Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes
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Curated by eLife
Evaluation Summary:
Zhang et al use evolution-guided mathematical models to guide the timing and dosing of arbiterone treatment in castrate-resistant prostate cancer. While the sample size is limited, the implications of the study outcome are broad and compelling, and the paper importantly highlights the transformative potential of deeply interdisciplinary research.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance.
Methods:
In the study cohort, abiraterone was stopped when PSA was <50% of pretreatment value and resumed when PSA returned to baseline. Results are compared to a contemporaneous cohort who had >50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing.
Results:
17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53–70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; p<0.001) and median overall survival (OS; 58.5 months; hazard ratio, 0.41, 95% confidence interval (CI), 0.20–0.83, p<0.001) compared to 14.3 and 31.3 months in the SOC cohort. On average, study subjects received no abiraterone during 46% of time on trial. Longitudinal trial data demonstrated the competition coefficient ratio ( α RS /α SR ) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for α RS /α SR, cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction.
Conclusions:
Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes.
Funding:
Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network).
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Evaluation Summary:
Zhang et al use evolution-guided mathematical models to guide the timing and dosing of arbiterone treatment in castrate-resistant prostate cancer. While the sample size is limited, the implications of the study outcome are broad and compelling, and the paper importantly highlights the transformative potential of deeply interdisciplinary research.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
The authors presented updated results for a clinical trial described in a previous publication (Zhang J et al 2017). With the updated results, the authors were able to further support the validity of their evolution-based model proposed before. These datasets also allow the authors to fit individual-level evolution models and examine critical parameters in their models.
The concept of adaptive therapy is critical and has previously attracted broad attention in the field. The earlier work (Zhang J et al 2017) showed promising results in improving prognosis in prostate cancer patients. In this paper, the follow-up data for this clinical trial clearly confirms its previous findings that adaptive therapy was able to improve TTP and OS.
The authors also went on to infer an evolution model of treatment sensitive …Reviewer #1 (Public Review):
The authors presented updated results for a clinical trial described in a previous publication (Zhang J et al 2017). With the updated results, the authors were able to further support the validity of their evolution-based model proposed before. These datasets also allow the authors to fit individual-level evolution models and examine critical parameters in their models.
The concept of adaptive therapy is critical and has previously attracted broad attention in the field. The earlier work (Zhang J et al 2017) showed promising results in improving prognosis in prostate cancer patients. In this paper, the follow-up data for this clinical trial clearly confirms its previous findings that adaptive therapy was able to improve TTP and OS.
The authors also went on to infer an evolution model of treatment sensitive and resistant cells for each individual patient. With a small number of parameters, the authors can fit most patients' longitudinal data tightly. The authors found some parameters are important to determine the outcome of adaptive therapy. These results are interesting and could have clinical implications, but some model assumptions are strong (like assuming a shared competition coefficient across patients) and some claims need more explicit analysis.
One particularly interesting result from the modeling analysis is that failure of adaptive therapy is caused by overtreatment. However, the readers need to keep in mind that this conclusion is under the simple model described in the paper. More complicated clone composition, interaction and evolution paths will affect this conclusion. -
Reviewer #2 (Public Review):
In this study, Zhang et al. expand on their previous work on using mathematical modelling to guide the timing and dosing of arbiterone treatment in castrate-resistant prostate cancer. The study presents the results of a follow-up pilot trial with 33 patients and adapts an updated mathematical model to fit longitudinal patient data. While the sample size is limited, the implications of the study outcome are broad and compelling. The manuscript can be strengthened by showing that there are no statistically significant differences between the two treatment groups in terms of additional clinical features, such as prior therapies.
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