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.

Translational Relevance

In this retrospective, multicenter study, we developed a four-CpG–based classifier to predict the relapse risk of adult T-cell lymphoblastic lymphoma, and used an accuracy test to systematically assess the reproducibility and accuracy of the classifier. We further developed and validated a nomogram comprised of the four-CpG-based classifier, LDH level, ECOG-PS, CNS involvement, and NOTCH1/FBXW7 status that predicts relapse-free survival. We show that the nomogram is more accurate than the CpG-based classifier and/or clinicopathologic risk factors and could identify the subset of patients who are most likely to benefit from a certain therapeutic schedule. The four-CpG–based classifier provides a practical and reliable predictor for adults with T-LBL, and directly quantifies CpG methylation level from paraffin-embedded tissues based on pyrosequencing, making it easy to implement in clinical practice. The nomogram comprised of our CpG-based classifier may enable physicians to make more informed treatment decisions about therapeutic schedule for their patients.

T-cell lymphoblastic lymphoma (T-LBL) is a rare non-Hodgkin lymphoma and generally occurs in adolescents and young adults (1). Although classified as T-cell acute lymphoblastic leukemia (T-ALL) in the current WHO classification, T-LBL varies significantly in genetic profile, clinical presentations, subgroup stratification, and prognosis (2, 3). Unlike pediatric T-LBL, adults with T-LBL have a lower tolerance of intensive chemotherapy regimens, and, therefore, have lower complete remission (CR) rates and shorter survival (4). Intensive ALL-like regimens, such as the Berlin–Frankfurt–Muenster (BFM) protocol and the fractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone (hyper-CVAD) regimen, yield a CR rate of approximately 90% for T-LBL adults; however, relapse remains high after CR (40%–60%; refs. 5–7). Therefore, accurate assessment of high relapse risk after CR is important in providing useful insight into which subset of patients may require and benefit from more intensive chemotherapy and/or sequential hematopoietic stem cell transplantation (HSCT).

To date, only a few prognostic factors have been recognized in adult T-LBL (8–10). The minimal residual disease (MRD) and 18F-fluorodeoxyglucose PET scan also could not predict risk of relapse in patients with T-LBL treated with intensive therapy (11). Molecular factors such as NOTCH1/FBXW7 (N/F) mutations, N/K-Ras mutations, PTEN alterations, DNMT3A mutations, and T-cell receptor (TCR) gene rearrangements were also explored for predicting prognosis (11, 12). However, these risk factors have only been studied in small populations, and were not validated in independent centers. A formalin-fixed, paraffin-embedded (FFPE)-based miRNA signature has been developed to increase the predictive value of relapse of adult T-LBL (8). However, the miRNA-based signature would be influenced by tissue-specific dissimilarity. Moreover, the miRNA-based formulas and threshold values were detected by real-time PCR, which are not applied for other types of measurement platform data, and, therefore, have no universal applicability.

DNA methylation markers have rapidly emerged as predictors of cancer prognosis (13). However, few methylation markers have become established in T-LBL. High-throughput technologies allow us to identify reliable methylation markers. In this study, we delineated global genome-wide CpG methylation in patients with T-LBL and sought to identify and validate a CpG-based classifier for predicting relapse. A nomogram model was also established incorporating the CpG-based classifier, clinical variables, and genetic factors.

Patients

In this multicenter study, we collected 1,015 FFPE samples from patients with T-LBL who received treatment between January 1, 2003, and December 30, 2015, at Sun Yat-sen University Cancer Center (SYSUCC) and Guangdong General Hospital (GDGH), both in Guangzhou, China, and between January 1, 2006 and December 30, 2015, at 25 other medical centers across China. Total 549 were eligible for the final analysis (Supplementary Fig. S1), including 228 samples from SYSUCC and GDGH. We assigned 160 patients to the training cohort and 68 patients to the internal testing cohort using computer-generated random numbers. Furthermore, 321 samples from the 25 medical centers were assigned to the independent validation cohort. To generate CpG methylation profiles, we obtained 49 more FFPE samples from 21 relapsed and 28 relapse-free patients with T-LBL who received treatment between January 1, 2015 and January 1, 2016, at SYSUCC or GDGH (Supplementary Table S1).

All FFPE tissue samples were obtained by incisional lymph node biopsy and/or needle biopsy, and reassessed by three experienced pathologists independently (DX, LRH, and ML). The study was conducted in accordance with declaration of Helsinki. The study was approved by the institutional review board of each participating institution. All patients provided written informed content where appropriate.

Baseline assessments

Adults 18 to 65 years of age with complete clinical data and follow-up information, diagnosed with T-LBL, were included in this study. For the classification of T-LBL versus T-ALL, two experienced physicians (XPT and QQC) separately reassessed the diagnosis of T-LBL by the 2008 WHO criteria (Supplementary Fig. S1; ref. 14). We excluded patients with T-LBL with more than 20% lymphoblasts in the bone marrow. We also excluded patients who did not achieve CR, or from whom samples were not taken at initial diagnosis, or contained less than 80% tumor cells, or RNA content less than 5 ng/L, or DNA content less than 1 μg. We excluded patients who were not treated with hyper-CVAD protocol or BFM protocol (15, 16). Bone marrow aspiration and standard blood biochemistries, and thoraco-abdominal-pelvic CT or PET scan were performed at baseline. We identified CR and relapse by CT, PET-CT, and/or bone marrow biopsy according to Cheson criteria (17). Plasma LDH levels >245 U/L were considered elevated. Performance status was assessed using Eastern Cooperative Oncology Group performance status (ECOG-PS) and clinical stage was defined using the Ann Arbor stage system. We assessed NOTCH1/FBXW7 mutations as described previously (8). We further excluded patients who did not receive central nervous system (CNS) prophylaxis with intrathecal chemotherapy (e.g., methotrexate, cytarabine, and corticosteroids), and high-dose systemic chemotherapy (e.g., methotrexate and cytarabine). The clinicopathologic characteristics (except Ann Arbor Stage) did not differ significantly between patients receiving HSCT and those without HSCT in the entire cohort (Supplementary Table S2). The clinicopathologic characteristics of patients with T-LBL who were treated with BFM protocol and those who received hyper-CVAD did not differ significantly in the entire cohort (Supplementary Table S3).

