Immune response persisting melanoma cells: Mechanisms of immune escape

利用免疫系统的内在力量识别和摧毁肿瘤的治疗方法的最新进展使癌症治疗发生了革命性的变化。事实上，免疫检查点抑制剂（ICI）可能导致快速和持久的抗肿瘤反应。然而，许多接受ICI治疗的黑色素瘤患者不会体验到这些益处，因为持久的缓解率不超过60%。在ICI难治性患者中，适应性免疫反应的复杂性至少被一种甚至多种免疫逃逸机制所超越。这些可能包括功能失调的T细胞，调节性免疫和基质细胞，或肿瘤细胞特征。应该指出的是，这个列表并不详尽，没有一个因素是单独的，但它们是紧密交织在一起的。此外，免疫抗性表型不是静态的；相反，它不断地适应动态变化的免疫、内在和治疗环境，施加选择压力。当在FP1的三重转基因la - tg3 /PTENflox/Cre黑色素瘤模型中处理PTEN作为PI3K/ akt通路控制器的功能时，黑色素瘤与免疫系统之间的动态相互作用变得明显，这表明特定个体信号通路的一些“既定”功能代表了癌细胞中多种细胞效应与宿主（免疫）编辑机制的进化积分的终点。因此，为了消除这些障碍，我们计划采用一种综合方法，不仅在预测的小鼠模型中利用单细胞分析，而且在ICI开始前、开始后一周和进展时纵向收集临床样本。利用对肿瘤细胞遗传学和表观遗传学的集体理解，包括由此产生的功能和代谢组细胞状态，作为PhenoTImE的一部分，促进了这一点。反之亦然，我们在肿瘤和基质细胞相互作用和肿瘤（免疫）进化方面贡献我们的见解，以及我们在单细胞分析和空间蛋白质组学以及各自的生物信息学方面的技术专长。我们的总体假设是，黑色素瘤细胞状态的动态异质性和适应性免疫反应的可塑性代表了一个复杂的进化生态系统中两个稳定相互作用的方面。作为我们针对不同皮肤癌的长期努力的一部分，我们想要找出哪些分子信号驱动治疗上不利的T细胞终末分化/衰竭，以及如何维持有利的中枢记忆/干细胞样T细胞。预期的结果以及用于小鼠到人类推理的机器学习模型（反之亦然）将使我们能够增加我们项目的转化意义，最终产生克服黑色素瘤免疫抵抗的创新策略。

Recent advances in therapeutics harnessing the intrinsic power of the immune system to recognize and destroy tumors have revolutionized cancer treatment. Indeed, immune checkpoint inhibitors (ICI) may result in rapid and durable antitumor responses. However, numerous melanoma patients treated with ICI will not experience these benefits, as durable response rates do not exceed 60%. In ICI refractory patients, the complexity of adaptive immune responses is surpassed by at least one or even multiple immune escape mechanisms. These may include dysfunctional T cells, regulatory immune and stromal cells, or tumour cell characteristics. It should be noted, that this list is not exhaustive and none of the factors stands alone, but they are closely intertwined. Moreover, the immune-resistant phenotype is not static; rather, it is constantly adapting to dynamically changing immunological, intrinsic and therapeutic circumstances that exert selection pressure. When addressing PTEN function as a controller of the PI3K/AKT-pathway in the triple transgenic LLA-TG3/PTENflox/Cre melanoma model in FP1, the dynamic interactions between the melanoma and the immune system became obvious, illustrating that some ‘established’ functions of a given individual signalling pathway represent an endpoint resulting from an evolutional integral of diverse cellular effects in the cancer cell together with (immune) editing mechanism of the host. Thus, to remove the blinkers, we plan an integrative approach taking advantage of single cell analyses not only in the projected murine model, but also for longitudinally collected clinical samples obtained before, one week after initiation of ICI and at the time of progression. This is facilitated by being part of the PhenoTImE using the collective understanding of tumor cell genetics and epigenetics including the resulting functional and metabolomic cell states. Vice versa, we contribute our insights into tumor and stromal cell interaction and tumor (immune) evolution as well as our technical expertise in single cell analysis and spatial proteomics plus the respective bioinformatics. Our overall hypothesis is that the dynamic heterogeneity of melanoma cell states and the plasticity in adaptive immune responses represent two steadily interacting facets within one complex evolutionary ecosystem. As part of our long-term efforts across different skin cancers, we want to find out what molecular signals drive the therapeutically unfavourable terminal differentiation/exhaustion of T-cells and how advantageous central memory/stem cell-like T cells are maintained. The expected results together with machine learning models for mouse-to-human inference (and vice versa) will allow us to increase the translational significance of our project, ultimately resulting in innovative strategies to overcome immune resistance in melanoma.