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

预测 NSCLC 中 PD-1 抑制剂反应的模型

• 批准号: 9260334
• 负责人: EDWARD B GARON
• 金额: $ 63.91万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-03 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9260334
• 关键词: Accounting; Address; Adverse event; Age; Apoptosis; Bioinformatics; Biological Markers; Biology; Biopsy; CD8-Positive T-Lymphocytes; Cancer Etiology; Cancer Patient; Cessation of life; Characteristics; Clinical; Clinical Data; Clinical Trials; Community Networks; Complex; DNA analysis; Data; Data Set; Diagnosis; Disease; Disease Progression; Drug Approval; Epidermal Growth Factor Receptor; Evaluation; Future; Gene Expression; Genes; Histology; Immune; Immune checkpoint inhibitor; Immunology; Individual; Infiltration; KRAS2 gene; Laboratories; Ligands; Light; Malignant neoplasm of lung; MicroRNAs; Modeling; Mutation; Nature; Non-Small-Cell Lung Carcinoma; Oncologist; Outcome; PDCD1LG1 gene; Pathology; Patient Care; Patients; Performance Status; Pharmaceutical Preparations; Pharmacologic Substance; Population; Predictive Factor; Prior Therapy; Property; RNA analysis; Recording of previous events; Research; Resistance; Sampling; Smoking; Specimen; Stable Disease; Staining method; Stains; Statistical Data Interpretation; T-Lymphocyte Subsets; Toxic effect; Training; United States; actionable mutation; advanced disease; base; biomarker development; cancer biomarkers; cancer survival; chemotherapy; cigarette smoking; clinical practice; companion diagnostics; design; diagnostic assay; disability; docetaxel; effective therapy; exome sequencing; experience; immune checkpoint; immunogenic; inhibitor/antagonist; neoplastic cell; objective response rate; outcome prediction; partial response; potential biomarker; predicting response; predictive marker; predictive modeling; response; targeted agent; tumor

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

项目摘要 肺癌是美国（US）和世界上癌症相关死亡的主要原因，占全球癌症死亡人数的60%。 仅在美国，每年就有超过150，000人死亡。近年来，对非小细胞肺生物学的了解 非小细胞肺癌（NSCLC）的发病率有所上升。尽管许多患者使用针对特定驱动因素的药物进行治疗， 由于他们的肿瘤中存在突变，大多数患者无法获得此类药物，最终出现耐药性。 针对程序性细胞死亡-1（PD-1）免疫检查点的药物最近显示， 伟大的承诺。虽然与约20%的客观缓解率（ORR）相关， 对于转移性非小细胞肺癌患者，缓解的质量和持续时间可能会很深刻，特别是在一个领域 即使是最有效的治疗，六个月后也习惯了疾病的进展 一个实质性的争论是基于生物标志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NSCLC患者的研究结果感到惊讶，该研究表明， ORR和PD-L1表达。在训练集中，我们发现至少一半的肿瘤细胞中PD-L1染色， 预测ORR更高。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染色的患者中，相比之下，在那些较少或 分别不染色。无进展生存期和总生存期的结果相似。 进一步的证据已经产生了其他潜在的生物标志物。我们与 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 缓解或稳定疾病持续至少6个月）。我们还看到了与结果和病史的相关性， 当前或既往吸烟，活检前CD 4+和CD 8 + T细胞以及某些基因的表达， miRNAs。然而，没有一个单一的因素预测结果的精度水平，将是理想的临床实践。 此外，尽管每个因素与临床结果相关，但不同因素与 彼此特别强烈。 迄今为止，我们已经储存了100多名接受PD-1抑制剂治疗的患者的样本。鉴于 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， 我们与TRIO-US网络合作，我们将迅速储存更多的高质量标本， 与临床数据相关。有了这些标本，我们计划能够创建模型， 预测哪些患者将从PD-1抑制剂中获益。该项目的具体目标是： 1.定义临床特征以及预测肿瘤和免疫微环境的性质 训练集中对单药PD-1抑制的应答 2.创建模型以确定NSCLC患者从PD-1抑制中获益的可能性 3.将从训练集样本生成的模型映射到独立的样本集中