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

三阴性乳腺癌(TNBC)是乳腺癌中最具侵袭性的亚型， 对于患有这种疾病的患者来说，可用的治疗选择很少。最近， 免疫疗法在部分TNBC患者中显示出良好的前景。然而，确定哪些是 患者将从免疫疗法中受益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其他患者不起作用，我们需要 更好地了解肿瘤微环境(TME)。TME由以下部分组成 癌细胞，以及它们周围的免疫和基质细胞。以前的工作有 证明了TME中某些细胞类型的相对丰度的变化可以 预测患者对治疗是否有反应。然而，我们一直缺乏工具来 全面了解不同组织之间的交互模式 TME中的细胞。对于本提案的F99阶段，我将把多路传输成像与 外显子组测序以全面描述TNBC患者的TME。我会分析100 TNBC患者样本来自一项临床试验，测试抗PD-1免疫疗法。我会先链接 对TME中细胞定位变化的遗传改变，以增加我们的 了解癌症遗传学与宿主细胞侵袭的关系。那我会的 利用这些关系来生成免疫治疗反应的生物标记物。对于K00 在这个提案阶段，我将开发一个TNBC TME的有机模型。使用这种有机化合物 在模型中，我将确定髓系细胞的缺失如何改变器官的表型。 然后我将使用单细胞测序来鉴定髓系细胞的转录变化 接受抗PD-1的后续治疗。这笔奖学金将为我提供必要的 接受计算分析和实验方法方面的培训，领导我自己的团队 研究免疫系统和癌症之间的相互作用。

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.