Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer (mCRPC): Updated analysis of the adaptive abiraterone (abi) study (NCT02415621).

5041 Background: To achieve better prostate cancer control and to delay the emergency of treatment resistance, we developed an evolutionary game theory model using Lotka-Volterra equations with three competing prostate cancer "species": T+, Tp, and T-. T+ prostate cancer cells depend on exogenous androgen; Tp cells express CYP17A1, produce and depend on androgen; and T- cells are androgen-independent and abi-resistant. We applied this model to guide the on and off treatment cycles with abi for mCRPC. At the first interim analysis with 11 patients, this approach was shown to prolong the time to cancer progression with less than 50% drug usage compared to the conventional continuous Abi ( Nat Commun. 2017). Here we present the updated data of this phase 2 study. Methods: Men with asymptomatic or minimal symptomatic mCRPC were enrolled after they achieved > 50% PSA reduction with abi as a frontline therapy for mCRPC. The primary objective is feasibility and is measured by the percentage of abi responsive men who remain to be responsive to abi (defined as > 50% decline of the pre Abi PSA) after completing 2 adaptive treatment cycles. The secondary objective is to assess the clinical benefits by comparing the radiographic progression free survival (rPFS) in men undergoing adaptive Abi therapy to the historical AA 302 trial. Results: At the data cut off in Jan 2019, the study has completed enrollment for the non-African American cohort. 15 enrolled men had > 11 months of follow up. All 15 men were off Abi for at least 3 months before abi was restarted for PSA progression at cycle 1. Seven out of the 15 men had completed at least 2 adaptive therapy cycles. Four of the rest 8 men remained on study and have not reached cycle 2. Six men were off study due to radiographic progression at month 11, 20.4, 30, 30.5, 38 and 53 from their first dose of Abi. Compare to the 16.2 months median rPFS in the AA 302 trial, the median rPFS of the 15 men would be no less than 30 months (p = 0.0068, Fisher’s exact test). Their average usage of Abi was 49% of the continuous Abi. Conclusions: Adaptive Abi therapy is feasible in men who responded to Abi as a frontline therapy for mCRPC. The updated data are consistent with our initial finding that our adaptive therapy approach can prolong the time to cancer progression with less than 50% drug usage compared to the conventional continuous Abi. Clinical trial information: NCT02415621.

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Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer

Nature Communications ◽

10.1038/s41467-017-01968-5 ◽

2017 ◽

Vol 8 (1) ◽

Cited By ~ 160

Author(s):

Jingsong Zhang ◽

Jessica J. Cunningham ◽

Joel S. Brown ◽

Robert A. Gatenby

Keyword(s):

Prostate Cancer ◽

Treatment Failure ◽

Evolutionary Dynamics ◽

Evolutionary Game ◽

Theory Model ◽

Patient Specific ◽

Volterra Equations ◽

Castrate Resistant Prostate Cancer ◽

Castrate Resistant ◽

Androgen Independent

AbstractAbiraterone treats metastatic castrate-resistant prostate cancer by inhibiting CYP17A, an enzyme for testosterone auto-production. With standard dosing, evolution of resistance with treatment failure (radiographic progression) occurs at a median of ~16.5 months. We hypothesize time to progression (TTP) could be increased by integrating evolutionary dynamics into therapy. We developed an evolutionary game theory model using Lotka–Volterra equations with three competing cancer “species”: androgen dependent, androgen producing, and androgen independent. Simulations with standard abiraterone dosing demonstrate strong selection for androgen-independent cells and rapid treatment failure. Adaptive therapy, using patient-specific tumor dynamics to inform on/off treatment cycles, suppresses proliferation of androgen-independent cells and lowers cumulative drug dose. In a pilot clinical trial, 10 of 11 patients maintained stable oscillations of tumor burdens; median TTP is at least 27 months with reduced cumulative drug use of 47% of standard dosing. The outcomes show significant improvement over published studies and a contemporaneous population.

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Darwinian Approaches for Cancer Treatment: Benefits of Mathematical Modeling

Cancers ◽

10.3390/cancers13174448 ◽

2021 ◽

Vol 13 (17) ◽

pp. 4448

Author(s):

Sophia Belkhir ◽

Frederic Thomas ◽

Benjamin Roche

Keyword(s):

Mathematical Modeling ◽

Evolutionary Dynamics ◽

Treatment Strategies ◽

Clinical Trial Design ◽

Volterra Equations ◽

Treatment Resistant ◽

Cancer Treatments ◽

Treatment Benefits ◽

Adaptive Therapy ◽

Resistant Cells

One of the major problems of traditional anti-cancer treatments is that they lead to the emergence of treatment-resistant cells, which results in treatment failure. To avoid or delay this phenomenon, it is relevant to take into account the eco-evolutionary dynamics of tumors. Designing evolution-based treatment strategies may help overcoming the problem of drug resistance. In particular, a promising candidate is adaptive therapy, a containment strategy which adjusts treatment cycles to the evolution of the tumors in order to keep the population of treatment-resistant cells under control. Mathematical modeling is a crucial tool to understand the dynamics of cancer in response to treatments, and to make predictions about the outcomes of these treatments. In this review, we highlight the benefits of in silico modeling to design adaptive therapy strategies, and to assess whether they could effectively improve treatment outcomes. Specifically, we review how two main types of models (i.e., mathematical models based on Lotka–Volterra equations and agent-based models) have been used to model tumor dynamics in response to adaptive therapy. We give examples of the advances they permitted in the field of adaptive therapy and discuss about how these models can be integrated in experimental approaches and clinical trial design.

