Title: High-Dimensional Precision Medicine From Patient-Derived Xenografts.

Authors: Rashid, Naim U; Luckett, Daniel J; Chen, Jingxiang; Lawson, Michael T; Wang, Longshaokan; Zhang, Yunshu; Laber, Eric B; Liu, Yufeng; Yeh, Jen Jen; Zeng, Donglin; Kosorok, Michael R

Published In J Am Stat Assoc, (2021)

Abstract: The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

PubMed ID: 34548714

MeSH Terms: No MeSH terms associated with this publication