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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

基本信息

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
项目类别：
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

项目摘要

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/文摘