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Kronik N.,Institute for Medical BioMathematics | Kronik N.,Holon Institute of Technology | Kogan Y.,Institute for Medical BioMathematics | Elishmereni M.,Institute for Medical BioMathematics | And 3 more authors.
PLoS ONE | Year: 2010

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. © 2010 Kronik et al.


Kronik N.,Holon Institute of Technology | Kogan Y.,Institute for Medical BioMathematics | Schlegel P.G.,University of Würzburg | Wolfl M.,University of Würzburg
Journal of Immunotherapy | Year: 2012

T-cell mediated immunotherapy for malignant diseases has become an effective treatment option, especially in malignant melanoma. Recent advances have enabled the transfer of high T-cell numbers with high functionality. However, with more T cells becoming technically available for transfer, questions about dose, treatment schedule, and safety become most relevant. Mathematical oncology can simulate tumor characteristics in silico and predict the tumor response to novel therapeutics. Using similar methods to classical pharmacokinetics/pharmacodynamics-type models, mathematical oncology translates the findings into a multiparameter model system and simulates T-cell therapy for malignant diseases. The tumor and immune system dynamics model can provide minimal requirements (in terms of T-cell dose and T-cell functionality) depending on the tumor characteristics (growth rate, residual tumor size) for a clinical study, and help select the best treatment schedule (repetitive doses, minimally required duration, etc.). Here, we present a new mathematical model developed for modeling cellular immunotherapy for melanoma. Computer simulations based on the new model offer an explanation for the observed finding from clinical trials that the patients with the smallest tumor load respond better. We simulate different parameters critical for improvement of cellular therapy for patients with high tumor load of fast-growing tumors. We show that tumor growth rate and tumor load are crucial in predicting the outcome of T-cell therapy. Rather than intuitively extrapolating from experimental data, we demonstrate how mathematical oncology can assist in rational planning of clinical trials. Copyright © 2012 by Lippincott Williams & Wilkins.


Agur Z.,Institute for Medical BioMathematics | Kogan Y.,Institute for Medical BioMathematics | Levi L.,Institute for Medical BioMathematics | Harrison H.,University of Manchester | And 3 more authors.
Biology Direct | Year: 2010

Background: The balance between self-renewal and differentiation of stem cells is expected to be tightly controlled in order to maintain tissue homeostasis throughout life, also in the face of environmental hazards. Theory, predicting that homeostasis is maintained by a negative feedback on stem cell proliferation, implies a Quorum Sensing mechanism in higher vertebrates.Results: Application of this theory to a cellular automata model of stem cell development in disrupted environments shows a sharply dichotomous growth dynamics: maturation within 50-400 cell cycles, or immortalization. This dichotomy is mainly driven by intercellular communication, low intensity of which causes perpetual proliferation. Another driving force is the cells' kinetic parameters. Reduced tissue life span of differentiated cells results in uncontrolled proliferation. Model's analysis, showing that under the Quorum Sensing control, stem cell fraction within a steady state population is fixed, is corroborated by experiments in breast carcinoma cells. Experimental results show that the plating densities of CD44+ cells and of CD44+/24lo/ESA+ cells do not affect stem cell fraction near confluence.Conclusions: This study suggests that stem cell immortalization may be triggered by reduced intercellular communication, rather than exclusively result from somatic evolution, and implies that stem cell proliferation can be attenuated by signal manipulation, or enhanced by cytotoxics targeted to differentiated cells. In vivo verification and identification of the Quorum Sensing mediating molecules will pave the way to a higher level control of stem cell proliferation in cancer and in tissue engineering. © 2010 Agur et al; licensee BioMed Central Ltd.


Jager E.,Institute for Medical BioMathematics | van der Velden V.H.J.,Rotterdam University | te Marvelde J.G.,Rotterdam University | Walter R.B.,Fred Hutchinson Cancer Research Center | And 4 more authors.
PLoS ONE | Year: 2011

