Mining immunotherapy clinical trials Clinical trial data can provide a wealth of information about how drugs work. Yet such information often belongs to pharmaceutical companies and is rarely accessible to the scientific community at large. Cristescu et al. provide exploratory analysis of a cancer genomics dataset, collected from four separate clinical trials of Merck's PD-1 immunotherapy drug, pembrolizumab. This informative public resource examines more than 300 patient samples representing 22 different tumor types. Two widely used signatures that currently predict immunotherapy response are tumor mutational burden and a “hot” T cell–inflamed microenvironment. The study analyzed these two proposed biomarkers in combination to see what predictive clinical utility they may hold. Science, this issue p. eaar3593 Genomic biomarkers will help to elucidate which cancer patients will benefit from PD-1 blockade immunotherapy. INTRODUCTION Immunotherapy targeting the programmed cell death protein–1 (PD-1) axis elicits durable antitumor responses in multiple cancer types. However, clinical responses vary, and biomarkers predictive of response may help to identify patients who will derive the greatest therapeutic benefit. Clinically validated biomarkers predictive of response to the anti–PD-1 monoclonal antibody pembrolizumab include PD-1 ligand 1 (PD-L1) expression in specific cancers and high microsatellite instability (MSI-H) regardless of tumor type. Tumor mutational burden (TMB) and T cell–inflamed gene expression profile (GEP) are emerging predictive biomarkers for pembrolizumab. Both PD-L1 and GEP are inflammatory biomarkers indicative of a T cell–inflamed tumor microenvironment (TME), whereas TMB and MSI-H are indirect measures of tumor antigenicity generated by somatic tumor mutations. However, the relationship between these two categories of biomarkers is not well characterized. RATIONALE This study assessed the potential for TMB and a T cell–inflamed GEP to jointly predict clinical response to pembrolizumab in >300 patient samples with advanced solid tumors and melanoma across 22 tumor types from four KEYNOTE clinical trials. To assess the individual and joint clinical utility of TMB and GEP, patients were stratified in four biomarker-defined clinical response groups [GEP low and TMB low (GEPlo TMBlo), GEP low and TMB high (GEPlo TMBhi), GEPhi TMBlo, and GEPhi TMBhi] based on predefined cutoffs for TMB and GEP. These patient-defined biomarker groups were further used to guide transcriptome and exome analyses of tumors in a large molecular database [The Cancer Genome Atlas (TCGA)] (n = 6384 tumors) to identify targetable patterns of biology that may modulate response and resistance. RESULTS TMB and GEP exhibited only modest correlation and were independently predictive of response across the KEYNOTE clinical datasets. We found that objective response rates were strongest in patients with GEPhi TMBhi (37 to 57%), moderate in those with GEPhi TMBlo (12 to 35%) and GEPlo TMBhi (11 to 42%), and reduced or absent in those with GEPlo TMBlo (0 to 9%) (see the figure). Additionally, longer progression-free survival times were seen in patients with higher levels of both TMB and GEP. Findings were comparable when TMB and PD-L1 expression were jointly assessed. Within TCGA database, GEP and TMB again had a low correlation, demonstrating the potential to jointly stratify transcriptomic and genomic features across cancer types. Specific gene expression patterns reflective of TME biology showed significant associations with TMB, GEP, or both. In particular, gene set enrichment analysis identified proliferative and stromal, myeloid, and vascular biology corresponding to specific TMB-defined subgroups within GEPhi tumors. In TMBhi tumors, indication-dependent somatic DNA alterations in key cancer driver genes showed a strong negative association with GEP. CONCLUSION This analysis shows that TMB and inflammatory biomarkers (T cell–inflamed GEP and PD-L1 expression) can jointly stratify human cancers into groups with different clinical responses to pembrolizumab monotherapy and identify patterns of underlying, targetable biology related to these groups. TMB and inflammatory biomarkers independently predict response and may capture distinct features of neoantigenicity and T cell activation, respectively. This approach may provide a precision medicine framework for rationally constructing and evaluating anti–PD-1– and/or –PD-L1–based combination therapy regimens. Biomarker-defined responses to pembrolizumab monotherapy identify targetable-resistance biology. (A) Tumors have low TMB and low neoantigenicity and lack a T cell–inflamed TME. (B) Tumors can evade the immune response despite high TMB and high neoantigenicity. (C) Although T cells are present, stromal and/or endothelial factors in the TME, low TMB, and low neoantigenicity impede their activity. (D) Tumors have high TMB, high neoantigenicity, and a T cell–inflamed TME, typified by activated T cells and other immune cells with cytolytic roles. Programmed cell death protein–1 (PD-1) and programmed cell death ligand–1 (PD-L1) checkpoint blockade immunotherapy elicits durable antitumor effects in multiple cancers, yet not all patients respond. We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials. Tumor mutational burden (TMB) and a T cell–inflamed gene expression profile (GEP) exhibited joint predictive utility in identifying responders and nonresponders to the PD-1 antibody pembrolizumab. TMB and GEP were independently predictive of response and demonstrated low correlation, suggesting that they capture distinct features of neoantigenicity and T cell activation. Analysis of The Cancer Genome Atlas database showed TMB and GEP to have a low correlation, and analysis by joint stratification revealed biomarker-defined patterns of targetable-resistance biology. These biomarkers may have utility in clinical trial design by guiding rational selection of anti–PD-1 monotherapy and combination immunotherapy regimens.

