Cancer immunotherapy

癌症免疫治疗

• 批准号: 10486985
• 负责人: Eytan Ruppin
• 金额: $ 75.83万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10486985
• 关键词: Binding; CAR T cell therapy; Data; Effectiveness; Gene Expression Profiling; Genes; Immune checkpoint inhibitor; Immune response; Immune system; Immunotherapy; Machine Learning; Malignant Neoplasms; Metastatic Melanoma; NF1 gene; Neuroblastoma; Outcome; Patients; Pharmaceutical Preparations; Reporting; Role; Shapes; Translations; Tumor Suppressor Proteins; anti-cancer; base; cancer immunotherapy; cancer type; checkpoint therapy; genome-wide analysis; immune checkpoint blockade; melanoma; new therapeutic target; novel; patient response; predicting response; predictive test; predictive tools; response; targeted biomarker; targeted treatment; tumor heterogeneity; urea cycle

In the last two years we have become actively engaged in collaborative studies in cancer immunotherapy, ranging from studying the role of urea cycle dysregulation in modulating the response to checkpoint inhibitors in different cancer types, studying the role of intratumor heterogeneity in shaping the immune response and its effectiveness, and very recently, building machine learning based predictors of patients' response to checkpoint therapies in melanoma. We also found CAPN1 is a novel binding partner and regulator of the tumor suppressor Neurofibromin 1 (NF1) in melanoma. We also found robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Analyzing gene expression data from patients with neuroblastoma, we identified 15 checkpoint-related pairs of genes whose relative activity could be used to predict the effectiveness of the immune system's anticancer response, which we used to develop a predictive tool. When we applied this predictor to available data from patients with melanoma, it outperformed all other predictors of immunotherapy drug outcomes that had been reported so far.

在过去的两年里，我们积极参与癌症免疫治疗的合作研究，包括研究尿素循环失调在调节不同癌症类型对检查点抑制剂反应中的作用，研究肿瘤内异质性在塑造免疫反应中的作用，以及研究肿瘤内异质性在形成免疫反应中的作用。它的有效性，以及最近建立的基于机器学习的患者对黑色素瘤检查点疗法反应的预测因子。我们还发现 CAPN1 是黑色素瘤中肿瘤抑制因子神经纤维蛋白 1 (NF1) 的新型结合伴侣和调节因子。我们还发现了对转移性黑色素瘤免疫检查点阻断疗法反应的可靠预测。通过分析神经母细胞瘤患者的基因表达数据，我们确定了 15 个与检查点相关的基因对，它们的相对活性可用于预测免疫系统抗癌反应的有效性，我们用它来开发预测工具。当我们将该预测因子应用于黑色素瘤患者的可用数据时，它的表现优于迄今为止报道的所有其他免疫治疗药物结果的预测因子。