A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma.

PURPOSE: Adults with T-cell lymphoblastic lymphoma (T-LBL) generally benefit from treatment with acute lymphoblastic leukemia (ALL)-like regimens, but approximately 40% will relapse after such treatment. We evaluated the value of CpG methylation in predicting relapse for adults with T-LBL treated with ALL-like regimens. EXPERIMENTAL DESIGN: A total of 549 adults with T-LBL from 27 medical centers were included in the analysis. Using the Illumina Methylation 850K Beadchip, 44 relapse-related CpGs were identified from 49 T-LBL samples by two algorithms: least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE). We built a four-CpG classifier using LASSO Cox regression based on association between the methylation level of CpGs and relapse-free survival in the training cohort (n = 160). The four-CpG classifier was validated in the internal testing cohort (n = 68) and independent validation cohort (n = 321). RESULTS: The four-CpG-based classifier discriminated patients with T-LBL at high risk of relapse in the training cohort from those at low risk (P < 0.001). This classifier also showed good predictive value in the internal testing cohort (P < 0.001) and the independent validation cohort (P < 0.001). A nomogram incorporating five independent prognostic factors including the CpG-based classifier, lactate dehydrogenase levels, Eastern Cooperative Oncology Group performance status, central nervous system involvement, and NOTCH1/FBXW7 status showed a significantly higher predictive accuracy than each single variable. Stratification into different subgroups by the nomogram helped identify the subset of patients who most benefited from more intensive chemotherapy and/or sequential hematopoietic stem cell transplantation. CONCLUSIONS: Our four-CpG-based classifier could predict disease relapse in patients with T-LBL, and could be used to guide treatment decision.

A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study.

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.

Prognostic and predictive value of a microRNA signature in adults with T-cell lymphoblastic lymphoma.

New prognostic factors are needed to establish indications for haematopoietic stem cell transplantation (HSCT) in first complete remission (CR1) for T-cell lymphoblastic lymphoma (T-LBL) patients. We used microarray to compare T-LBL tissue samples (n = 75) and fetal thymus tissues (n = 20), and identified 35 differentially expressed miRNAs. Using 107 subjects as the training group, we developed a five-miRNA-based classifier to predict patient survival with LASSO Cox regression: lower risk was associated with better prognosis (disease-free survival (DFS): hazard ratio (HR) 4.548, 95% CI 2.433-8.499, p < 0.001; overall survival (OS): HR 5.030, 95% CI 2.407-10.513, p < 0.001). This classifier displayed good performance in the internal testing set (n = 106) and the independent external set (n = 304). High risk was associated with more favorable response to HSCT (DFS: HR 1.675, 95% CI 1.127-2.488, p = 0.011; OS: HR 1.602, 95% CI 1.055-2.433, p = 0.027). When combined with ECOG-PS and/or NOTCH1/FBXW7 status, this classifier had even better prognostic performance in patients receiving HSCT (DFS: HR 2.088, 95% CI 1.290-3.379, p = 0.003; OS: HR 1.996, 95% CI 1.203-3.311, p = 0.007). The five-miRNA classifier may be a useful prognostic biomarker for T-LBL adults, and could identify subjects who could benefit from HSCT.