Optimizing Adaptive Hormone Control for Personalized Prostate Cancer Treatment

ORAL

Abstract

With the oncologist acting as the "game leader", we employ a Stackelberg game-theoretic model involving multiple populations to study prostate cancer. We refine the drug dosing schedule using an empirical Bayes feed-forward analysis, based on clinical data that reflects each patient's prostate-specific drug response. Our approach quantitatively explores the parameter landscape of this adaptive multipopulation model, aiming to combat drug-resistant prostate cancer by fostering competition among drug-sensitive cell populations. Our results suggest that not only is it is feasible to considerably extend cancer suppression duration through careful optimization, but even transform metastatic prostate cancer into a chronic condition instead of an acute one for most patients, with supporting clinical and analytical evidence.

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Publication: https://arxiv.org/pdf/2410.16005

Presenters

  • Trung Phan

    • Johns Hopkins University

Authors

  • Trung Phan

    • Johns Hopkins University

  • Shengkai Li

    • Princeton University

  • Benjamin Howe

    • Princeton University

  • Sarah R Amend

    • Johns Hopkins Medical Institute

  • Kenneth J Pienta

    • Johns Hopkins Medical Institute
    • Johns Hopkins University

  • Joel S Brown

    • Moffitt Cancer Centre
    • Moffitt Cancer Center

  • Robert A Gatenby

    • Moffitt Cancer Centre

  • Constantine Frangakis

    • Johns Hopkins University

  • Robert H Austin

    • Princeton University

  • Ioannis G Kevrekidis

    • Johns Hopkins University
    • Department of Chemical and Biomolecular Engineering, John Hopkins University