Predicting response to anti-PD-1 therapy in triple negative breast cancer by comprehensive profiling of the tumor microenvironment

摘要 三阴性乳腺癌（TNBC）是乳腺癌中最具侵袭性的亚型， 每年影响超过37 000名妇女。以前的研究表明， TNBC的肿瘤微环境中的免疫细胞影响总体存活。这 促使临床试验测试免疫调节药物治疗这种疾病。 尽管免疫疗法在一系列肿瘤类型中取得了成功，但只有一种免疫疗法是有效的。 一部分患者经历了显著的益处。识别生物标志物以预测哪些患者 对这些药物的反应是极具挑战性的。基于排序的方法 需要在分析之前将组织解离，因此不捕获空间位置。 不同细胞类型之间的关系。成像方法确实捕捉到了这些空间 这些蛋白质可以与其他蛋白质相互作用，但一次只能看到少量的蛋白质。这导致 肿瘤微环境的复杂性的不完整的图片，因为不是所有的细胞类型可以 马上被确认。我们的团队最近开发了多路复用离子束成像， 使得可以可视化的抗体数量增加近10倍 同步我们的假设是，通过将这种新的成像方式与DNA和 RNA测序，我们将能够全面描绘肿瘤微环境， TNBC患者，从而显著提高反应的预测。在目标1中，我将改进 我们实验室使用计算工具来识别组织中相邻细胞之间的边界， 以便将成像信号准确地分配给正确的细胞。在目标2中，我将使用我们实验室的小说 一个成像平台，用于分析来自参与靶向PD-1的临床试验的患者的样本，这是一个关键 免疫调节蛋白然后，我将使用这些丰富的信息来预测患者对 疗法在目标3中，我将整合来自相同样品的测序数据和成像数据 来确定基因改变如何影响免疫细胞的组成 存在于肿瘤微环境中。这项工作将增加我们对 TNBC中的免疫相互作用，并将产生显着改进的模型来预测 对免疫疗法的反应。

Abstract Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and affects more than 37,000 women each year. Previous work has shown that the presence of immune cells in the tumor microenvironment of TNBC influences overall survival. This has prompted clinical trials testing immunomodulatory drugs for the treatment of this disease. Although immunotherapy has demonstrated success across a range of tumor types, only a subset of patients experience significant benefit. Identifying biomarkers to predict which patients will respond to these drugs has been extremely challenging. Sequencing based approaches require the tissue to be dissociated prior to analysis, and hence do not capture the spatial relationships between different cell types. Imaging methods do capture these spatial relationships, but can only visualize a small number of proteins at a time. This results in an incomplete picture of the complexity of the tumor microenvironment, since not all cell types can be identified at once. Our group has recently developed Multiplexed Ion Beam Imaging, which allows for a nearly 10-fold increase in the number of antibodies that can be visualized simultaneously. Our hypothesis is that by combining this novel imaging modality with DNA and RNA sequencing, we will be able to comprehensively profile the tumor microenvironment of TNBC patients, and thus significantly improve prediction of response. In Aim 1, I will improve the computational tools our lab uses to identify the boundaries between adjacent cells in tissue, in order to accurately assign imaging signal to the correct cell. In Aim 2, I will use our lab’s novel imaging platform to profile samples from patients enrolled in a clinical trial targeting PD-1, a key immune regulatory protein. I will then use this rich information to predict patient response to therapy. In Aim 3 I will integrate sequencing data from the same samples with the imaging data we generated to determine how genetic alterations influence the composition of immune cells present in the tumor microenvironment. This work will increase our understanding of the immune interactions in TNBC, and will generate significantly improved models to predict response to immunotherapy.