Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Cell Rep. 2017 Jan 3;18(1):248-262. doi: 10.1016/j.celrep.2016.12.019.
The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
癌症基因组图谱揭示了人类癌症的基因组景观。与此同时，免疫疗法正在改变晚期癌症的治疗方式。不幸的是，大多数患者对免疫疗法没有反应，因此，确定预测标志物和耐药机制是一个研究热点。为了增加我们对肿瘤-免疫细胞相互作用的理解，我们对 20 种实体瘤的肿瘤内免疫景观和肿瘤抗原组进行了特征分析，并创建了癌症免疫图谱（https://tcia.at/）。免疫浸润细胞的细胞特征分析表明，肿瘤基因型决定免疫表型和肿瘤逃逸机制。我们使用机器学习方法确定了肿瘤免疫原性的决定因素，并开发了一种称为免疫表型评分的定量评分方案。在两个独立的验证队列中，免疫表型评分是预测抗细胞毒性 T 淋巴细胞相关抗原 4（抗 CTLA-4）和抗程序性死亡蛋白 1（抗 PD-1）抗体反应的更好指标。我们的研究结果和这个资源可能有助于为癌症免疫治疗提供信息，并促进精准免疫肿瘤学的发展。
