Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, Maryland.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
Cancer Res. 2022 Jun 6;82(11):2076-2083. doi: 10.1158/0008-5472.CAN-21-2542.
The FDA has recently approved a high tumor mutational burden (TMB-high) biomarker, defined by ≥10 mutations/Mb, for the treatment of solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies have shown that this TMB-high biomarker is only able to stratify ICI responders in a subset of cancer types, and the mechanisms underlying this observation have remained unknown. The tumor immune microenvironment (TME) may modulate the stratification power of TMB (termed TMB power), determining if it will be predictive of ICI response in a given cancer type. To systematically study this hypothesis, we inferred the levels of 31 immune-related factors characteristic of the TME of different cancer types in The Cancer Genome Atlas. Integration of this information with TMB and response data of 2,277 patients treated with anti-PD1 identified key immune factors that determine TMB power across 14 different cancer types. We find that high levels of M1 macrophages and low resting dendritic cells in the TME characterized cancer types with high TMB power. A model based on these two immune factors strongly predicted TMB power in a given cancer type during cross-validation and testing (Spearman Rho = 0.76 and 1, respectively). Using this model, we predicted the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available. Our analysis indicates that TMB-high may be highly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.
This study uncovers immune-related factors that may modulate the relationship between high tumor mutational burden and ICI response, which can help prioritize cancer types for clinical trials.
美国食品和药物管理局最近批准了一种高肿瘤突变负担(TMB-high)生物标志物,定义为≥10 个突变/Mb,用于治疗使用 pembrolizumab(一种针对 PD1 的免疫检查点抑制剂)的实体瘤。然而,最近的研究表明,这种 TMB-high 生物标志物仅能够对某些癌症类型的免疫检查点抑制剂应答者进行分层,而这种观察结果的机制仍然未知。肿瘤免疫微环境(TME)可能调节 TMB 的分层能力(称为 TMB 能力),决定它是否会在特定癌症类型中预测免疫检查点抑制剂的反应。为了系统地研究这一假设,我们在癌症基因组图谱中推断了不同癌症类型的 TME 中 31 种具有免疫相关性的因素的水平。将这些信息与 2277 名接受抗 PD1 治疗的患者的 TMB 和反应数据整合在一起,确定了决定 14 种不同癌症类型 TMB 能力的关键免疫因素。我们发现,TME 中高水平的 M1 巨噬细胞和静止树突状细胞特征性地描述了具有高 TMB 能力的癌症类型。基于这两个免疫因素的模型在交叉验证和测试中强烈预测了给定癌症类型的 TMB 能力(Spearman Rho 分别为 0.76 和 1)。使用该模型,我们预测了另外 9 种癌症类型的 TMB 能力,包括罕见癌症,这些癌症类型的 TMB 和免疫检查点抑制剂反应数据尚未公开。我们的分析表明,TMB-high 可能高度预测宫颈癌对免疫检查点抑制剂的反应,这表明应该优先进行此类研究。
这项研究揭示了可能调节高肿瘤突变负担与免疫检查点抑制剂反应之间关系的免疫相关因素,这有助于为临床试验确定优先级。