Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy

Abstract

Programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) checkpoint blockade immunotherapy elicits durable antitumor effects in multiple cancers, yet not all patients respond. We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials. Tumor mutational burden (TMB) and a T cell-inflamed gene expression profile (GEP) exhibited joint predictive utility in identifying responders and nonresponders to the PD-1 antibody pembrolizumab. TMB and GEP were independently predictive of response and demonstrated low correlation, suggesting that they capture distinct features of neoantigenicity and T cell activation. Analysis of The Cancer Genome Atlas database showed TMB and GEP to have a low correlation, and analysis by joint stratification revealed biomarker-defined patterns of targetable-resistance biology. These biomarkers may have utility in clinical trial design by guiding rational selection of anti-PD-1 monotherapy and combination immunotherapy regimens.

Fig. 1.. Individual association of TMB or T cell–inflamed GEP with anti–PD-1 response across multiple patient cohorts.

(A and B) The association of (A) TMB, defined as the sum of somatic nonsynonymous mutations, and (B) T cell–inflamed GEP with BOR was assessed in pan-tumor, HNSCC, and melanoma cohorts by central radiology review for all-patients-as-treated populations in all cohorts. A responder is defined as having a partial response (PR) or a complete response (CR); a nonresponder is defined as having no PR or CR. Nonresponders and responders for TMB, respectively, were n = 103 and n = 16 for pan-tumor, n = 86 and n = 21 for HNSCC, and n = 51 and n = 38 for melanoma cohorts. For GEP score analysis, nonresponders and responders were n = 97 and n = 16 for pan-tumor, n = 84 and n = 21 for HNSCC, and n = 48 and n = 38 for melanoma cohorts. For both (A) and (B), raw data are displayed in standard box plots with medians and interquartile ranges. (C) AUROCs for TMB and T cell–inflamed GEP in the three patient cohorts. Youden Index–associated cutoffs for TMB in each cohort are shown.

Fig. 2.. Joint relationship of TMB or T cell–inflamed GEP with anti–PD-1 response across multiple patient cohorts.

(A) Relationships of both TMB and T cell–inflamed GEP signatures with BOR. A responder is defined as having a PR or CR (filled circles); a nonresponder has no PR or CR (open circles). Dashed horizontal lines represent the Youden Index–associated cutoffs for TMB in each cohort as derived from AUROCs in Fig. 1C. Dashed vertical lines represent a discovery cutoff for the T cell–inflamed GEP selected via analysis of pan-cancer data. (B) Response (PR or CR) rates [expressed as a percentage calculated as the number of responders divided by the number in the cutoff-defined group, with 95% confidence intervals (CI)] per TMB cutoff status and Tcell–inflamed GEP cutoff status as designated in (A). TMB^hi and TMB^lo response groups are defined by values greater than or equal to and less than Youden Index–associated cut points (102.5, 86, and 191.5 for pan-cancer, HNSCC, and mela-noma cohorts, respectively); GEP^hi and GEP^lo groups are defined by cutoffs greater than or equal to and less than −0.318, respectively.

Fig. 3.. Relationship between TMB and T cell–inflamed GEP signatures and PFS after anti-PD-1 treatment across multiple patient cohorts.

Relationships of TMB and T cell–inflamed GEP with PFS in all patients as treated per TMB cutoff and GEP cutoff as described in the legend to Fig. 2. Median PFS times in days for (A) pan-tumor, (B) HNSCC, and (C) melanoma cohorts for TMB^hi versus TMB^lo were 115 versus 59 (hazard ratio, 0.48; 95% CI, 0.30 to 0.76), 64 versus 64 (0.70; 0.46 to 1.07), and 502 versus 85 (0.48; 0.28 to 0.84); those for GEP^hi versus GEP^lo were 96 versus 57 (0.54; 0.35 to 0.81), 103 versus 57 (0.45; 0.28 to 0.72), and 418 versus 90 (0.73; 0.40 to 1.31); those for TMB^hi GEP^hi versus TMB^lo GEP^lo or TMB^lo GEP^lo were 189 versus 59 (0.43; 0.26 to 0.71), 110 versus 62 (0.51; 0.32 to 0.82), and 504 versus 123 (0.63; 0.36 to 1.09). Kaplan-Meier plots are shown, and median survival was estimated on the basis of Kaplan-Meier estimates. Hazard ratios with 95% CI were derived from a Cox proportional model fit, with adjustment for baseline ECOG score and protocol where relevant.

