High-Dimensional Precision Medicine From Patient-Derived Xenografts.

Journal: Journal of the American Statistical Association
Published Date: Nov 12, 2020

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.

Authors

  • Naim U Rashid
    Department of Biostatistics, University of North Carolina at Chapel Hill.
  • Daniel J Luckett
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Jingxiang Chen
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Michael T Lawson
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Longshaokan Wang
    Department of Statistics, North Carolina State University, Raleigh, NC, USA.
  • Yunshu Zhang
    Department of Statistics, North Carolina State University, Raleigh, NC, USA.
  • Eric B Laber
    Department of Statistics, North Carolina State University, Raleigh, NC, USA.
  • Yufeng Liu
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Jen Jen Yeh
    Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Donglin Zeng
    Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516.
  • Michael R Kosorok
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.

Keywords

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