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

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.

Figure 1.. Systematic Evaluation of the Correlates of Therapy to Inhibit Programmed Cell Death 1 and Its Ligand (PD-1/PD-L1) Across Different Cancer Types

A, Correlation of log₁₀ (mutational burden) with the objective response rate to anti–PD-1/PD-L1 therapy across cancer types. The estimated CD8⁺ T-cell abundance (eCD8T) of each cancer type is color coded where red denotes high abundance and blue, low abundance. B, Correlation of eCD8T with the objective response rate to anti–PD-1/PD-L1 therapy across cancer types. The mutational burden of each cancer type is color coded where red denotes high mutational burden and blue, low mutational burden. C, Triple axis of anti–PD-1/PD-L1 response shows the distribution of Spearman correlation coefficients (blue) (radial axis) of the 3 classes of variables (polar axis) associated with tumor neoantigen (red), tumor immune microenvironment (green), and checkpoint targets (yellow). The dots inside the innermost circle denote the cases where the absolute value of the correlation coefficients are less than 0.30. ACC indicates adrenocortical carcinoma; BLCA, bladder carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD MSI, microsatellite-unstable colon adenocarcinoma; COAD MSS, microsatellite-stable colon adenocarcinoma; CPS, combined positive score; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; INF, interferon; KIRC, kidney renal clear cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MB, mutational burden; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; and UVM, uveal melanoma.

Figure 2.. Combined Regression Models for Predicting Therapy to Inhibit Programmed Cell Death 1 and Its Ligand (Anti–PD-1/PD-L1) Across Cancer Types

A, Combined effect of the mutational burden and estimated CD8⁺ T-cell abundance (eCD8T) bivariate model (Spearman R = 0.85; P < 1.1 × 10⁻⁶). The regression formula for the objective response rate (ORR) is 0.13 × log₁₀ (mutational burden) + 1.3 × eCD8T − 0.21. The fraction of high PD-1 messenger RNA expression samples (fPD1) of each cancer type is color coded where red denotes a high fraction and blue, a low fraction. B, Combined effect of the mutational burden–eCD8T-fPD1 trivariate model (Spearman R = 0.90; P < 4.1 × 10⁻⁸). The regression formula for the ORR is 0.12 × log₁₀ (mutational burden) + 0.96 × eCD8T + 0.21 × fPD1 − 0.19. C and D, The estimated ORR to anti–PD-1/PD-L1 therapy in polyomavirus-positive Merkel cell carcinoma (MCC) (C) and small cell lung cancer (SCLC) (D) using a univariate mutational burden model (gray), the mutational burden–eCD8T bivariate model (blue), and the mutational burden–eCD8T-fPD1 trivariate model (yellow). The observed ORRs are depicted with dotted lines.