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基于 PD-1 检查点阻断免疫治疗的泛肿瘤基因组生物标志物

挖掘免疫治疗临床试验 临床试验数据可以提供关于药物如何发挥作用的丰富信息。然而，此类信息通常属于制药公司，一般科学界很少能获得这些信息。克里斯特斯库等人。提供对癌症基因组数据集的探索性分析，这些数据集是从默克公司的 PD-1 免疫治疗药物派姆单抗的四项独立临床试验中收集的。这个信息丰富的公共资源检查了代表 22 种不同肿瘤类型的 300 多个患者样本。目前预测免疫治疗反应的两个广泛使用的特征是肿瘤突变负荷和“热”T细胞发炎的微环境。该研究结合分析了这两种提议的生物标志物，以了解它们可能具有哪些预测性临床效用。科学，这个问题 p。eaar3593 基因组生物标志物将有助于阐明哪些癌症患者将受益于 PD-1 阻断免疫疗法。引言 针对程序性细胞死亡蛋白-1 (PD-1) 轴的免疫疗法可在多种癌症类型中引发持久的抗肿瘤反应。然而，临床反应各不相同，预测反应的生物标志物可能有助于识别将获得最大治疗益处的患者。经临床验证的预测抗 PD-1 单克隆抗体派姆单抗反应的生物标志物包括特定癌症中的 PD-1 配体 1 (PD-L1) 表达和高微卫星不稳定性 (MSI-H)，无论肿瘤类型如何。肿瘤突变负荷 (TMB) 和 T 细胞炎症基因表达谱 (GEP) 是派姆单抗的新兴预测生物标志物。PD-L1 和 GEP 都是指示 T 细胞发炎的肿瘤微环境 (TME) 的炎症生物标志物，而 TMB 和 MSI-H 是由体细胞肿瘤突变产生的肿瘤抗原性的间接测量。然而，这两类生物标志物之间的关系并未得到很好的表征。基本原理 本研究评估了 TMB 和 T 细胞炎症 GEP 在来自四项 KEYNOTE 临床试验的 22 种肿瘤类型的超过 300 名晚期实体瘤和黑色素瘤患者样本中联合预测对派姆单抗的临床反应的潜力。为了评估 TMB 和 GEP 的个体和联合临床效用，将患者分为四个生物标志物定义的临床反应组 [GEP 低和 TMB 低 (GEPlo TMBlo)、GEP 低和 TMB 高 (GEPlo TMBhi)、GEPhi TMBlo 和 GEPhi TMBhi] 基于 TMB 和 GEP 的预定义截止值。这些患者定义的生物标志物组进一步用于指导大型分子数据库 [癌症基因组图谱 (TCGA)]（n = 6384 个肿瘤）中肿瘤的转录组和外显子组分析，以确定可能调节反应和抵抗的可靶向生物学模式。结果 TMB 和 GEP 仅表现出适度的相关性，并且可以独立预测整个 KEYNOTE 临床数据集的反应。我们发现 GEPhi TMBhi 患者的客观缓解率最高（37% 至 57%），GEPhi TMBhi 患者（12% 至 35%）和 GEPlo TMBhi（11% 至 42%）的客观缓解率中等，而 GEPlo 患者的客观缓解率降低或不存在TMBlo（0 到 9%）（见图）。此外，在 TMB 和 GEP 水平较高的患者中观察到更长的无进展生存时间。当联合评估 TMB 和 PD-L1 表达时，结果具有可比性。在 TCGA 数据库中，GEP 和 TMB 再次具有低相关性，证明了联合对癌症类型的转录组学和基因组特征进行分层的潜力。反映 TME 生物学的特定基因表达模式显示与 TMB、GEP 或两者都有显着关联。特别是，基因集富集分析确定了与 GEPhi 肿瘤内特定 TMB 定义的亚组相对应的增殖和基质、骨髓和血管生物学。在 TMBhi 肿瘤中，关键癌症驱动基因中的适应症依赖性体细胞 DNA 改变与 GEP 呈强烈负相关。结论 该分析表明，TMB 和炎症生物标志物（T 细胞发炎的 GEP 和 PD-L1 表达）可以共同将人类癌症分为对派姆单抗单药治疗具有不同临床反应的组，并确定潜在的、与这些群体相关的可靶向生物学。TMB 和炎症生物标志物独立预测反应，并可能分别捕捉新抗原性和 T 细胞活化的不同特征。这种方法可以为合理构建和评估基于抗 PD-1 和/或 PD-L1 的联合治疗方案提供精准医学框架。对 pembrolizumab 单药治疗的生物标志物定义的反应确定了靶向抗性生物学。(A) 肿瘤具有低 TMB 和低新抗原性，并且缺乏 T 细胞发炎的 TME。(B) 尽管 TMB 和新抗原性高，但肿瘤可以逃避免疫反应。(C) 尽管存在 T 细胞，但 TME 中的基质和/或内皮因子、低 TMB 和低新抗原性阻碍了它们的活性。(D) 肿瘤具有高 TMB、高新抗原性和 T 细胞发炎的 TME，以活化的 T 细胞和其他具有溶细胞作用的免疫细胞为代表。程序性细胞死亡蛋白-1 (PD-1) 和程序性细胞死亡配体-1 (PD-L1) 检查点阻断免疫疗法在多种癌症中引起持久的抗肿瘤作用，但并非所有患者都有反应。我们报告了对来自四项 KEYNOTE 临床试验的 22 种肿瘤类型的 >300 名患者样本的评估。肿瘤突变负荷 (TMB) 和 T 细胞炎症基因表达谱 (GEP) 在识别对 PD-1 抗体派姆单抗的反应者和无反应者方面表现出联合预测效用。TMB 和 GEP 可独立预测反应并显示低相关性，表明它们捕获了新抗原性和 T 细胞活化的不同特征。对癌症基因组图谱数据库的分析显示 TMB 和 GEP 具有低相关性，联合分层分析揭示了生物标志物定义的靶向抗性生物学模式。这些生物标志物可能通过指导抗 PD-1 单药治疗和联合免疫治疗方案的合理选择在临床试验设计中发挥作用。