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Brief Report
August 22, 2019

Multiomics Prediction of Response Rates to Therapies to Inhibit Programmed Cell Death 1 and Programmed Cell Death 1 Ligand 1

Author Affiliations
  • 1Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
JAMA Oncol. Published online August 22, 2019. doi:10.1001/jamaoncol.2019.2311
Key Points

Question  What are the most important variables that predict the response to therapy to inhibit programmed cell death 1 and its ligand across different cancer types?

Findings  This analysis of multiomics data from the Cancer Genome Atlas cohort and objective response rates to therapy data across 21 cancer types found that estimated CD8+ T-cell abundance is the most predictive, followed by tumor mutational burden and the fraction of samples with high programmed cell death 1 gene expression.

Meaning  Immune, neoantigen, and checkpoint target variables are required in combination for accurately predicting Fthe response to therapy to inhibit programmed cell death 1 and its ligand across multiple cancers.

Abstract

Importance  Therapies to inhibit programmed cell death 1 and its ligand (anti–PD-1/PD-L1) provide significant survival benefits in many cancers, but the efficacy of these treatments varies considerably across different cancer types. Identifying the underlying variables associated with this cancer type–specific response remains an important open research challenge.

Objective  To evaluate systematically a multitude of neoantigen-, checkpoint-, and immune response–related variables to determine the key variables that accurately predict the response to anti–PD-1/PD-L1 therapy across different cancer types.

Design, Setting, and Participants  This analysis of a broad range of data used whole-exome and RNA sequencing of 7187 patients from the publicly available Cancer Genome Atlas and the objective response rate (ORR) data of 21 cancer types obtained from a collection of clinical trials. Thirty-six variables of 3 distinct classes considered were associated with (1) tumor neoantigens, (2) tumor microenvironment and inflammation, and (3) the checkpoint targets. The performance of each class of variables and their combinations in predicting the ORR to anti–PD-1/PD-L1 therapy was evaluated. Accuracy of predictions was quantified with Spearman correlation measured using a standard leave-one-out cross-validation, a statistical method of evaluating a statistical model by dividing data into 2 segments: one to train the model and the other to validate the model. Data were collected from October 19 through 31, 2018, and were analyzed from November 1 through December 14, 2018.

Main Outcomes and Measures  Response to anti-PD-1/PD-1 therapy.

Results  Among the 36 variables, estimated CD8+ T-cell abundance was the most predictive of the response to anti–PD-1/PD-L1 therapy across cancer types (Spearman R = 0.72; P < 2.3 × 10−4), followed by the tumor mutational burden (Spearman R = 0.68; P < 6.2 × 10−4), and the fraction of samples with high PD1 gene expression (Spearman R = 0.68; P < 6.9 × 10−4). Notably these top 3 variables cover the 3 classes considered, and their combination is highly correlated with response (Spearman R = 0.90; P < 4.1 × 10−8), explaining more than 80% of the ORR variance observed across different tumor types.

Conclusions and Relevance  That we know of, this is the first systematic evaluation of the different variables associated with anti–PD-1/PD-L1 therapy response across different tumor types. The findings suggest that the 3 key variables can explain most of the observed cross-cancer response variability, but their relative explanatory roles may vary in specific cancer types.

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