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

项目1摘要：细胞因子对于人类健康和疾病中的细胞间通讯至关重要，但由于细胞因子的半衰期短和细胞因子功能的复杂性/冗余性，细胞因子信号传导活性的研究仍然具有挑战性。为了解决这些挑战，我们开发了细胞因子信号分析仪（CytoSig，https://cytosig.ccr.cancer.gov），提供了由细胞因子调节的靶基因的数据库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应答的转录组图谱。这种由细胞因子诱导的转录模式图谱使得能够使用大量和单细胞数据可靠地预测感染性疾病，慢性炎症和癌症中不同免疫细胞群体中的信号传导活性。CytoSig揭示了许多细胞因子（如作为抗炎因子的BMP 6）的先前未鉴定的作用，并确定了人类炎症性疾病中的候选治疗靶点，如严重COVID-19的CXCL 8。项目2总结：尽管最近在癌症免疫治疗方面取得了突破，但T细胞疗法在实体瘤中的疗效有限。由于目前筛选平台的局限性，确定T细胞功能障碍中的调节因子仍然具有挑战性。使用来自肿瘤的单细胞转录组数据作为输入，我们开发了一个计算模型来识别T细胞弹性的调节因子，T细胞弹性被定义为T细胞在免疫抑制信号（如TGF-β和TRAIL）下增殖的能力。整合来自7种肿瘤类型的14个单细胞转录组学队列，我们将FIB和TMEM 222确定为T细胞弹性的最高调节因子。在鼠和人供体T细胞中敲除这些基因，特别是FIB，显著增强了T细胞介导的癌症杀伤和过继细胞疗法的功效。我们发现，在CD 8淋巴细胞中的FIB敲除通过限制胆固醇代谢来加重T细胞功能障碍，并且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作为增强癌症免疫治疗的候选靶点。

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.