Fig. 4.. Relationships of TMB, GEP, and other key biomarkers with gene expression across tumor types in TCGA.

(A) Data are stratified by TMB and GEP cutoffs, which are equivalent in terms of prevalence to those that define the clinical response groups in the pan-tumor cohort of patients treated with pembrolizumab from the KEYNOTE studies. The WES cutoff of >100 mutations per exome for TMB was chosen to match the Youden Index–associated TMB cutoff defined for the pan-tumor cohort. The GEP cutoff was chosen as the top pan-cancer tertile value. Columns represent individual tumors, and rows represent genomic features. Red and green represent elevated and decreased expression, respectively (versus the median, in black), for continuous variables, and red and white represent true and false for Boolean (binary) variables. In the absence of MSI evaluation across cancer types, MSI-H status was determined by loss of MLH1 gene expression by using cutoffs determined by the bimodality in the distribution of expression. (B) Percentages of tumors in each cancer type in biomarker-defined response groups as defined in (A) in TCGA database. SCC, squamous cell carcinoma; MSS, microsatellite stable; TNBC, triple-negative breast cancer.

Fig. 5.. Transcriptomic and genomic features defined by the GEP and TMB biomarker–based stratification in TCGA database.

(A) Association of T cell–inflamed GEP (15) with other key markers and expression signatures representative of T cell inflammation and a cytolytic environment, including chemokine signature (29), Immunoscore (30), and cytolytic activity (CYT) (13). (B) Association between T cell–inflamed GEP and expression of each gene in TCGA for tumors with a TMB of >100 mutations per exome (x axis) and in tumors with a TMB of ≤100 mutations per exome (y axis). (C) Each gene in the transcriptome is assigned to one of four clusters determined by cutoffs obtained from the distribution of correlation with the T cell-inflamed GEP. The cutoffs used were the inflection point where the distribution deviates from normal on the positive side (0.15; 83rd quantile), the cut point that selects T cell–inflamed GEP genes (0.6; 98% quantile), and the inflection point where the distribution deviates from normal on the negative side (−0.15; 15th quantile). Vertical lines represent cutoffs for gene sets 1, 2, and 3 (r > 0.6, r = 0.15 to 0.6, and r < −0.15, respectively); gene sets are color coded on the regression line. (D) Gene set annotation in each cluster suggested enrichment for biological patterns with distinct relevance for the individual biomarker-based groups. Contour plots illustrate the association with TMB and GEP of selected patterns of TME and cellular biology represented by gene expression modules formed by genes coexpressed in TCGA database. Blue and red represent under- and overexpression, respectively.

Fig. 6.. Cancer driver genes associated with immune evasion in selected tumor types.

(A) Volcano plots of AUROC and rank sum P values illustrating the association of somatic SNV mutations with GEP in lung squamous cell carcinoma, lung adenocarcinoma, and colorectal adenocarcinoma in TCGA database. Analysis was restricted to cancer types having >20% of tumors with TMB^hi (>100 mutations per exome). For each cancer type, the negative logi₀-transformed rank sum P value between GEP and mutations was calculated for each gene. (B) Rank sum P values of association between GEP and mutations in selected genes. The selection was made on the basis of a nominal P value of <0.01 for negative association with GEP in any cancer type and an alteration frequency of ≥10% in that cancer type. Negative and positive associations are represented in blue and red, respectively. Negative associations for known cancer driver genes are shown in boxes.