Pembrolizumab had an acceptable side-effect profile and showed antitumor activity in patients with advanced non-small-cell lung cancer. PD-L1 expression in at least 50% of tumor cells correlated with improved efficacy of pembrolizumab. (Funded by Merck; KEYNOTE-001 ClinicalTrials.gov number, NCT01295827.).

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INTRODUCTION: Immunotherapy targeting the programmed cell death protein–1 (PD-1) axis elicits durable antitumor responses in multiple cancer types. However, clinical responses vary, and biomarkers predictive of response may help to identify patients who will derive the greatest therapeutic benefit. Clinically validated biomarkers predictive of response to the anti–PD-1 monoclonal antibody pembrolizumab include PD-1 ligand 1 (PD-L1) expression in specific cancers and high microsatellite instability (MSI-H) regardless of tumor type. Tumor mutational burden (TMB) and T cell–inflamed gene expression profile (GEP) are emerging predictive biomarkers for pembrolizumab. Both PD-L1 and GEP are inflammatory biomarkers indicative of a T cell–inflamed tumor microenvironment (TME), whereas TMB and MSI-H are indirect measures of tumor antigenicity generated by somatic tumor mutations. However, the relationship between these two categories of biomarkers is not well characterized. RATIONALE: This study assessed the potential for TMB and a T cell–inflamed GEP to jointly predict clinical response to pembrolizumab in >300 patient samples with advanced solid tumors and melanoma across 22 tumor types from four KEYNOTE clinical trials. To assess the individual and joint clinical utility of TMB and GEP, patients were stratified in four biomarker–defined clinical response groups [GEP low and TMB low (GEPlo TMBlo), GEP low and TMB high (GEPlo TMBhi), GEPhi TMBlo, and GEPhi TMBhi] based on predefined cutoffs for TMB and GEP. These patient–defined biomarker groups were further used to guide transcriptome and exome analyses of tumors in a large molecular database [The Cancer Genome Atlas (TCGA)] (n = 6384 tumors) to identify targetable patterns of biology that may modulate response and resistance. RESULTS: TMB and GEP exhibited only modest correlation and were independently predictive of response across the KEYNOTE clinical datasets. We found that objective response rates were strongest in patients with GEPhi TMBhi (37 to 57%), moderate in those with GEPhi TMBlo (12 to 35%) and GEPlo TMBhi (11 to 42%), and reduced or absent in those with GEPlo TMBlo (0 to 9%) (see the figure). Additionally, longer progression–free survival times were seen in patients with higher levels of both TMB and GEP. Findings were comparable when TMB and PD-L1 expression were jointly assessed. Within TCGA database, GEP and TMB again had a low correlation, demonstrating the potential to jointly stratify transcriptomic and genomic features across cancer types. Specific gene expression patterns reflective of TME biology showed significant associations with TMB, GEP, or both. In particular, gene set enrichment analysis identified proliferative and stromal, myeloid, and vascular biology corresponding to specific TMB-defined subgroups within GEPhi tumors. In TMBhi tumors, indication-dependent somatic DNA alterations in key cancer driver genes showed a strong negative association with GEP. CONCLUSION: This analysis shows that TMB and inflammatory biomarkers (T cell–in...

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Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use.

FOR THE SITAGLIPTIN STUDY 021 GROUP*OBJECTIVE -To examine the efficacy and safety of once-daily oral sitagliptin as monotherapy in patients with type 2 diabetes. RESULTS -Sitagliptin 100 and 200 mg produced significant (P Ͻ 0.001) placebosubtracted reductions in A1C (Ϫ0.79 and Ϫ0.94%, respectively) and fasting plasma glucose (Ϫ1.0 mmol/l [Ϫ17.1 mg/dl] and Ϫ1.2 mmol/l [Ϫ21.3 mg/dl], respectively). Patients with baseline A1C Ն9% had greater reductions in placebo-subtracted A1C with sitagliptin 100 and 200 mg (Ϫ1.52 and Ϫ1.50%, respectively) than those with baseline A1C Ͻ8% (Ϫ0.57 and Ϫ0.65%) or Ն8 to Ͻ9.0% (Ϫ0.80 and Ϫ1.13%, respectively). In a meal tolerance test, sitagliptin 100 and 200 mg significantly decreased 2-h postprandial glucose (PPG) (placebo-subtracted PPG Ϫ2.6 mmol/l [Ϫ46.7 mg/dl] and Ϫ3.0 mmol/l [Ϫ54.1 mg/dl], respectively). Results for the above key efficacy parameters were not significantly different between sitagliptin doses. Homeostasis model assessment of ␤-cell function and proinsulin-to-insulin ratio improved with sitagliptin. The incidence of hypoglycemia was similar, and overall gastrointestinal adverse experiences were slightly higher with sitagliptin. No meaningful body weight changes from baseline were observed with sitagliptin 100 (Ϫ0.2 kg) or 200 mg (Ϫ0.1 kg). The body weight change with placebo (Ϫ1.1 kg) was significantly (P Ͻ 0.01) different from that observed with sitagliptin. RESEARCH DESIGN AND METHODSCONCLUSIONS -In this 24-week study, once-daily sitagliptin monotherapy improved glycemic control in the fasting and postprandial states, improved measures of ␤-cell function, and was well tolerated in patients with type 2 diabetes.