Data-driven inference of regulators for cytokine-mediated tumor killing

肿瘤微环境中的细胞毒性淋巴细胞可通过抗原特异性杀伤和细胞因子介导作用杀死癌细胞。相同的抗癌细胞因子，如干扰素- γ或TNF - α，可能对同一肿瘤中不同的癌细胞群和不同的细胞类型具有不同的抑制作用。我将开发计算模型来测量肿瘤生态系统中各种细胞因子对细胞增殖和代谢活动的抑制作用。基于细胞因子抑制模型，我将评估不同的统计方法，以选择介导细胞因子介导的肿瘤杀伤效果的特征。该项目的成果将是潜在的靶基因或途径，其抑制可以通过调节肿瘤细胞因子的作用来增强癌症免疫治疗的有效性。最重要的预测将在小鼠模型中得到实验验证。

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.