De León Rodríguez Saraí G, Hernández Herrera Paúl, Aguilar Flores Cristina, Pérez Koldenkova Vadim, Guerrero Adán, Mantilla Alejandra, Fuentes-Pananá Ezequiel M, Wood Christopher, Bonifaz Laura C
UMAE Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Unidad de Investigación Médica en Inmunoquímica, Mexico City, Mexico.
Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Mexico City, Mexico.
J Oncol. 2022 Oct 12;2022:9775736. doi: 10.1155/2022/9775736. eCollection 2022.
Melanoma is the deadliest form of skin cancer. Due to its high mutation rates, melanoma is a convenient model to study antitumor immune responses. Dendritic cells (DCs) play a key role in activating cytotoxic CD8 T lymphocytes and directing them to kill tumor cells. Although there is evidence that DCs infiltrate melanomas, information about the profile of these cells, their activity states, and potential antitumor function remains unclear, particularly for conventional DCs type 1 (cDC1). Approaches to profiling tumor-infiltrating DCs are hindered by their diversity and the high number of signals that can affect their state of activation. Multiplexed immunofluorescence (mIF) allows the simultaneous analysis of multiple markers, but image-based analysis is time-consuming and often inconsistent among analysts. In this work, we evaluated several machine learning (ML) algorithms and established a workflow of nine-parameter image analysis that allowed us to study cDC1s in a reproducible and accessible manner. Using this workflow, we compared melanoma samples between disease-free and metastatic patients at diagnosis. We observed that cDC1s are more abundant in the tumor infiltrate of the former. Furthermore, cDC1s in disease-free patients exhibit an expression profile more congruent with an activator function: CD40PD-L1 CD86IL-12. Although disease-free patients were also enriched with CD40PD-L1 cDC1s, these cells were also more compatible with an activator phenotype. The opposite was true for metastatic patients at diagnosis who were enriched for cDC1s with a more tolerogenic phenotype (CD40PD-L1CD86IL-12IDO). ML-based workflows like the one developed here can be used to analyze complex phenotypes of other immune cells and can be brought to laboratories with standard expertise and computer capacity.
黑色素瘤是最致命的皮肤癌形式。由于其高突变率，黑色素瘤是研究抗肿瘤免疫反应的便捷模型。树突状细胞（DCs）在激活细胞毒性CD8 T淋巴细胞并引导它们杀死肿瘤细胞方面发挥着关键作用。尽管有证据表明DCs浸润黑色素瘤，但关于这些细胞的特征、它们的活性状态以及潜在的抗肿瘤功能的信息仍不清楚，特别是对于1型传统DCs（cDC1）。分析肿瘤浸润DCs的方法受到其多样性以及可能影响其激活状态的大量信号的阻碍。多重免疫荧光（mIF）允许同时分析多个标志物，但基于图像的分析耗时且分析人员之间往往不一致。在这项工作中，我们评估了几种机器学习（ML）算法，并建立了一个九参数图像分析工作流程，使我们能够以可重复且易于操作的方式研究cDC1s。使用这个工作流程，我们比较了诊断时无病患者和转移性患者的黑色素瘤样本。我们观察到，cDC1s在前一类患者的肿瘤浸润中更为丰富。此外，无病患者中的cDC1s表现出与激活功能更一致的表达谱：CD40PD-L1 CD86IL-12。尽管无病患者中也富含CD40PD-L1 cDC1s，但这些细胞也更符合激活表型。诊断时的转移性患者情况则相反，他们富含具有更强耐受性表型（CD40PD-L1CD86IL-12IDO）的cDC1s。像这里开发的基于ML的工作流程可用于分析其他免疫细胞的复杂表型，并且可以引入具有标准专业知识和计算机能力的实验室。
