State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
Leukemia. 2020 Sep;34(9):2392-2404. doi: 10.1038/s41375-020-0757-5. Epub 2020 Feb 20.
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.
我们旨在建立一个基于基因表达的判别分类器，以预测 T 细胞淋巴母细胞淋巴瘤（T-LBL）患者的生存结局。在探索进展性（n=22）与无进展性（n=28）T-LBL 患者的全基因表达谱后，我们发现了 43 个差异表达的 mRNA。然后，我们使用基于 NanoString 定量的 LASSO Cox 回归建立了一个包含 11 个基因的分类器。在训练队列（n=169）中，使用该分类器分层的高危患者的无进展生存期（PFS：危险比 4.123，95%CI 2.565-6.628；p<0.001）、无疾病生存期（DFS：HR 3.148，95%CI 1.857-5.339；p<0.001）和总生存期（OS：HR 3.790，95%CI 2.237-6.423；p<0.001）显著低于低危患者。该分类器的预后准确性在内部测试（n=84）和独立验证队列（n=360）中得到了验证。一个包含五个独立变量的预后列线图，包括分类器、乳酸脱氢酶水平、ECOG-PS、中枢神经系统受累和 NOTCH1/FBXW7 状态，与每个单一变量相比，具有显著更高的预后准确性。加入一个基于五个 miRNA 的特征进一步提高了该列线图的准确性。此外，列线图评分≥154.2 的患者从 BFM 方案中显著获益。总之，我们的列线图包括基于 11 个基因的分类器，可能有助于个体预后预测和治疗决策。
