A composite single-nucleotide polymorphism prediction signature for extranodal natural killer/T-cell lymphoma.

Current prognostic scoring systems based on clinicopathologic variables are inadequate in predicting the survival and treatment response of extranodal natural killer/T-cell lymphoma (ENKTL) patients undergoing nonanthracyline-based treatment. We aimed to construct a classifier based on single-nucleotide polymorphisms (SNPs) for improving predictive accuracy and guiding clinical decision making. Data from 722 patients with ENKTL from international centers were analyzed. A 7-SNP-based classifier was constructed using LASSO Cox regression in the training cohort (n = 336) and further validated in the internal testing cohort (n = 144) and in 2 external validation cohorts (n = 142 and n = 100). The 7-SNP-based classifier showed good prognostic predictive efficacy in the training cohort and the 3 validation cohorts. Patients with high- and low-risk scores calculated by the classifier exhibited significantly different progression-free survival (PFS) and overall survival (OS) (all P < .001). The 7-SNP-based classifier was further proved to be an independent prognostic factor by multivariate analysis, and its predictive accuracy was significantly better than clinicopathological risk variables. Application of the 7-SNP-based classifier was not affected by sample types. Notably, chemotherapy combined with radiotherapy significantly improved PFS and OS vs radiotherapy alone in high-risk Ann Arbor stage I patients, whereas there was no statistical difference between the 2 therapeutic modalities among low-risk patients. A nomogram was constructed comprising the classifier and clinicopathological variables; it showed remarkably better predictive accuracy than either variable alone. The 7-SNP-based classifier is a complement to existing risk-stratification systems in ENKTL, which could have significant implications for clinical decision making for patients with ENKTL.

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.

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.

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.

MicroRNA-506 is up-regulated in the development of pancreatic ductal adenocarcinoma and is associated with attenuated disease progression.

BACKGROUND: MicroRNA-506 (miR-506) has been reported to function in several tumors as a tumor suppressor gene or oncogene. However, the expression and role of miR-506 in pancreatic ductal adenocarcinoma (PDAC) remains unclear. In this study, we aimed to evaluate the phenotype of miR-506 in PDAC. METHODS: Using miRNA in situ hybridization, we examined the expression of miR-506 in 113 PDACs and 87 paired normal pancreatic tissues. We evaluated miR-506 expression in PDAC cells, normal pancreatic ducts, and acinus/islands, and we analyzed the associations between miR-506 expression and the clinicopathologic characteristics of PDAC patients. RESULTS: miR-506 expression was higher in PDAC than in matched normal pancreatic ductal cells (P < 0.001). On the other hand, the combined group of well and moderately differentiated PDACs showed higher levels of miR-506 than the poorly differentiated ones (P = 0.023). Moreover, miR-506 expression was negatively associated with pathologic T category (P = 0.004) and lymph node metastasis (P = 0.033), suggesting that miR-506 might inhibit the progression of PDAC. CONCLUSIONS: Our results suggest that miR-506 either plays a role as an oncogene in the tumorigenesis and a tumor suppressor in the progression or serves as a house-keeping, tumor-suppressing miRNA, whose expression can be activated by oncogenic signals in early development to hinder the progression of PDAC.