Comprehensive profiling of the tumor microenvironment to predict patient response to immunotherapy

全面分析肿瘤微环境以预测患者对免疫治疗的反应

• 批准号: 10478139
• 负责人: NOAH GREENWALD
• 金额: $ 3.92万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10478139
• 关键词: 3-Dimensional; Algorithms; Antibodies; Architecture; Binding; Biological Markers; Breast Cancer Patient; Cells; Clinical; Clinical Trials; Clinical Trials Design; Combination Drug Therapy; Complex; Computer Analysis; Cytotoxic T-Lymphocytes; DNA sequencing; Data; Disease; Ensure; Experimental Models; Fellowship; Genetic; Genetic Transcription; Goals; Image; Immune; Immune response; Immune system; Immunohistochemistry; Immunotherapy; Infiltration; Label; Lead; Light; Link; Location; Machine Learning; Malignant Neoplasms; Mass Spectrum Analysis; Mentorship; Metals; Methods; Modeling; Multiplexed Ion Beam Imaging; Mutation; Myeloid Cells; Neoplasm Metastasis; Organoids; Outcome; PD-1/PD-L1; Patient-Focused Outcomes; Patients; Pattern; Phase; Phenotype; Play; Population; Principal Investigator; Process; Proteins; Research Project Grants; Resistance; Role; Sampling; Shapes; Stromal Cells; T-Lymphocyte; TP53 gene; Technology; Testing; Time; Training; Tumor-infiltrating immune cells; Work; aggressive breast cancer; anti-PD-1; anti-PD-1/PD-L1; anti-PD-L1 therapy; anti-PD1 therapy; cancer cell; cancer genetics; cancer subtypes; cancer therapy; cell type; chemotherapy; clinical care; driver mutation; drug development; exhaust; exome sequencing; improved; insight; macrophage; malignant breast neoplasm; multiplexed imaging; neoantigens; novel; novel therapeutic intervention; patient prognosis; patient response; patient stratification; predicting response; predictive marker; prognostic; programmed cell death protein 1; response; response biomarker; single cell sequencing; skills; therapeutic development; tool; transcriptome sequencing; translational potential; triple-negative invasive breast carcinoma; tumor; tumor microenvironment; tumor progression

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and there are few available treatment options for patients with this disease. Recently, immunotherapy has shown promise in a subset of TNBC patients. However, identifying which patients will benefit from immunotherapy is currently extremely challenging. In order to understand why certain patients respond to immunotherapy and others do not, we need to develop a better understanding of the tumor microenvironment (TME). The TME is made up of cancer cells, as well as the immune and stromal cells which surround them. Previous work has demonstrated that changes in the relative abundance of certain cell types in the TME can predict whether patients will respond to treatment. However, we have lacked the tools to develop a comprehensive understanding of the patterns of interaction between the different cells in the TME. For the F99 phase of this proposal, I will combine multiplexed imaging with exome sequencing to comprehensively profile the TME in TNBC patients. I will analyze 100 TNBC patient samples from a clinical trial testing the anti-PD-1 immunotherapy. I will first link genetic alterations to changes in the localization of cells in the TME, to increase our understanding of the relationship between cancer genetics and host cell infiltration. I will then use these relationships to generate biomarkers of response to immunotherapy. For the K00 phase of this proposal, I will develop an organoid model of the TNBC TME. Using this organoid model, I will determine how the absence of myeloid cells alters the phenotype of the organoid. I will then use single-cell sequencing to identify the transcriptional changes that myeloid cells undergo following treatment with anti-PD-1. This fellowship will provide me with the necessary training in both computational analysis and experimental methods to lead my own group studying the interaction between the immune system and cancer.

三阴性乳腺癌（TNBC）是乳腺癌中最具侵袭性的亚型， 并且对于患有这种疾病的患者几乎没有可用的治疗选择。最近， 免疫疗法在TNBC患者亚组中显示出前景。然而，确定哪些 患者将受益于免疫疗法目前是极具挑战性的。为了 了解为什么某些患者对免疫疗法有反应，而另一些则没有，我们需要 更好地了解肿瘤微环境（TME）。TME由以下人员组成 癌细胞以及它们周围的免疫细胞和基质细胞。先前的工作已经 表明TME中某些细胞类型相对丰度的变化可以 预测患者是否对治疗有反应。然而，我们缺乏工具， 全面了解不同组织之间的相互作用模式 TME中的细胞。对于本提案的F99阶段，我将结合联合收割机多路复用成像与 外显子组测序以全面分析TNBC患者中的TME。我会分析100个 来自测试抗PD-1免疫疗法的临床试验的TNBC患者样品。我将首先链接 基因改变改变TME中细胞定位的变化，以增加我们的 了解癌症遗传学与宿主细胞浸润之间的关系。然后我将 使用这些关系来产生对免疫疗法的反应的生物标志物。对于K 00 在本提案的第一阶段，我将开发TNBC TME的类器官模型。利用这个类器官 模型，我将确定如何骨髓细胞的缺乏改变了类器官的表型。 然后，我将使用单细胞测序来鉴定髓样细胞 接受抗PD-1治疗。这个奖学金将为我提供必要的 在计算分析和实验方法方面的培训，以领导我自己的团队 研究免疫系统和癌症之间的相互作用。