The transcriptomic expression pattern of immune checkpoints shows heterogeneity between and within cancer types.

Transcriptomic expression profiles of immune checkpoint markers are of interest in order to decipher the mechanisms of immunotherapy response and resistance. Overall, 514 patients with various solid tumors were retrospectively analyzed in this study. The RNA expression levels of tumor checkpoint markers (ADORA2A, BTLA, CD276, CTLA4, IDO1, IDO2, LAG3, NOS2, PD-1, PD-L1, PD-L2, PVR, TIGIT, TIM3, VISTA, and VTCN) were ranked from 0-100 percentile based on a reference population. The expression of each checkpoint was correlated with cancer type, microsatellite instability (MSI), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) by immunohistochemistry (IHC). The cohort included 30 different tumor types, with colorectal cancer being the most common (27%). When RNA percentile rank values were categorized as "Low" (0-24), "Intermediate" (25-74), and "High" (75-100), each patient had a distinctive portfolio of the categorical expression of 16 checkpoint markers. Association between some checkpoint markers and cancer types were observed; NOS2 showed significantly higher expression in colorectal and stomach cancer (P < 0.001). Principal component analysis demonstrated no clear association between combined RNA expression patterns of 16 checkpoint markers and cancer types, TMB, MSI or PD-L1 IHC. Immune checkpoint RNA expression varies from patient to patient, both within and between tumor types, though colorectal and stomach cancer showed the highest levels of NOS2, a mediator of inflammation and immunosuppression. There were no specific combined expression patterns correlated with MSI, TMB or PD-L1 IHC. Next generation immunotherapy trials may benefit from individual analysis of patient tumors as selection criteria for specific immunomodulatory approaches.

T-cell priming transcriptomic markers: implications of immunome heterogeneity for precision immunotherapy.

Immune checkpoint blockade is effective for only a subset of cancers. Targeting T-cell priming markers (TPMs) may enhance activity, but proper application of these agents in the clinic is challenging due to immune complexity and heterogeneity. We interrogated transcriptomics of 15 TPMs (CD137, CD27, CD28, CD80, CD86, CD40, CD40LG, GITR, ICOS, ICOSLG, OX40, OX40LG, GZMB, IFNG, and TBX21) in a pan-cancer cohort (N = 514 patients, 30 types of cancer). TPM expression was analyzed for correlation with histological type, microsatellite instability high (MSI-H), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) expression. Among 514 patients, the most common histological types were colorectal (27%), pancreatic (11%), and breast cancer (10%). No statistically significant association between histological type and TPM expression was seen. In contrast, expression of GZMB (granzyme B, a serine protease stored in activated T and NK cells that induces cancer cell apoptosis) and IFNG (activates cytotoxic T cells) were significantly higher in tumors with MSI-H, TMB ≥ 10 mutations/mb and PD-L1 ≥ 1%. PD-L1 ≥ 1% was also associated with significantly higher CD137, GITR, and ICOS expression. Patients' tumors were classified into "Hot", "Mixed", or "Cold" clusters based on TPM expression using hierarchical clustering. The cold cluster showed a significantly lower proportion of tumors with PD-L1 ≥ 1%. Overall, 502 patients (98%) had individually distinct patterns of TPM expression. Diverse expression patterns of TPMs independent of histological type but correlating with other immunotherapy biomarkers (PD-L1 ≥ 1%, MSI-H and TMB ≥ 10 mutations/mb) were observed. Individualized selection of patients based on TPM immunomic profiles may potentially help with immunotherapy optimization.

Tumor Infiltrating Lymphocyte Expression of PD-1 Predicts Response to Anti-PD-1/PD-L1 Immunotherapy.

Introduction: Many studies have focused on the role of programmed death receptor ligand 1 (PD-L1) expression in predicting immunotherapy outcomes. Limited clinical data are available regarding the role of programmed death receptor 1 (PD-1; the PD-L1 receptor) expressing tumor-infiltrating lymphocytes (TILs) in PD-1/PD-L1 antibody responsiveness. However, preclinical studies demonstrate that TILs expressing PD-1 contribute to tumor immune evasion. Methods: This study analyzed the association between TIL-PD-1 status and outcome after immune checkpoint blockade (ICB) therapy. We evaluated 123 patients with various solid tumors treated with monoclonal antibodies targeting the PD-1/PD-L1 signaling axis. Additionally, 8706 solid tumor specimens were assessed for TIL-PD-1 and tumor mutational burden (TMB) status. Results: The presence of PD-1-expressing TILs in tumors was associated with increased median progression-free survival (7.0 vs 1.9 months; p = 0.006) and overall survival (18.1 vs 8.0 months; p = 0.04) after treatment with ICB. TIL-PD-1-positive patients had an objective response rate (ORR) of 41% (95% CI, 24-61; N = 12/29) compared with 17% (95% CI, 4-43; N = 3/17) for TIL-PD-1-negative patients (p = 0.18). Analyzed as continuous variables, TIL-PD-1 and TMB showed a weak correlation in 8706 solid tumor samples (Pearson r = 0.074); when analyzed as categorical variables (cutoffs: TIL-PD-1 ≥ 1% and TMB ≥ 10 mutations/Mb), the two variables are correlated (p < 0.0001). TIL-PD-1-positive status is also associated with enrichment of pathologic variants within several genes, most notably TP53 (adjusted p < 0.05). Conclusion: TIL-PD-1 positivity in tumors (≥ 1%) is associated with significantly longer progression-free and overall survival after ICB. ClinicalTrials.gov ID: NCT02478931.

Test Volume Ratio Benchmarking to Identify and Reduce Low-Value Laboratory Utilization.

OBJECTIVES: We analyzed test volume data to identify low-value test utilization. We subsequently tracked the efficacy of interventions to improve test utilization by decreasing low-value testing. METHODS: Test volume data for analytes included in the Choosing Wisely guidelines were analyzed to identify population outliers. Outliers were defined by test volume ratios of either analyte to sodium or paired analytes to correct for variation in patient volumes at each site. Interventions to improve test utilization were targeted to outlier sites. Relative efficacy in reducing low-value testing was tracked at those sites. RESULTS: After appropriate data cleaning, test volume ratios for 17 analytes paired with sodium and 8 pairs of analytes were acquired from 108 national sites. A site with abnormally high Clostridium difficile/sodium ratio was selected for intervention, leading to a 71% decrease in C difficile tests. Two different interventions to decrease creatine kinase MB isoform (CKMB) testing were performed at two unique sites with abnormally high CKMB/troponin ratios. These interventions decreased CKMB by 11% and 98% at the different sites, showing the efficacy of the different kinds of interventions. CONCLUSIONS: Test volume ratio analysis and benchmarking enable identification of low-value test utilization.