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

项目1摘要：细胞因子对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，对细胞因子信号活性的研究仍然具有挑战性。为了应对这些挑战，我们开发了细胞因子信号分析器(CytoSig，https://cytosig.ccr.cancer.gov)，)，它既提供了一个受细胞因子调控的靶基因数据库，也提供了一个从转录谱中预测细胞因子信号级联的模型。我们收集了20,591个人类细胞因子、趋化因子和生长因子反应的转录组图谱。这一由细胞因子诱导的转录模式图谱使我们能够使用批量和单细胞数据可靠地预测传染病、慢性炎症和癌症中不同免疫细胞群中的信号活动。CytoSig揭示了许多以前未知的细胞因子的作用，如作为抗炎因子的Bmp6，并确定了人类炎症性疾病的候选治疗靶点，如针对严重新冠肺炎的CXCL8。项目2总结：尽管最近在癌症免疫治疗方面取得了突破，T细胞治疗在实体瘤中的疗效有限。由于目前筛查平台的局限性，确定T细胞功能障碍的调节因子仍然具有挑战性。使用肿瘤的单细胞转录数据作为输入，我们开发了一个计算模型来识别T细胞弹性的调节因素，T细胞弹性的定义是T细胞在免疫抑制信号(如转化生长因子-β和TRAIL)下增殖的能力。综合来自七种肿瘤类型的14个单细胞转录队列，我们确定FIBP和TMEM222是T细胞弹性的最高调节因子。在小鼠和人类供体T细胞中敲除这些基因，特别是FIBP，显著提高了T细胞介导的癌症杀伤和过继细胞治疗的效果。我们发现，CD8淋巴细胞中的FIBP基因敲除通过限制胆固醇代谢来缓解T细胞功能障碍，并且已知高胆固醇水平会抑制T细胞的活性。总之，我们的T细胞弹性模型显示FIBP是加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.