# Predicting patient-specific responses to personalize androgen deprivation therapy for prostate cancer

> **NIH NIH R21** · H. LEE MOFFITT CANCER CTR & RES INST · 2020 · $222,068

## Abstract

Project Summary
Prostate cancer (PCa) is the most prevalent cancer in men in the US. A major obstacle in PCa therapy is that
continuous treatment at maximum tolerable doses often renders the tumor resistant. PCa is comprised of
androgen-independent cancer stem cells (PCaSC) and more differentiated, androgen-dependent PCa cells
(PCaC) that make up the bulk of the tumor. Treatment-induced enrichment in PCaSC appears to confer
therapy resistance. Continuous treatment neglects the evolutionary dynamics where competition, adaptation
and selection between treatment-sensitive and -resistant cells contribute to therapy failure. Intermittent
androgen deprivation therapy (IADT) with on-and off-treatment cycles may counteract competitive release of
androgen-independent cancer cells and delay time to progression (TTP). Successful clinical implementation of
IADT requires identification of resistance mechanisms, prediction of responses, and determination of clinically
actionable triggers for pausing and resuming IADT cycles. We propose to integrate our mathematical,
biological, clinical, and statistical expertise to test the hypothesis that PCaSC dynamics underlie response to
therapy and evolution of resistance in IADT. By fitting different mechanistic mathematical models to
retrospective longitudinal data of individual patients in a training data set we can determine clinically plausible
model parameter distributions. From treatment response dynamics in early treatment cycles, we aim to
simulate and reliably forecast an individual patient's response to subsequent treatment cycles in a validation
data set. Then, we will use the validated model to simulate IADT protocols with different cycle intervals for
each patient. Nominal and relative cutoffs for PSA levels to pause and resume IADT will be simulated and
TTP will be determined. PSA cutoffs that maximize TTP will be correlated with model-derived PCaSC
dynamics and used to identify optimal patient-specific IADT protocols. Compared to androgen deprivation
alone, co-treatment with docetaxel (DOC) improves patient survival with the survival benefit dependent on
treatment timing. We will simulate DOC therapy initialized at different IADT cycles to determine DOC timing-
dependent TTP in correlation to patient-specific PCaSC dynamics parameters to further improve PCa
treatment outcomes. This exploratory high-risk and high-reward project may provide a significant conceptual
advance in PCa treatment, away from continuous androgen deprivation at maximum tolerable dose until the
tumor becomes resistant towards an IADT protocol, using triggers based on individual patients' response
dynamics. If successful, the findings of this proposal will inform the optimal protocol and required sample size
of a subsequent first-in-kind clinical trial of personalized adaptive IADT that delays TTP.

## Key facts

- **NIH application ID:** 9962349
- **Project number:** 5R21CA234787-02
- **Recipient organization:** H. LEE MOFFITT CANCER CTR & RES INST
- **Principal Investigator:** Heiko Enderling
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $222,068
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9962349

## Citation

> US National Institutes of Health, RePORTER application 9962349, Predicting patient-specific responses to personalize androgen deprivation therapy for prostate cancer (5R21CA234787-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9962349. Licensed CC0.

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