A model for predicting response to PD-1 inhibitors in NSCLC

项目总结 肺癌是美国和世界上与癌症相关的死亡的主要原因 仅在美国每年就有超过15万人死亡。近年来，对非小细胞肺生物学的认识 癌症(非小细胞肺癌)有所增加。尽管许多患者使用针对特定司机的药物进行治疗 在他们的肿瘤中发生突变，这种药物对大多数患者来说是不可用的，最终会出现耐药性。 针对程序性细胞死亡-1(PD-1)免疫检查点的药物最近显示 很好的承诺。尽管与未选择的约20%的客观应答率(ORR)相关 转移性非小细胞肺癌患者的反应质量和持续时间可能是深刻的，特别是在一个领域 习惯了六个月后疾病的发展，即使是最有效的治疗 一场实质性的辩论是基于生物标记物的预测性质来选择患者进行治疗。 许多人对我领导的一项对495名非小细胞肺癌患者的研究结果感到惊讶，该研究表明 ORR和PD-L1的表达。在训练集中，我们发现至少一半的肿瘤细胞中有PD-L1的染色 预测了更大的ORR。当我们在独立患者中验证我们的结果时，ORR为45.2% 在那些至少一半的肿瘤细胞中有染色的人中，相比之下，在那些较少或更少的肿瘤细胞中，这一比例分别为16.5%和10.7% 分别缺失染色。在无进展和总存活率方面也看到了类似的结果。 在寻找其他潜在的生物标志物时，已经产生了进一步的证据。我们与之合作 其他研究表明，非同义突变的数量与持久的临床益处相关(部分 病情缓解或病情稳定至少6个月)。我们也看到了与结果和历史的相关性 现在或以前吸烟，活检前CD4+和CD8+T细胞以及某些基因和 MiRNAs。然而，没有一个单一的因素可以预测结果的精确度，这将是临床实践的理想水平。 此外，尽管每个因素与临床结果相关，但不同的因素与临床结果无关 彼此之间的关系尤其强烈。 到目前为止，我们已经收集了100多名使用PD-1抑制剂治疗的患者的样本。根据…… 最近的药物批准，与我们的附属卫星办公室和社区肿瘤学家网络合作 我们合作的三美网络，我们将迅速保存更多的高质量样本，这些样本 与临床数据相关联。有了这些样本，我们计划能够创建能够有效地 预测哪些患者将受益于PD-1抑制剂。该项目的具体目标是： 1.明确预测肿瘤和免疫微环境的临床特征和特性 训练集中对单剂PD-1抑制的反应 2.建立模型以确定PD-1抑制在非小细胞肺癌中受益的可能性 3.验证从独立样本集中的训练集样本生成的模型

PROJECT SUMMARY Lung cancer is the leading cause of cancer related deaths in the United States (US) and the world, accounting for over 150,000 deaths per year in the US alone. Recently, understanding of the biology of non-small cell lung cancer (NSCLC) has increased. Although many patients are treated with agents targeting specific driver mutations in their tumor, such agents are unavailable for most patients, and resistance eventually emerges. Agents directed against the programmed cell death-1 (PD-1) immune checkpoint have recently shown great promise. Although associated with an objective response rate (ORR) of about 20% in unselected metastatic NSCLC patients, the quality and duration of responses can be profound, particularly in a field accustomed to progression of disease after six months with even the most effective therapies A substantial debate is based on the predictive nature of biomarkers to select patients for therapy. Many were surprised by the results of a study of 495 NSCLC patients I led suggesting an association between ORR and PD-L1 expression. In a training set, we found that staining for PD-L1 in at least half of the tumor cells predicted a greater ORR. When we looked to validate our results in independent patients, the ORR was 45.2% in those with staining in at least half of their tumor cells compared to 16.5% and 10.7% in those with lesser or absent staining respectively. Similar results were seen for progression free and overall survival. Further evidence has been generated looking at other potential biomarkers. We collaborated with others to show that the number of non-synonymous mutations correlated with durable clinical benefit (partial response or stable disease lasting at least 6 months). We also saw correlations with outcome and a history of current or prior cigarette smoking, pre-biopsy CD4+ and CD8+ T cells and expression of certain genes and miRNAs. Yet, no single factor predicts outcome at the level of precision that would be ideal for clinical practice. Further, despite the correlation of each factor with clinical-outcome, the different factors don't correlate with one another particularly strongly. We have banked specimens from well over 100 patients treated with a PD-1 inhibitor to date. In light of recent drug approvals, working with our affiliated satellite offices and a network of community oncologists with whom we collaborate, the TRIO-US network, we will rapidly bank additional high quality specimens that are associated with clinical data. With these specimens, we plan to be able to create models that can effectively predict which patients stand to benefit from PD-1 inhibitors. The specific aims of this project are: 1. Define the clinical characteristics and the properties of the tumor and immune microenvironment that predict response to single agent PD-1 inhibition in a training set 2. Create models to identify the likelihood of benefit from PD-1 inhibition in NSCLC 3. Validate the models generated from the training set samples in an independent set of samples