蛋白质组学是一种强大的方法，可以快速增进我们对癌症发展的理解。通过对癌症患者中蛋白质表达与遗传、药物基因组学和免疫景观的详细表征，可以为癌症中蛋白质功能角色提供新的认识。通过利用癌症基因组图谱（TCGA）中的基因型数据和癌症蛋白组图谱（TCPA）中的蛋白质表达数据，我们对31种癌症类型中遗传变异对蛋白质表达的影响进行了表征，并鉴定出了大约10万个蛋白质数量性状位点（pQTL）。其中，超过8000个pQTL与患者的总体生存相关。此外，通过对350多种预测的抗癌药物在患者中的蛋白质表达影响进行分析，揭示了近23万个显著相关性。此外，还发现了大约2.1万个蛋白质表达与免疫细胞含量之间的显著相关性。最后，我们开发了一个用户友好的数据门户网站GPIP（https://hanlaboratory.com/GPIP），其中包含多个模块，可供研究人员探索、可视化和浏览多维数据。这项详细分析揭示了蛋白质组学景观与遗传变异、患者预后、免疫微环境和药物反应在癌症类型间的相关性，为癌症中蛋白质的功能研究提供了一个有价值的临床见解资源，并鼓励进一步的功能研究。

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.