In silico screening for immune surveillance adaptation in cancer using Common Fund data resources

总结/摘要 免疫疗法的进步最近彻底改变了癌症护理。癌症免疫治疗的关键策略是 针对免疫监视适应的“非细胞自主”机制，通过调节 癌细胞分泌的免疫调节剂。然而，对这些目标的计算机系统筛选， 由于缺乏计算工具，免疫调节剂（潜在的治疗药物）仍然未经测试 分析相关的大型数据库资源。本研究是根据RFA-RM-23-003提出的， 有意义地整合多个NIH共同基金和其他NIH资助的数据集，以告知分子基础 免疫监视适应和筛选潜在的免疫调节剂。我们的核心假设 通过深度学习捕获的癌症基因组特征可以预测癌细胞的非细胞自主信号 通过化合物治疗诱导以调节肿瘤微环境中的免疫细胞。我们建议测试 通过开发一个创新的和可行的计算框架，是建立在我们公布的假设 深度学习模型具体而言，在目标1.1中，我们提出鉴定与免疫缺陷相关的免疫细胞类型， 成人（癌症基因组图谱[TCGA]）和儿童肿瘤中的相关免疫基因特征 （加布里埃拉米勒儿童第一[儿童第一]和治疗适用的研究，以产生有效的 治疗[目标]）。然后，我们将构建一个深度学习模型来预测已识别的 使用整合的基因文库在癌细胞系中由化合物诱导的免疫学基因标记 基于网络的蜂窝签名（LINCS）数据。在目标1.2中，我们将使用以下方法对关键发现进行实验验证： 我们内部的体外模型。我们已经组建了一个跨学科的团队，拥有强大的互补专业知识， 有效地实现了提出的目标：干实验室博士Yu-Chiao Chiu（MPI）的癌症生物信息学，多模态 数据集成和人工智能;以及龚一楠博士（MPI）的癌症免疫学湿实验室， 免疫疗法和肿瘤细胞死亡机制。成功完成试点研究将产生高- 影响初步结果：i）第一个系统地结合多模态的深度学习框架 基因组和药物基因组学数据，以筛选免疫调节剂，ii）更深入地了解 免疫监视适应的分子基础比以前可能的，更重要的是iii）一套 有希望的目标初步验证在体外。这些初步数据将导致后续研究探索 我们的结果的功能和临床前方面。我们还希望拟议的研究提供一个计算 加强NIH共同基金数据和其他公共可用数据的利用和整合的框架 大型数据集。

Summary/Abstract Advances in immunotherapy have lately revolutionized cancer care. A key strategy of cancer immunotherapy is to target “non-cell-autonomous” mechanisms of immune surveillance adaptation, achieved via regulating the secretions of immune modulators from cancer cells. Yet, an in silico systematic screen for these targets and immunomodulating agents (potentially therapeutic drugs) remains untested due to a lack of computational tools to analyze relevant large-scale database resources. The study is proposed in response to RFA-RM-23-003 to meaningfully integrate multiple NIH Common Fund and other NIH-funded datasets to inform the molecular basis of immune surveillance adaptation and screen for potential immunomodulating agents. Our central hypothesis is that cancer genomic features captured by deep learning predict cancer cells’ non-cell autonomous signals induced by a compound treatment to modulate immune cells in the tumor microenvironment. We propose to test the hypothesis by developing an innovative and feasible computational framework that is built upon our published deep learning models. Specifically, in Aim 1.1 we propose to identify prognosis-related immune cell types and associated immunologic gene signatures among adult (The Cancer Genome Atlas [TCGA]) and pediatric tumors (Gabriella Miller Kids First [Kids First] and Therapeutically Applicable Research To Generate Effective Treatments [TARGET]). We will then build a deep learning model to predict the perturbation of the identified immunologic gene signatures induced by a compound in a cancer cell line using the Library of Integrated Network-based Cellular Signatures (LINCS) data. In Aim 1.2, we will experimentally validate key findings using our in-house in vitro models. We have formed a cross-disciplinary team with strong complementary expertise to efficiently achieve the proposed goals: dry lab of Dr. Yu-Chiao Chiu (MPI) for cancer bioinformatics, multi-modal data integration, and artificial intelligence; and wet lab of Dr. Yi-Nan Gong (MPI) for cancer immunology, immunotherapy, and tumor cell death mechanisms. Successful completion of the pilot study will produce high- impact preliminary results: i) the first deep learning framework that systematically incorporates multi-modal genomic and pharmacogenomic data to screen for immunomodulating agents, ii) a deeper understanding of the molecular basis of immune surveillance adaptation than was previously possible, and more importantly iii) a set of promising targets preliminarily validated in vitro. These preliminary data will lead to a follow-up study to explore functional and preclinical aspects of our results. We also expect the proposed study to provide a computational framework that enhances the utilization and integration of NIH Common Fund data and other publicly available large datasets.