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Purpose We built an integrated analytic pipeline to robustly and comprehensively profile molecular features of pembrolizumab-treated tumors using whole exome sequencing (WES) data from clinical trials across different indications. Experimental design We implemented a computational framework for analysis of WES data generated by different sequencing vendors for 1467 samples from 8 major tumor types. Reads were first processed by BWA-MEM, Picard, and GATK (v2) to generate analysis-ready BAM files. Quality controls (QC) before downstream analysis included Y/X reads ratio for matched normal sample as patient sex prediction and tumor/normal concordance and contamination estimation by Conpair. Key molecular features were evaluated, including tumor mutation burden (TMB) by MuTect (v1) and VEP; HLA-I typing by OptiType; neoantigen burden by NetMHC (v3.4); mutation signature by deconstructSigs; allele-specific copy number by VarScan2 and Sequenza; clonality by PyClone; presence of oncogenic viruses (eg, EBV, HBV, HPV); and LOH score indicating homologous recombination deficiency (HRD-LOH). Results Concordance rate between predicted and clinical sex was 1446/1464 (98.8%), and 1420/1467 (96.8%) samples passed tumor-normal alignment QC. Highest TMB (median [range]) was detected in melanoma (245 [2-6246]) and urothelial carcinoma (UC) (124 [4-1579]), with lowest TMB in metastatic castration-resistant prostate cancer (52 [1-6143]) and ovarian cancer (OvCa) (49.5 [8-272]). The correlation of median TMB across 7 cancer types in our data and TCGA was Spearman R = 0.957. Within indications, there was no difference in TMB distribution for sequencing data originating from different sequencing vendors and TCGA data, which demonstrated concordance across data sets and robust TMB calling by our integrated pipeline. Neoantigen burden strongly correlated with TMB (N = 1420; Spearman R = 0.890). HPV was detected in 20/129 (15.5%) head and neck squamous cell carcinomas (HNSCC) and 4/6 (66.7%) anal cancers; EBV was detected in 8/129 (6.2%) HNSCC and 18/318 (5.7%) gastric cancers (GC). The dominant mutation signatures by disease included APOBEC for UC (135/236) and HNSCC (22/122), alcohol for HCC (19/35), HRD for OvCa (12/64) and triple-negative breast cancer (51/175), UV exposure for melanoma (145/176), and dMMR for GC (84/287) and CRPC (16/155). Samples with deleterious BRCA mutations showed significantly higher HRD-LOH score (N = 1420; AUROC = 0.61 [95% CI, 0.53-0.69]) and HRD mutational signature (N = 1316; DOR = 6.4 [95% CI, 3.7-11.1]). Conclusion We assembled heterogeneous computational modules into an integrated pipeline to reliably profile diverse molecular features from WES data of nearly 1500 clinical samples across different tumor types. These data serve as a foundation for translational research efforts supporting pembrolizumab development. Citation Format: Xiaoqiao Liu, Xinwei Sher, Hongchao Lu, Jun Zhuang, Weilong Zhao, Andrew Albright, Cinthia Umemoto, Christen Wudarski, Maureen Lane, Mark Ayers, Andrea L. Webber, Sandra C. Souza, Ping Qiu, Diane Levitan, Jennifer Cho, Deepti Aurora-Garg, Matthew Marton, Alexandra Snyder, Michael Morrissey, Andrey Loboda, Ronghua Chen, Razvan Cristescu. An integrated bioinformatics pipeline for profiling cancer-immune interaction from whole exome sequencing of pembrolizumab clinical samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2483.