Predicting Individual Responses in Phase I Oncology Trials Using Routinely Collected Clinical Biomarkers

Abstract

Information which may support an individual’s participation in a cancer phase I trial, such as their response to prior therapies, other medical conditions they may have, features in their tumor genomic profile, etc., should be considered to avoid negative consequences of participating in the trial. However, knowing which pieces of information are relevant is crucial. We built predictive models of responses in phase I trials using routinely collected demographic and clinical chemistry data. We obtained data on 1386 participants in 252 phase I trials pursued within the US Oncology clinical trial network in the years 2007–2018. We used mixed models, as well as machine learning (ML) techniques exploiting randomly generated training and test data sets, to build predictive models of four different outcomes while controlling for which trial a participant was enrolled in. The outcomes of interest were time on the experimental therapy, time on the study drug relative to the drug during which their cancer progressed, 90-day mortality, and Grade 5 toxicity. We also controlled for other potential sources of variation in outcomes such as weight, height, and sex. We found that an unfavorable participant profile includes elevated white blood cells, low albumin levels, and low hemoglobin levels, as well as low BMI for mortality risk, among other factors, many of which are consistent with previously published findings. In addition, our ML-based predictions achieved, on average, > 80% area under the receiver/operator curve (AUC) statistics reflecting good accuracy for predicting dichotomous outcomes. Our findings could be of general use when recruiting for Phase I oncology clinical trials.