Personalized Cancer Therapy Design: Robustness vs. Optimality

TL;DR

This paper develops a stochastic hybrid automaton model for prostate cancer under intermittent therapy, using IPA to optimize therapy thresholds and explore robustness versus optimality in treatment design.

Contribution

It introduces a novel IPA-based approach for sensitivity analysis and therapy optimization, highlighting the trade-off between robustness and optimality in personalized cancer treatment.

Findings

IPA provides unbiased gradient estimators for therapy parameters.

Relaxing optimality improves robustness to model uncertainties.

Sensitivity analysis identifies critical parameters affecting therapy success.

Abstract

Intermittent Androgen Suppression (IAS) is a treatment strategy for delaying or even preventing time to relapse of advanced prostate cancer. IAS consists of alternating cycles of therapy (in the form of androgen suppression) and off-treatment periods. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. In spite of extensive recent clinical experience with IAS, the design of an ideal protocol for any given patient remains one of the main challenges associated with effectively implementing this therapy. We use a threshold-based policy for optimal IAS therapy design that is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. We apply Infinitesimal Perturbation Analysis (IPA) to a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under IAS and derive unbiased estimators of the cost metric gradient with respect to various model and therapy parameters. These estimators are subsequently used for system analysis. By evaluating sensitivity estimates with respect to several model parameters, we identify critical parameters and demonstrate that relaxing the optimality condition in favor of increased robustness to modeling errors provides an alternative objective to therapy design for at least some patients.