Gemtuzumab ozogamicin (GO) is a chemotherapy-conjugated anti-CD33 monoclonal antibody effective in some patients with acute myeloid leukemia (AML). The optimal treatment schedule and optimal timing of GO administration relative to other agents remains unknown. Conventional pharmacokinetic analysis has been of limited insight for the schedule optimization. We developed a mechanism-based mathematical model and employed it to analyze the time-course of free and GO-bound CD33 molecules on the lekemic blasts in individual AML patients treated with GO. We calculated expected intravascular drug exposure (I-AUC) as a surrogate marker for the response to the drug. A high CD33 production rate and low drug efflux were the most important determinants of high I-AUC, characterizing patients with favorable pharmacokinetic profile and, hence, improved response. I-AUC was insensitive to other studied parameters within biologically relevant ranges, including internalization rate and dissociation constant. Our computations suggested that even moderate blast burden reduction prior to drug administration enables lowering of GO doses without significantly compromising intracellular drug exposure. These findings indicate that GO may optimally be used after cyto-reductive chemotherapy, rather than before, or concomitantly with it, and that GO efficacy can be maintained by dose reduction to 6 mg/m 2 and a dosing interval of 7 days. Model predictions are validated by comparison with the results of EORTC-GIMEMA AML19 clinical trial, where two different GO schedules were administered. We suggest that incorporation of our results in clinical practice can serve identification of the subpopulation of elderly patients who can benefit most of the GO treatment and enable return of the currently suspended drug to clinic. © 2011 Jager et al.


Agur Z.,Institute for Medical BioMathematics | Kirnasovsky O.U.,Institute for Medical BioMathematics | Vasserman G.,Institute for Medical BioMathematics | Tencer-Hershkowicz L.,Institute for Medical BioMathematics | And 4 more authors.
PLoS ONE | Year: 2011

Background: Modulation of cellular signaling pathways can change the replication/differentiation balance in cancer stem cells (CSCs), thus affecting tumor growth and recurrence. Analysis of a simple, experimentally verified, mathematical model suggests that this balance is maintained by quorum sensing (QS). Methodology/Principal Findings: To explore the mechanism by which putative QS cellular signals in mammary stem cells (SCs) may regulate SC fate decisions, we developed a multi-scale mathematical model, integrating extra-cellular and intra-cellular signal transduction within the mammary tissue dynamics. Preliminary model analysis of the single cell dynamics indicated that Dickkopf1 (Dkk1), a protein known to negatively regulate the Wnt pathway, can serve as anti-proliferation and pro-maturation signal to the cell. Simulations of the multi-scale tissue model suggested that Dkk1 may be a QS factor, regulating SC density on the level of the whole tissue: relatively low levels of exogenously applied Dkk1 have little effect on SC numbers, whereas high levels drive SCs into differentiation. To verify these model predictions, we treated the MCF-7 cell line and primary breast cancer (BC) cells from 3 patient samples with different concentrations and dosing regimens of Dkk1, and evaluated subsequent formation of mammospheres (MS) and the mammary SC marker CD44 +CD24 lo. As predicted by the model, low concentrations of Dkk1 had no effect on primary BC cells, or even increased MS formation among MCF-7 cells, whereas high Dkk1 concentrations decreased MS formation among both primary BC cells and MCF-7 cells. Conclusions/Significance: Our study suggests that Dkk1 treatment may be more robust than other methods for eliminating CSCs, as it challenges a general cellular homeostasis mechanism, namely, fate decision by QS. The study also suggests that low dose Dkk1 administration may be counterproductive; we showed experimentally that in some cases it can stimulate CSC proliferation, although this needs validating in vivo. © 2011 Agur et al.


Vainstein V.,Institute for Medical Biomathematics | Kirnasovsky O.U.,Institute for Medical Biomathematics | Kogan Y.,Institute for Medical Biomathematics | Agur Z.,Institute for Medical Biomathematics
Journal of Theoretical Biology | Year: 2012

The cancer stem cell (CSC) hypothesis states that only a small fraction of a malignant cell population is responsible for tumor growth and relapse. Understanding the relationships between CSC dynamics and cancer progression may contribute to improvements in cancer treatment. Analysis of a simple discrete mathematical model has suggested that homeostasis in developing tissues is governed by a "quorum sensing" control mechanism, in which stem cells differentiate or proliferate according to feedback they receive from neighboring cell populations. Further analysis of the same model has indicated that excessive stem cell proliferation leading to malignant transformation mainly results from altered sensitivity to such micro-environmental signals. Our aim in this work is to expand the analysis to the dynamics of established populations of cancer cells and to examine possible therapeutic avenues for eliminating CSCs. The proposed model considers two populations of cells: CSCs, which can divide indefinitely, and differentiated cancer cells, which do not divide and have a limited lifespan. We assume that total cell density has negative feedback on CSC proliferation and that high CSC density activates CSC differentiation. We show that neither stimulation of CSC differentiation nor inhibition of CSC proliferation alone is sufficient for complete CSC elimination and cancer cure, since each of these two therapies affects a different subpopulation of CSCs. However, a combination of these two strategies can substantially reduce the population sizes and densities of all types of cancer cells. Therefore, we propose that in clinical trials, CSC differentiation therapy should only be examined in combination with chemotherapy. Our conclusions are corroborated by clinical experience with differentiating agents in acute promyelocytic leukemia and neuroblastoma. © 2011 Elsevier Ltd.


