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

使用共同基金数据资源对癌症免疫监测适应进行计算机筛选

• 批准号: 10773268
• 负责人: Yu-Chiao Chiu
• 金额: $ 31.8万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10773268
• 关键词: Adult; Affect; Antineoplastic Agents; Artificial Intelligence; Award; Bioinformatics; Biological; CD8-Positive T-Lymphocytes; Cancer cell line; Cell Death; Cell Line; Cell secretion; Cells; Child; Computer Analysis; Computer Models; Data; Data Set; Detection; Development; Dryness; Follow-Up Studies; Funding; Genetic; Genomics; Goals; Immune; Immune Evasion; Immune Targeting; Immune response; Immune system; Immunologic Surveillance; Immunologics; Immunology; Immunology procedure; Immunomodulators; Immunotherapy; In Vitro; Intelligence; Knowledge; Laboratories; Libraries; Macrophage; Malignant Neoplasms; Mediating; Modeling; Molecular; Molecular Profiling; Network-based; Pattern; Pediatric Neoplasm; Performance; Pharmaceutical Preparations; Pharmacogenomics; Pilot Projects; Production; Productivity; Prognosis; Publishing; Research; Resources; Signal Induction; Signal Transduction; Testing; The Cancer Genome Atlas; Therapeutic; Training; Tumor-infiltrating immune cells; United States National Institutes of Health; Validation; Work; cancer care; cancer cell; cancer genomics; cancer immunotherapy; cancer type; carcinogenesis; cell type; chemokine; computer framework; computerized tools; cytokine; data integration; data resource; deep learning; deep learning model; design; effective therapy; experience; follow-up; genetic signature; immune function; immune modulating agents; immunoregulation; improved; in silico; in vitro Assay; in vitro Model; innovation; insight; interest; large datasets; large-scale database; multimodal data; multimodality; neoplastic cell; novel; pharmacologic; pre-clinical; programs; response; screening; targeted agent; tumor immunology; tumor microenvironment

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.

摘要/摘要 免疫疗法的进展最近彻底改变了癌症护理。癌症免疫疗法的关键策略是 针对免疫监视适应的“非细胞自主”机制，通过调节实现 来自癌细胞免疫调节剂的分泌。然而，这些目标的硅系统屏幕和 由于缺乏计算工具，免疫调节剂（潜在的治疗药物）仍未测试 分析相关的大规模数据库资源。该研究是针对RFA-RM-23-003的响应提出的 有意义地整合了多个NIH共同基金和其他NIH资助的数据集，以告知分子基础 免疫监视适应和潜在免疫调节剂的筛查。我们的中心假设 是由深度学习捕获的癌症基因组特征预测癌细胞的非细胞自主信号 通过复合处理诱导，以调节肿瘤微环境中的免疫细胞。我们建议测试 通过开发一种创新且可行的计算框架来建立的假设 深度学习模型。特别是，在AIM 1.1中，我们建议确定与预后相关的免疫细胞类型，并且 成年人（癌症基因组[TCGA]）和小儿肿瘤的相关免疫基因特征 （Gabriella Miller Kids First [Kids First]和治疗上适用的研究以产生有效 治疗[目标]）。然后，我们将建立一个深度学习模型，以预测已确定的 使用综合的库中的化合物诱导的化合物诱导的免疫基因特征 基于网络的蜂窝签名（LINCS）数据。在AIM 1.2中，我们将使用实验来验证关键发现 我们的内部体外模型。我们组成了一个具有强大完善专业知识的跨学科团队 有效地实现拟议的目标：Yu-Chiao Chiu博士（MPI）的干燥实验室，用于癌症生物信息学，多模式 数据集成和人工智能； Yi-Nan Gong博士（MPI）的湿实验室，用于癌症免疫学， 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）系统合并多模式的第一个深度学习框架 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 免疫监视适应性的分子基础比以前可能发生的，更重要的是iii） 有希望的目标在体外初步验证。这些初步数据将导致一项后续研究以探索 结果的功能和临床前方面。我们还期望拟议的研究提供计算 增强NIH共同基金数据和其他公开可用的利用率和集成的框架 大数据集。