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

项目1总结：细胞因子对人类细胞间通讯至关重要 健康和疾病，但细胞因子信号传导活性的研究仍然存在 由于细胞因子的短半衰期和细胞因子的复杂性/冗余性， 功能协调发展的为了应对这些挑战，我们开发了细胞因子信号分析仪（CytoSig， https://cytosig.ccr.cancer.gov），提供了通过以下途径调节的靶基因的数据库： 细胞因子和来自转录组学谱的细胞因子信号级联的预测模型。 我们收集了20，591份人类细胞因子、趋化因子和生长因子的转录组图谱 应答这种由细胞因子诱导的转录模式图谱使可靠的 预测感染性疾病中不同免疫细胞群体中的信号传导活性， 慢性炎症和癌症使用大量和单细胞数据。CytoSig此前透露， 许多细胞因子的未鉴定作用，如作为抗炎因子的BMP 6，和 在人类炎症性疾病中确定的候选治疗靶点，例如用于治疗炎症性疾病的CXCL 8， 严重的COVID-19项目2摘要：尽管最近在癌症免疫治疗方面取得了突破， T细胞疗法在实体瘤中的疗效有限。识别T细胞中的调节因子 由于当前筛选平台的限制，功能障碍仍然具有挑战性。使用 作为输入，我们开发了一个计算模型， 确定T细胞弹性的调节因子，定义为T细胞增殖的能力 在免疫抑制信号如TGF-β和TRAIL的作用下。集成14个单电池 从七种肿瘤类型的转录组学队列中，我们将FIB和TMEM 222确定为最高 调节T细胞恢复力的基因敲除这些基因，特别是FIB，在小鼠和 人供体T细胞显著增强了T细胞介导的癌症杀伤的功效， 过继细胞疗法我们发现，在CD 8淋巴细胞中敲除FIB可以使T细胞增殖， 胆固醇代谢功能障碍，已知高胆固醇水平 抑制T细胞活性。总之，我们的T细胞弹性模型揭示了FIB作为候选者 靶向增强癌症免疫治疗。

Project 1 Summary: Cytokines are critical for intercellular communication in human health and disease, but the investigation of cytokine signaling activity has remained challenging due to the short half-lives of cytokines and the complexity/redundancy of cytokine functions. To address these challenges, we developed the Cytokine Signaling Analyzer (CytoSig, https://cytosig.ccr.cancer.gov), providing both a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine, and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct immune cell populations in infectious diseases, chronic inflammation, and cancer using bulk and single-cell data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor, and identified candidate therapeutic targets in human inflammatory diseases, such as CXCL8 for severe COVID-19. Project 2 Summary: Despite recent breakthroughs in cancer immunotherapy, T-cell therapies achieve limited efficacy in solid tumors. Identifying regulators in T-cell dysfunction remains challenging due to limitations of current screening platforms. Using single-cell transcriptomic data from tumors as input, we developed a computational model to identify regulators of T-cell resilience, defined as the ability of T cells to proliferate under immune-suppressive signals such as TGF-beta and TRAIL. Integrating 14 single-cell transcriptomic cohorts from seven tumor types, we identified FIBP and TMEM222 as top regulators of T-cell resilience. Knocking out these genes, especially FIBP, in murine and human donor T cells significantly enhanced the efficacy of T-cell mediated cancer killing and adoptive cell therapy. We show that FIBP knockout in CD8 lymphocytes alleviates T-cell dysfunction by limiting cholesterol metabolisms, and high cholesterol level is known to inhibit T-cell activity. Together, our T-cell resilience model revealed FIBP as a candidate target to potentiate cancer immunotherapy.