Song Jiwei, Huang FeiMing, Chen Lei, Feng KaiYan, Jian Fangfang, Huang Tao, Cai Yu-Dong
College of Life Science, Changchun Sci-Tech University, Shuangyang, China.
School of Life Sciences, Shanghai University, Shanghai, China.
Front Oncol. 2022 Aug 11;12:976262. doi: 10.3389/fonc.2022.976262. eCollection 2022.
CD19-targeted CAR T cell immunotherapy has exceptional efficacy for the treatment of B-cell malignancies. B-cell acute lymphocytic leukemia and non-Hodgkin's lymphoma are two common B-cell malignancies with high recurrence rate and are refractory to cure. Although CAR T-cell immunotherapy overcomes the limitations of conventional treatments for such malignancies, failure of treatment and tumor recurrence remain common. In this study, we searched for important methylation signatures to differentiate CAR-transduced and untransduced T cells from patients with acute lymphoblastic leukemia and non-Hodgkin's lymphoma. First, we used three feature ranking methods, namely, Monte Carlo feature selection, light gradient boosting machine, and least absolute shrinkage and selection operator, to rank all methylation features in order of their importance. Then, the incremental feature selection method was adopted to construct efficient classifiers and filter the optimal feature subsets. Some important methylated genes, namely, , , , , and , were identified. Furthermore, the classification rules for distinguishing different classes were established, which can precisely describe the role of methylation features in the classification. Overall, we applied advanced machine learning approaches to the high-throughput data, investigating the mechanism of CAR T cells to establish the theoretical foundation for modifying CAR T cells.
靶向CD19的嵌合抗原受体(CAR)T细胞免疫疗法在治疗B细胞恶性肿瘤方面具有卓越疗效。B细胞急性淋巴细胞白血病和非霍奇金淋巴瘤是两种常见的B细胞恶性肿瘤,复发率高且难以治愈。尽管CAR T细胞免疫疗法克服了此类恶性肿瘤传统治疗方法的局限性,但治疗失败和肿瘤复发仍然很常见。在本研究中,我们寻找重要的甲基化特征,以区分急性淋巴细胞白血病和非霍奇金淋巴瘤患者中经CAR转导和未转导的T细胞。首先,我们使用三种特征排序方法,即蒙特卡罗特征选择、轻梯度提升机和最小绝对收缩和选择算子,按重要性顺序对所有甲基化特征进行排序。然后,采用增量特征选择方法构建高效分类器并筛选出最优特征子集。确定了一些重要的甲基化基因,即 、 、 、 、 和 。此外,建立了区分不同类别的分类规则,能够精确描述甲基化特征在分类中的作用。总体而言,我们将先进的机器学习方法应用于高通量数据,研究CAR T细胞的作用机制,为改造CAR T细胞奠定理论基础。