Models to understand and predict the clinical efficacy of combination cancer
therapy
Dr. Adam Palmer
Assistant Professor of Pharmacology, University of North Carolina at Chapel Hill
Wednesday October 20, 2021, 12:00 to 1:00 pm EDT
Register for free at https://www.rosaandco.com/webinars
Abstract:
Developing optimal drug combinations is one of the central challenges of cancer
treatment research: drug combinations are used to treat most types of cancer,
and are almost exclusively responsible for cures of advanced cancers. However,
historically successful combination therapies were developed empirically, and
the mechanistic basis for their efficacy has been largely speculative. I will
present experiments, models, and computational analyses of clinical trial data,
to investigate the mechanistic basis of clinically successful combination
therapies across 12 types of cancer and 30 different therapies. These studies
consistently identify the control of cancer heterogeneity between-patients
(inter-tumor) and within-patients (intra-tumor) by independently active drugs
as critical contributors to the efficacy of combination therapies in human
patients. The key approaches for data analysis and modeling in these studies
consist of adapting classical pharmacological concepts to the complex situation
of populations of cancers with heterogeneous drug sensitivity. We find that
supra-additive drug interactions are uncommon in humans among approved
combination therapies, and multiple curative regimens are consistent with drug
additivity in both experimental measurements and in clinical outcomes.
Mathematical descriptions of heterogeneity in cellular or patient populations,
and quantitative experimental measurements of how drug combinations address
heterogeneity, lead to accurate predictions of clinical trial results for a
diverse range of combination therapies, including those with immune checkpoint
inhibitors (correlation between observed and expected Progression Free Survival
in 14 trials, Pearson r = 0.98, P < 10^-8) and curative chemotherapy regimens
for hematological cancers (correlation between observed and expected response
rates and cure rates in childhood ALL, Pearson r = 0.99, P < 10^-10). These
results have broad significance for the treatment of cancers, for the
interpretation of clinical trials, and point to new opportunities to use
combination therapies with greater precision.