Biomarkers of response to checkpoint inhibitors beyond PD-L1 in lung cancer

医学 肺癌 肿瘤微环境 癌症免疫疗法 抗原 免疫系统 生物标志物 免疫学 免疫检查点 癌症研究 肿瘤科 抗原呈递 内科学 癌症 T细胞 免疫疗法 生物 生物化学
作者
Lynette M. Sholl
出处
期刊:Modern Pathology [Elsevier BV]
卷期号:35 (Suppl 1): 66-74 被引量:93
标识
DOI:10.1038/s41379-021-00932-5
摘要

Immunotherapy, including use of checkpoint inhibitors against PD-1, PD-L1, and CTLA-4, forms the backbone of oncologic management for the majority of non-small cell lung carcinoma patients. However, response to these therapies varies widely, from patients who have complete resolution of metastatic disease and long-term remission, to those who rapidly progress and succumb to their cancer despite use of the newest checkpoint inhibitors. While PD-L1 protein expression by immunohistochemistry serves as the principle predictive biomarker for immunotherapy response, neither the sensitivity nor the specificity of this approach is optimal, and clinical PD-L1 testing is plagued by concerns around result reproducibility and confusion born from the proliferation of different companion diagnostic assays. At the same time, insights into tumor and host immune-specific factors that inform both prognosis and response prediction are beginning to define better immunotherapy biomarkers. Beyond immune checkpoint expression status, common themes in analyses of immunotherapy response prediction include cancer neoantigen production, the state of the antigen presentation pathway in both tumor and antigen presenting cells, the admixture of effector and suppressor immune cells in the tumor microenvironment, and the genomic drivers and comutations that can influence the all of these variables. This review will address the state of PD-L1 testing in lung cancer, the role for tumor mutation burden as a predictive biomarker, the evolving status of human leukocyte antigen/major histocompatibility complex expression as a marker of antigen presentation, approaches to tumor immune cell quantitation including by multiplex immunofluorescence, and the importance of tumor genomic profiling to ascertain oncogenic driver (EGFR, ALK, KRAS, MET, etc.) and co-mutation (STK11, KEAP1, SMARCA4) status.
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