Modeling and targeting tumor-immune signaling interactions in tumor microenvironment

项目摘要 肿瘤微环境(TME)中的肿瘤间质/免疫细胞信号传递起着重要作用 在肿瘤发展中的作用以及对靶向和免疫治疗的反应。然而，我们所知道的 TME内复杂的信号传递及其在肿瘤发展、药物和 免疫治疗反应有限。有效的分子靶点仍然缺乏，可以抑制肿瘤- 基质信令通信。单细胞rna测序(scrna-seq)已经成为一种强有力的方法。 捕捉TME内单个肿瘤、间质和免疫细胞转录变化的技术。而当 人类癌症的scRNA-seq数据集的数量正在迅速增长，这导致了许多基本和 随着翻译研究的不断深入，对动态肿瘤间质信号传递的研究还很有限。限制 因素包括：1)TME内复杂交互的静态和单时间点快照；2) 难以干扰大量相关的信号目标；并测量相应的功能 在小鼠或肿瘤组织中对这些扰动的影响(以确定新的治疗靶点和治疗方法)。 为了解决这些挑战，在这项研究中，我们建议结合尖端技术，包括 新的人工智能(AI)模型，scRNA-seq，基于CRISPR的单或双敲除(CDKO)，3D 肿瘤-CAF-共培养实验和遗传小鼠模型，以系统生物学的方式进行。具体而言，(在 目标1)，我们将利用PDAC人类有价值的大量scRNA-seq数据开发新的网络人工智能模型 WASH U肿瘤识别静态核心肿瘤-CAF-相互作用(TCTI)信号网络(多细胞内 /TCTi的细胞间信号网络)；以及一组初步的抗TCTi靶标。在目标2中，我们将进一步 开发另一个网络人工智能模型(M-STEP)，以使用功能验证来推断更好的抗TCTI目标 目标3中的反馈；并预测协同药物组合(抑制多个关键的抗TCTI靶点)。在AIM 3、利用可扩展的3D肿瘤-CAF-共培养技术对预测的靶点和药物进行有效的评估 分析和基于CRISPR的基因敲除(E-STEP)，具有3个可测量的指标，即肿瘤增殖、迁移、 血管生成。M-STEP(建模)和E-STEP(验证)形成E-M过程，以识别关键的反 TCTI反复瞄准目标和药物。我们将把这些人工智能模型应用于胰腺导管腺癌 (PDAC)因为1)免疫疗法的反应非常有限；2)没有有效的治疗方法；3) 几乎所有患者在确诊后2年内都会出现化疗耐药和转移肿瘤。也是 (可行性)，4)我们有一个强大的跨学科团队研究PDAC TME(由NCI孢子支持 和人类肿瘤图谱网络(Htan)，拥有宝贵的最先进的资源。这件事的成功 研究将确定新的抗肿瘤靶点--CAF和用于PDAC治疗的药物鸡尾酒。人工智能模型， 支持新颖的电-机械系统生物学，可应用于其他癌症和疾病。

Project Summary Tumor-stroma/immune cell signaling communications within the tumor microenvironment (TME) play important roles in tumor development and responses to targeted and immunotherapies. However, our knowledge of complex signaling communications within TME, and their roles in tumor development, drug and immunotherapy response is limited. Effective molecular targets are still missing that can inhibit the tumor- stroma signaling communications. Single cell RNA sequencing (scRNA-seq) has been being a powerful technology to capture transcriptional changes in individual tumor, stroma, immune cells within TME. While scRNA-seq datasets of human cancer are rapidly growing in number, which is leading to many basic and translational discoveries, the study of dynamic tumor-stroma signaling communications is limited. Limiting factors include: 1) static and single time-point snapshots of the complex interactions within the TME, and 2) difficulty in perturbing a large number of related signaling targets; and measuring corresponding functional effects to these perturbations in mouse or tumor tissues (to identify novel therapeutic targets and treatments). To resolve these challenges, in this study, we propose to combine the cutting-edge technologies, including novel artificial intelligence (AI) models, scRNA-seq, crispr-based single or double knockouts (CDKOs), 3D tumor-CAF-TAM co-culture assays, and genetic mouse models, in a systems biology manner. Specifically, (in Aim 1), we will develop novel network AI models using valuable large sets of scRNA-seq data of PDAC human tumors at WashU to identify static core tumor-CAF-TAM interaction (TCTi) signaling networks (multi-cell intra- /inter-cellular signaling networks of TCTi); and an initial set of anti-TCTi targets. In Aim 2, we will further develop another network AI model (M-Step) to infer the better anti-TCTi targets using the functional validation feedbacks in Aim 3; and predict synergistic drug combinations (inhibiting multiple key anti-TCTi targets). In Aim 3, the predicted targets and drugs will be efficiently evaluated using scalable 3D Tumor-CAF-TAM co-culture assays and crispr-based knockouts (E-step) with 3 measurable metrics, i.e., tumor proliferation, migration, angiogenesis. The M-step (modeling) and E-step (validation) forms an E-M process to identify key anti- TCTi targets and drugs iteratively. We will apply these AI models in Pancreatic ductal adenocarcinoma (PDAC) because 1) there have been very limited responses to immunotherapy; 2) no effective treatment; 3) nearly all patients will develop chemo-resistant and metastatic tumors within 2 years of diagnosis. Also (feasibility), 4) we have a strong cross-disciplinary team studying the PDAC TME (supported by NCI SPORE and human tumor atlas network (HTAN)), with the valuable state-of-the-art resources. The success of this study will identify novel anti-tumor-TAM-CAF targets and drug cocktails for PDAC treatment. The AI models, supporting the novel E-M systems biology, can be applied to other cancers and diseases.