Abstract Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in cancer patients could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas (TCGA) and protein expression data from The Cancer Proteome Atlas (TCPA), we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTL were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer.