A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study
A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study
复制标题
基于基因表达的特征可预测 T 细胞淋巴母细胞淋巴瘤成人患者的生存：一项多中心研究
DOI：
10.1038/s41375-020-0757-5
复制
发表时间：
2020-02-20
期刊：
影响因子：
11.4
通讯作者：
Cai, Qing-Qing
中科院分区：
文献类型：
--
作者：
Tian, Xiao-Peng;Xie, Dan;Cai, Qing-Qing
我们的目的是建立一个基于基因表达的鉴别分类器来预测t细胞淋巴母细胞淋巴瘤（T-LBL）患者的生存结果。在研究进展性（n = 22）和无进展性（n = 28） T-LBL患者的整体基因表达谱后，鉴定出43种差异表达的mrna。在此基础上，采用基于NanoString量化的LASSO Cox回归建立了基于11个基因的分类器。在训练队列（n = 169）中，使用分类器分层的高危患者与低危患者相比，无进展生存（PFS：风险比4.123,95% CI 2.565-6.628, p < 0.001）、无病生存（DFS: HR 3.148, 95% CI 1.857-5.339, p < 0.001）和总生存（OS: HR 3.790, 95% CI 2.237-6.423, p < 0.001）显著降低。在内部测试（n = 84）和独立验证队列（n = 360）中验证了分类器的预后准确性。由分类器、乳酸脱氢酶水平、ECOG-PS、中枢神经系统受损伤和NOTCH1/FBXW7状态等5个自变量组成的预后nomogram显示出比单独使用单个变量更高的预后准确性。添加一个基于5 - mirna的特征进一步提高了该nomogram的准确性。此外，nomogram评分>= 154.2的患者显著受益于BFM方案。总之，我们的nomogram包含了基于11个基因的分类器，可能有助于个体预后预测和治疗决策。
We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565-6.628; p < 0.001), disease-free survival (DFS: HR 3.148, 95% CI 1.857-5.339; p < 0.001), and overall survival (OS: HR 3.790, 95% CI 2.237-6.423; p < 0.001) compared with low-risk patients. The prognostic accuracy of the classifier was validated in the internal testing (n = 84) and independent validation cohorts (n = 360). A prognostic nomogram consisting of five independent variables including the classifier, lactate dehydrogenase levels, ECOG-PS, central nervous system involvement, and NOTCH1/FBXW7 status showed significantly greater prognostic accuracy than each single variable alone. The addition of a five-miRNA-based signature further enhanced the accuracy of this nomogram. Furthermore, patients with a nomogram score >= 154.2 significantly benefited from the BFM protocol. In conclusion, our nomogram comprising the 11-gene-based classifier may make contributions to individual prognosis prediction and treatment decision-making.