Data-driven inference of regulators for cytokine-mediated tumor killing
Data-driven inference of regulators for cytokine-mediated tumor killing
批准号:
10262524
负责人:
Peng Jiang
金额:
$40.52万
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:
至
关键词:
AntigensCell ProliferationComputer ModelsDataEcosystemEffectivenessGenesInterferon Type IILymphocyteMeasuresMediatingMetabolicModelingPathway interactionsPopulationStatistical ModelsTNF geneanti-cancerbasecancer cellcancer immunotherapycell typecytokinecytotoxicdata integrationmouse modeltumortumor microenvironment
中文摘要
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英文摘要
Cytotoxic lymphocytes in the tumor microenvironment can kill cancer cells through both antigen-specific killing and cytokine-mediated effects. The same anticancer cytokine, such as interferon-gamma or TNF alpha, may have different inhibitive effects on distinct cancer cell populations and diverse cell types in the same tumor. I will develop computational models to measure the inhibitive effect of various cytokines in the tumor ecosystem on cell proliferation and metabolic activities. Based on the cytokine-inhibitive model, I will evaluate different statistical approaches to select features that mediate the efficacy of cytokine-mediated tumor killing. The deliverable of this project will be potential target genes or pathways whose inhibition can enhance the effectiveness of cancer immunotherapies through modulating the cytokine effects in tumors. Top predictions will be experimentally validated in murine models.
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