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The contribution of evolutionary game theory to understanding and treating cancer

10.1101/2020.12.02.20241703 ◽

2020 ◽

Author(s):

Benjamin Wölfl ◽

Hedy te Rietmole ◽

Monica Salvioli ◽

Frank Thuijsman ◽

Joel S. Brown ◽

...

Keyword(s):

Game Theory ◽

Cancer Progression ◽

Evolutionary Game Theory ◽

Cancer Biology ◽

Evolutionary Dynamics ◽

Evolutionary Game ◽

Great Promise ◽

Full Potential ◽

Theory Approach ◽

Game Theoretic

AbstractEvolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather, inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. We discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with an evolutionary game theory approach has medically useful implications that can inform and create a lockstep between empirical findings, and mathematical modeling. We suggest that cancer progression is an evolutionary game and needs to be viewed as such.

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Stromal Reactivity Differentially Drives Tumor Cell Evolution and Prostate Cancer Progression

10.1101/159616 ◽

2017 ◽

Cited By ~ 2

Author(s):

Ziv Frankenstein ◽

David Basanta ◽

Omar E. Franco ◽

Yan Gao ◽

Rodrigo A. Javier ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Progression ◽

Evolutionary Dynamics ◽

Multiscale Model ◽

Cell Behavior ◽

Tumor Evolution ◽

Tumor Aggressiveness ◽

Prostate Cancer Progression ◽

Reactive Stroma ◽

Clinical Biomarkers

AbstractWe implemented a hybrid multiscale model of carcinogenesis that merges data from biology and pathology on the microenvironmental regulation of prostate cancer (PCa) cell behavior. It recapitulates the biology of stromal influence in prostate cancer progression. Our data indicate that the interactions between the tumor cells and reactive stroma shape the evolutionary dynamics of PCa cells and explain overall tumor aggressiveness. We show that the degree of stromal reactivity, when coupled with the current clinical biomarkers, significantly improves PCa prognostication, both for death and recurrence, that may alter treatment decisions. We also show that stromal reactivity correlates directly with tumor growth but inversely modulates tumor evolution. This suggests that the aggressive stromal independent PCa may be an inevitable evolutionary result of poor stromal reactivity. It also suggests that purely tumor centric metrics of aggressiveness may be misleading in terms on clinical outcome.

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Review: Mathematical Modeling of Prostate Cancer and Clinical Application

Applied Sciences ◽

10.3390/app10082721 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2721 ◽

Cited By ~ 2

Author(s):

Tin Phan ◽

Sharon M. Crook ◽

Alan H. Bryce ◽

Carlo C. Maley ◽

Eric J. Kostelich ◽

...

Keyword(s):

Mathematical Modeling ◽

Prostate Cancer ◽

Prostate Specific Antigen ◽

Clinical Application ◽

Cancer Progression ◽

Spatial Data ◽

Specific Antigen ◽

Treatment Resistance ◽

Treatment Schedule ◽

Adaptive Therapy

We review and synthesize key findings and limitations of mathematical models for prostate cancer, both from theoretical work and data-validated approaches, especially concerning clinical applications. Our focus is on models of prostate cancer dynamics under treatment, particularly with a view toward optimizing hormone-based treatment schedules and estimating the onset of treatment resistance under various assumptions. Population models suggest that intermittent or adaptive therapy is more beneficial to delay cancer relapse as compared to the standard continuous therapy if treatment resistance comes at a competitive cost for cancer cells. Another consensus among existing work is that the standard biomarker for cancer growth, prostate-specific antigen, may not always correlate well with cancer progression. Instead, its doubling rate appears to be a better indicator of tumor growth. Much of the existing work utilizes simple ordinary differential equations due to difficulty in collecting spatial data and due to the early success of using prostate-specific antigen in mathematical modeling. However, a shift toward more complex and realistic models is taking place, which leaves many of the theoretical and mathematical questions unexplored. Furthermore, as adaptive therapy displays better potential than existing treatment protocols, an increasing number of studies incorporate this treatment into modeling efforts. Although existing modeling work has explored and yielded useful insights on the treatment of prostate cancer, the road to clinical application is still elusive. Among the pertinent issues needed to be addressed to bridge the gap from modeling work to clinical application are (1) real-time data validation and model identification, (2) sensitivity analysis and uncertainty quantification for model prediction, and (3) optimal treatment/schedule while considering drug properties, interactions, and toxicity. To address these issues, we suggest in-depth studies on various aspects of the parameters in dynamical models such as the evolution of parameters over time. We hope this review will assist future attempts at studying prostate cancer.

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The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer

Dynamic Games and Applications ◽

10.1007/s13235-021-00397-w ◽

2021 ◽

Author(s):

Benjamin Wölfl ◽

Hedy te Rietmole ◽

Monica Salvioli ◽

Artem Kaznatcheev ◽

Frank Thuijsman ◽

...

Keyword(s):

Game Theory ◽

Cancer Progression ◽

Evolutionary Game Theory ◽

Cancer Biology ◽

Evolutionary Dynamics ◽

Evolutionary Game ◽

Great Promise ◽

Full Potential ◽

Biological Interactions ◽

Game Theoretic

AbstractEvolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.

Download Full-text