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 免疫疗法的进步最近彻底改变了癌症治疗。癌症免疫疗法的一个关键策略是。 针对免疫监视适应的“非细胞自主”机制，通过调节 然而，对这些目标的计算机系统筛选和免疫调节剂的分泌。 由于缺乏计算工具，免疫调节剂（潜在的治疗药物）仍未经过测试 分析相关大型数据库资源。本研究是响应 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，为分子基础提供信息 免疫监视适应和潜在免疫调节剂的筛选我们的中心假设。 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 我们建议进行测试，以调节肿瘤微环境中的免疫细胞。 该假设是通过创新且可行的计算框架开发的，该框架建立在我们已发表的 具体来说，在目标 1.1 中，我们建议识别与预后相关的免疫细胞类型和 成人（癌症基因组图谱 [TCGA]）和儿童肿瘤中相关的免疫基因特征 （加布里埃拉·米勒“孩子优先”[Kids First] 和治疗性应用研究，以产生有效的 然后，我们将构建一个深度学习模型来预测已识别的扰动。 使用集成库分析癌细胞系中化合物诱导的免疫基因特征 基于网络的蜂窝签名 (LINCS) 数据在目标 1.2 中，我们将使用实验验证关键发现。 我们的内部体外模型已组建了一支具有强大互补专业知识的跨学科团队。 有效实现拟议目标：Yu-Chiao Chiu 博士 (MPI) 癌症生物信息学、多模态干实验室 数据集成和人工智能；以及龚一南博士 (MPI) 的癌症免疫学湿实验室， 成功完成试点研究将产生高水平的免疫治疗和肿瘤细胞死亡机制。 影响初步结果：i）第一个系统地整合多模态的深度学习框架 基因组和药物基因组数据来筛选免疫调节剂，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) 一组 这些初步数据将导致后续研究的探索。 我们还期望拟议的研究能够提供计算结果。 加强 NIH 共同基金数据和其他公开数据的利用和整合的框架 大型数据集。