https://doi.org/10.1371/journal.pone.0015482
Kronik, Natalie ; Kogan, Yuri ; Elishmereni, Moran ; Halevi-Tobias, Karin ; Vuk-Pavlović, Stanimir ; Agur, Zvia. / Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. In: PLoS One. 2010 ; Vol. 5, No. 12.
@article{0934be2f51f744a79b7977a4bc26ecec,
title = "Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models",
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.",
author = "Natalie Kronik and Yuri Kogan and Moran Elishmereni and Karin Halevi-Tobias and Stanimir Vuk-Pavlović and Zvia Agur",
year = "2010",
doi = "10.1371/journal.pone.0015482",
language = "English (US)",
volume = "5",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "12",

}

TY - JOUR

T1 - Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models

AU - Kronik, Natalie

AU - Kogan, Yuri

AU - Elishmereni, Moran

AU - Halevi-Tobias, Karin

AU - Vuk-Pavlović, Stanimir

AU - Agur, Zvia

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78650111045&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650111045&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0015482

DO - 10.1371/journal.pone.0015482

M3 - Article

C2 - 21151630

AN - SCOPUS:78650111045

VL - 5

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 12

M1 - e15482

ER -