Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium.
Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium.
Methods Cell Biol. 2024;183:115-142. doi: 10.1016/bs.mcb.2023.05.001. Epub 2023 Sep 23.
The highly diverse T cell receptor (TCR) repertoire is a crucial component of the adaptive immune system that aids in the protection against a wide variety of pathogens. This TCR repertoire, comprising the collection of all TCRs in an individual, is a valuable source of information on both recent and ongoing T cell activation. Cancer cells, like pathogens, have the ability to trigger an adaptive immune response. However, because cancer cells use a variety of strategies to escape immune responses, this is often insufficient to completely eradicate them. As a result, immunotherapy is a promising treatment option for cancer patients. This treatment is expected to increase T cell activation and subsequently alter the TCR repertoire composition in these patients. Monitoring TCR repertoires before and after immunotherapy can therefore provide additional insight into T cell responses and might identify cancer-associated TCR sequences. Here we present a computational strategy to identify those changes in the TCR repertoire that occur after treatment with immunotherapy. Since this method allows the identification of TCR patterns that might be treatment-associated, it can help future research by revealing those patterns that are related with response. This TCR analysis workflow is illustrated using public data from three different cancer patients who received anti-PD-1 treatment.
高度多样化的 T 细胞受体 (TCR) 库是适应性免疫系统的重要组成部分，有助于抵御各种病原体。该 TCR 库由个体中所有 TCR 的集合组成，是近期和正在进行的 T 细胞激活的有价值信息来源。与病原体一样，癌细胞也有能力引发适应性免疫反应。然而，由于癌细胞使用多种策略来逃避免疫反应，这通常不足以将其完全清除。因此，免疫疗法是癌症患者的一种有前途的治疗选择。这种治疗预计会增加 T 细胞的激活，随后改变这些患者的 TCR 库组成。因此，在免疫治疗前后监测 TCR 库可以提供对 T 细胞反应的额外了解，并可能识别与癌症相关的 TCR 序列。在这里，我们提出了一种计算策略来识别免疫治疗后 TCR 库发生的变化。由于该方法允许鉴定可能与治疗相关的 TCR 模式，因此可以通过揭示与反应相关的模式来帮助未来的研究。该 TCR 分析工作流程使用来自接受抗 PD-1 治疗的三名不同癌症患者的公共数据进行了说明。
