CAR-T(CAR-T)细胞疗法在非肿瘤性疾病中的应用
Chimeric antigen receptor T (CAR-T) cell therapy in non-oncological diseases.
CAR-T(CAR-T)细胞在血液系统恶性肿瘤中的应用推动了这种免疫治疗形式的显著进展。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Identification of methylation signatures associated with CAR T cell in B-cell acute lymphoblastic leukemia and non-hodgkin's lymphoma.
Identification of methylation signatures associated with CAR T cell in B-cell acute lymphoblastic leukemia and non-hodgkin's lymphoma.
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CD19靶向的CAR-T 细胞免疫治疗对B细胞恶性肿瘤具有卓越疗效。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 细胞免疫治疗克服了此类恶性肿瘤传统治疗的局限性,但治疗失败和肿瘤复发仍然常见。
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CAR转导与未转导的T细胞。首先,我们使用三种特征排序方法,即蒙特卡洛特征选择、轻量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最优特征子集。鉴定出一些重要的甲基化基因,即SERPINB6、ANK1、PDCD5、DAPK2和DNAJB6。
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 细胞的机制,为改造CAR-T 细胞奠定理论基础。
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, SERPINB6 , ANK1 , PDCD5 , DAPK2 , and DNAJB6 , 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.
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