RTI Health Solutions, The Pavilion, Towers Business Park, Wilmslow Road, Didsbury, Manchester, M20 2LS, UK.
Pfizer, Inc., La Jolla, USA.
Diagn Pathol. 2020 Jan 30;15(1):6. doi: 10.1186/s13000-020-0927-9.
To achieve optimal outcomes, an individual approach is needed in the treatment and care of patients. The potential value of tumor mutational burden (TMB) status and/or programmed cell death ligand 1 (PD-L1) expression as biomarkers to predict which patients are most likely to respond to checkpoint inhibitors has been explored in many studies. The goal of this targeted literature review is to identify data available for TMB status and/or PD-L1 expression that predict response to checkpoint inhibitors and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies.
Targeted literature searches were performed using electronic medical databases (MEDLINE, Embase, and BIOSIS) and internet searches of specified sites. Bibliographies of key systematic literature reviews and meta-analyses also were reviewed for studies of interest.
The review identified 27 studies of non-small cell lung cancer (NSCLC), 40 studies of melanoma, 10 studies of urothelial cancer, and 5 studies of renal cell cancer indications. Studies also were identified in other cancer types, e.g., colorectal, breast, gastric, and Merkel cell cancer and squamous-cell carcinoma of the head and neck. Twelve trials, including six in NSCLC and four in melanoma, evaluated TMB as a predictor of outcomes. A TMB of ≥10 mutations per megabase was shown to be an effective biomarker in the CheckMate 227 study. PD-L1 expression was included in the majority of identified studies and was found to predict response in in melanoma and in all types of NSCLC. Prediction of response was not a prespecified analysis in some studies; others had small sample sizes and wide confidence intervals. A clear predictive trend for PD-L1 expression was not identified in renal, breast, gastric, or Merkel cell cancer.
Based on data contained in this review, assessment of TMB status and PD-L1 expression may help enhance the prediction of response to checkpoint inhibition in some tumors, such as NSCLC and melanoma. In this rapidly growing area of research, further exploratory biomarkers are being investigated including tumor-infiltrating lymphocytes, immune profiling (e.g., effector T cells or regulatory T cells), epigenetic signatures, T-cell receptor repertoire, proteomics, microbiome, and metabolomics.
为了实现最佳结果，需要对患者进行个体化的治疗和护理。许多研究已经探讨了肿瘤突变负荷（TMB）状态和/或程序性死亡配体 1（PD-L1）表达作为预测哪些患者最有可能对检查点抑制剂产生反应的生物标志物的潜在价值。本目标性文献综述的目的是确定可用于预测检查点抑制剂和/或抗细胞毒性 T 淋巴细胞相关蛋白 4（CTLA-4）抗体反应的 TMB 状态和/或 PD-L1 表达的数据。
使用电子医学数据库（MEDLINE、Embase 和 BIOSIS）和指定网站的互联网搜索进行目标性文献搜索。还对关键系统文献综述和荟萃分析的参考文献进行了审查，以寻找感兴趣的研究。
综述确定了 27 项非小细胞肺癌（NSCLC）研究、40 项黑色素瘤研究、10 项尿路上皮癌研究和 5 项肾细胞癌研究。还在其他癌症类型（如结直肠癌、乳腺癌、胃癌和 Merkel 细胞癌以及头颈部鳞状细胞癌）中确定了研究。12 项试验，包括 6 项 NSCLC 试验和 4 项黑色素瘤试验，评估了 TMB 作为结局预测因子。在 CheckMate 227 研究中，TMB 为≥10 个突变/兆碱基被证明是一种有效的生物标志物。PD-L1 表达被纳入大多数确定的研究中，并被发现可预测黑色素瘤和所有类型 NSCLC 的反应。在一些研究中，预测反应不是预设分析；其他研究的样本量较小，置信区间较宽。在肾细胞癌、乳腺癌、胃癌或 Merkel 细胞癌中，并未确定 PD-L1 表达的明确预测趋势。
基于本综述中包含的数据，评估 TMB 状态和 PD-L1 表达可能有助于增强对某些肿瘤（如 NSCLC 和黑色素瘤）对检查点抑制反应的预测。在这个快速发展的研究领域，正在研究进一步的探索性生物标志物，包括肿瘤浸润淋巴细胞、免疫谱（如效应 T 细胞或调节 T 细胞）、表观遗传特征、T 细胞受体谱、蛋白质组学、微生物组和代谢组学。
