Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Immune Monitoring Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Cell Rep Methods. 2023 Aug 2;3(8):100546. doi: 10.1016/j.crmeth.2023.100546. eCollection 2023 Aug 28.
We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a T cell exhaustion topic that independently recapitulates the previously identified LAG-3 immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a T cell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity.
我们提出了 TopicFlow，这是一个用于分析患者血液样本中流式细胞术数据的计算框架，用于识别循环 T 细胞群体中功能和动态主题。该框架应用了潜在狄利克雷分配（LDA）模型，将文本挖掘中的主题建模概念应用于流式细胞术。为了展示我们方法的实用性，我们对 51 名接受免疫检查点抑制剂（ICI）治疗的黑色素瘤患者的 138 个外周血样本中收集的约 1700 万个 T 细胞进行了分析。我们的研究突出了 LDA 识别的三个潜在动态主题：一个 T 细胞耗竭主题，它独立地再现了先前确定的与 ICI 耐药相关的 LAG-3 免疫表型，一个幼稚主题及其与免疫相关毒性的关联，以及一个在 ICI 治疗后出现的 T 细胞激活主题。我们的方法可以广泛应用于挖掘高参数流式细胞术数据，以深入了解治疗反应和毒性的机制。
