Background: Tumor response to therapy is affected by both the cell types and the cell states present in the tumor microenvironment. This is true for many cancer treatments, including notably immune checkpoint inhibitors (ICIs). While it is well-established that ICIs promote T cell activation, their broader impact on other intratumoral immune cells is unclear; this information is needed to identify new mechanisms of action and improve ICI efficacy. Many preclinical studies have begun to use single cell analysis to delineate therapeutic responses in individual immune cell types within tumors. One major limitation to this approach is that therapeutic mechanisms identified in preclinical models have failed to fully translate to human disease, restraining efforts to improve ICI efficacy in bench to bedside research. Method: We previously developed a computational transfer learning approach to identify shared biology between independent high-throughput single-cell RNA sequencing (scRNA-seq) datasets. In the present study, we test this framework's ability to identify conserved and clinically relevant transcriptional changes in complex tumor scRNA-seq data and further expand its application beyond comparison of scRNA-seq datasets into comparison of scRNA-seq datasets with additional data types such as bulk RNA-seq and mass cytometry. Results: We found a conserved signature of NK cell activation in anti-CTLA-4 responsive mice and human tumors. In human melanoma, we found that the NK cell activation signature correlates with longer overall survival and is predictive of anti-CTLA-4 (ipilimumab) response. Additional molecular approaches to confirm the computational findings demonstrated that human NK cells express CTLA-4 and bind anti-CTLA-4 independent of the antibody binding receptor (FcR), and that similar to T cells, CTLA-4 expression by NK cells is modified by cytokine-mediated and target cell-mediated NK cell activation. Conclusions: These data demonstrate the ability of our transfer learning approach to identify cell state transitions conserved in preclinical models and human tumors. This approach can be adapted to explore many immuno-oncology questions, enhancing bench to bedside research and enabling better understanding and treatment of disease.

中文翻译：

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临床前模型与人类肿瘤之间的转移学习可确定抗CTLA-4反应性肿瘤中保守的NK细胞活化特征

背景：肿瘤对治疗的反应受肿瘤微环境中存在的细胞类型和细胞状态的影响。对于许多癌症治疗都是如此，尤其是免疫检查点抑制剂（ICI）。众所周知，ICI可以促进T细胞活化，但对其他肿瘤内免疫细胞的广泛影响尚不清楚。需要这些信息来确定新的作用机制并提高ICI效力。许多临床前研究已开始使用单细胞分析来描述肿瘤内单个免疫细胞类型的治疗反应。这种方法的主要局限性在于，临床前模型中确定的治疗机制未能完全转化为人类疾病，从而限制了从台式到床旁研究的提高ICI功效的努力。方法：我们之前开发了一种计算转移学习方法，以识别独立的高通量单细胞RNA测序（scRNA-seq）数据集之间的共享生物学。在本研究中，我们测试了该框架在复杂肿瘤scRNA-seq数据中识别保守的和临床相关的转录变化的能力，并进一步将其应用范围扩大到将scRNA-seq数据集与其他数据类型进行比较（超出对scRNA-seq数据集的比较）。大量RNA-seq和大规模流式细胞仪。结果：我们在抗CTLA-4反应小鼠和人类肿瘤中发现了NK细胞活化的保守标记。在人类黑素瘤中，我们发现NK细胞活化信号与更长的总生存期相关，并预测抗CTLA-4（ipilimumab）反应。证实计算结果的其他分子方法表明，人NK细胞表达CTLA-4并结合独立于抗体结合受体（FcR）的抗CTLA-4，并且类似于T细胞，NK细胞表达CTLA-4的过程被修饰通过细胞因子介导的和靶细胞介导的NK细胞活化。结论：这些数据证明了我们的转移学习方法能够识别临床前模型和人类肿瘤中保守的细胞状态转变的能力。该方法可适用于探索许多免疫肿瘤学问题，增强从床头到床的研究，并能更好地理解和治疗疾病。NK细胞的CTLA-4表达被细胞因子介导的和靶细胞介导的NK细胞激活修饰。结论：这些数据证明了我们的转移学习方法能够识别临床前模型和人类肿瘤中保守的细胞状态转变的能力。该方法可适用于探索许多免疫肿瘤学问题，增强从床头到床的研究，并能更好地理解和治疗疾病。NK细胞的CTLA-4表达被细胞因子介导的和靶细胞介导的NK细胞激活修饰。结论：这些数据证明了我们的转移学习方法能够识别临床前模型和人类肿瘤中保守的细胞状态转变的能力。该方法可适用于探索许多免疫肿瘤学问题，增强从床头到床的研究，并能更好地理解和治疗疾病。