Forys U.,University of Warsaw | Bodnar M.,University of Warsaw | Kogan Y.,Institute for Medical BioMathematics
Journal of Mathematical Biology | Year: 2016

In the case of some specific cancers, immunotherapy is one of the possible treatments that can be considered. Our study is based on a mathematical model of patient-specific immunotherapy proposed in Kronik et al. (PLoS One 5(12):e15,482, 2010). This model was validated for clinical trials presented in Michael et al. (Clin Cancer Res 11(12):4469–4478, 2005). It consists of seven ordinary differential equations and its asymptotic dynamics can be described by some t-periodic one-dimensional dynamical system. In this paper we propose a generalised version of this t-periodic system and study the dynamics of the proposed model. We show that there are three possible types of the model behaviour: the solution either converges to zero, or diverges to infinity, or it is periodic. Moreover, the periodic solution is unique, and it divides the phase space into two sub-regions. The general results are applied to the PC specific case, which allow to derive conditions guaranteeing successful as well as unsuccessful treatment. The results indicate that a single vaccination is not sufficient to cure the cancer. © 2016 The Author(s)


Kogan Y.,Institute for Medical BioMathematics | Halevi-Tobias K.,Institute for Medical BioMathematics | Elishmereni M.,Institute for Medical BioMathematics | Vuk-Pavlovic S.,Rochester College | Agur Z.,Institute for Medical BioMathematics
Cancer Research | Year: 2012

Although therapeutic vaccination often induces markers of tumor-specific immunity, therapeutic responses remain rare. An improved understanding of patient-specific dynamic interactions of immunity and tumor progression, combined with personalized application of immune therapeutics would increase the efficacy of immunotherapy. Here, we developed a method to predict and enhance the individual response to immunotherapy by using personalized mathematical models, constructed in the early phase of treatment. Our approach includes an iterative real-time in-treatment evaluation of patient-specific parameters from the accruing clinical data, construction of personalized models and their validation, model-based simulation of subsequent response to ongoing therapy, and suggestion of potentially more effective patient-specific modified treatment. Using a mathematical model of prostate cancer immunotherapy, we applied our model to data obtained in a clinical investigation of an allogeneic whole-cell therapeutic prostate cancer vaccine. Personalized models for the patients who responded to treatment were derived and validated by data collected before treatment and during its early phase. Simulations, based on personalized models, suggested that an increase in vaccine dose and administration frequency would stabilize the disease in most patients. Together, our findings suggest that application of our method could facilitate development of a new paradigm for studies of in-treatment personalization of the immune agent administration regimens (P-trials), with treatment modifications restricted to an approved range, resulting in more efficacious immunotherapies. ©2012 AACR.


Kogan Y.,Institute for Medical BioMathematics | Halevi-Tobias K.E.,Institute for Medical BioMathematics | Hochman G.,Institute for Medical BioMathematics | Baczmanska A.K.,Vrije Universiteit Brussel | And 2 more authors.
Biochemical Journal | Year: 2012

The Wnt signalling pathway controls cell proliferation and differentiation, and its deregulation is implicated in different diseases including cancer. Learning how to manipulate this pathway could substantially contribute to the development of therapies. We developed a mathematical model describing the initial sequence of events in the Wnt pathway, from ligand binding to β-catenin accumulation, and the effects of inhibitors, such as sFRPs (secreted Frizzled-related proteins) and Dkk (Dickkopf). Model parameters were retrieved from experimental data reported previously. The model was retrospectively validated by accurately predicting the effects of Wnt3a and sFRP1 on β-catenin levels in two independent published experiments (R 2between 0.63 and 0.91). Prospective validation was obtained by testing the model's accuracy in predicting the effect of Dkk1 on Wnt-induced β-catenin accumulation (R2≈0.94).Model simulations under different combinations of sFRP1 and Dkk1 predicted a clear synergistic effect of these two inhibitors on β-catenin accumulation, which may point towards a new treatment avenue. Our model allows precise calculation of the effect of inhibitors applied alone or in combination, and provides a flexible framework for identifying potential targets for intervention in the Wnt signalling pathway. © The Authors Journal compilation © 2012 Biochemical Society.


Agur Z.,Institute for Medical BioMathematics | Agur Z.,Optimata | Elishmereni M.,Institute for Medical BioMathematics | Elishmereni M.,Optimata | And 2 more authors.
Wiley Interdisciplinary Reviews: Systems Biology and Medicine | Year: 2014

Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article. © 2014 Wiley Periodicals, Inc.

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