Study procedures

By Dobbin's method (18), the optimal sample size of microarray assay was first estimated to be 49, including 28 relapse-free and 21 relapse samples using the criterion of 1.5 as the estimated standardized fold change, and 0.10 as the tolerance. Methylation 850K Beadchip (Illumina) was used to screen significant CpGs. After primary filtration, the least absolute shrinkage and selector operation (LASSO) algorithm (19) and support vector machine–recursive feature elimination (SVM-RFE; ref. 20) were used for relapse-related CpG selection (Supplementary Table S4). In the training phase, relapse-related CpGs were further tested by pyrosequencing in 160 FFPE T-LBL samples from the training cohort. Relapse-related CpGs from both algorithms were entered to construct a CpG-based classifier for predicting relapse-free survival (RFS) by LASSO Cox regression analysis in the training cohort (21, 22). Four CpGs are selected by LASSO Cox regression analysis according to 1-SE criteria (optimal values). X-tile software (version 3.6.1; Yale University) was used to determine the optimal cutoff in the training cohort (23). In the validation phase, to estimate the reproducibility and validity of the classifier, we evaluated the CpG-based classifier in the internal testing cohort and the independent validation cohort. For reproducibility of the Illumina 850K Beadchip assay, we compared methylation levels by methylation array and pyrosequencing using the same group of 49 samples (Supplementary Fig. S2). For wide clinical application of the CpG classifier, we compared the methylation levels of single CpGs from the CpG-based classifier between FFPE tissues and fresh-frozen as well as fresh effusion samples by pyrosequencing (Supplementary Fig. S3). To examine the effect of tumor heterogeneity on prediction accuracy of the classifier, we compared the methylation levels of the four CpGs between different areas of the same tissue or different FFPE tissues from the same patient (Supplementary Fig. S3). Subgroup stratification analysis based on LDH level, CNS involvement, ECOG-PS, and NOTCH1/FBXW7 (N/F) status was also used to examine the performance of the classifier.

Methylation array

Genomic DNA was extracted from 49 FFPE samples using QIAamp DNA Mini Kit (Qiagen), following the manufacturer's manual and assayed with Infinium Human Methylation 850K Beadchip according to Illumina's protocol. To maximize the extraction of FFPE information, all DNA samples were restored and passed quality control test by Infinium FFPE DNA Restoration and QC Kit (Illumina; Supplementary Fig. S4). The signal value was processed using “RnBeads” package in R software (24) and normalized by Beta-Mixture Quantile dilation (BMIQ) method (Supplementary Figs. S4 and S5; ref. 25). The β values, from 0 (unmethylated) to 1 (totally methylated) were used to quantify methylation levels. After exclusion steps shown in Supplementary Fig. S6, a threshold value of 0.15 for the average β and the associated P < 0.05 were considered significant. The methylation microarray data are available at the Gene Expression Omnibus (GSE131677).

Pyrosequencing array

CpG methylation levels in all three cohorts were determined by pyrosequencing as described previously (26). Briefly, genomic DNA was extracted with QIAamp DNA Mini Kit (Qiagen) and converted by EpiTect Bisulfite Kit (Qiagen). PCR was run using primers from PyroMark PCR Kit (Qiagen; Supplementary Table S5). cg00207389 primer was not designed and therefore excluded due to the density of CG sites. The CpG methylation value was calculated as [mC/(mC+C)]% using PyroMark Q24 instrument (Qiagen).

Statistical analysis

Univariate analysis was used to analyze correlation between variables and RFS/OS and variables with P < 0.1 were entered into multivariate analysis. Covariates included age (≤45 years vs. >45 years), sex, ECOG-PS (≤1 vs. ≥2), pleural and/or pericardial effusion, CNS involvement, mediastinal involvement, bone marrow involvement, LDH level (normal vs. elevated), Ann Arbor Stage (≤2 vs. >2), NOTCH1/FBXW7 status (mutated vs. wild-type), and four-CpG classifier (low vs. high). Log-rank test was used to compare survival curves. The package of “glmnet” and “e1071” were used to perform LASSO logistic regression and SVM-RFE algorithm, respectively, with R version 3.5.0 software. Survival was analyzed using the “survival” package in R software. RFS and overall survival (OS) were calculated from the date of diagnosis to the date of confirmed tumor relapse and death, respectively. Survival was censored at the date of death of other causes, or the date of the last follow-up visit. The accuracy of CpG-based classifier was assessed by time-dependent ROC curve analysis using the “survival ROC” package in R software. The performance of the nomogram was explored by calibration plots, using the “rms” package in R software. Statistical significance was set at P value less than 0.05.

Patient characteristics

The study flowchart is shown in Fig. 1 and the characteristics of patients with T-LBL in the training, internal testing, and independent validation cohorts are listed in Table 1. The median duration of follow-up was 42.0 (IQR 21.1–57.9) months in the training cohort, 42.5 (IQR 21.6–57.8) months in the internal testing cohort, and 37.7 (IQR 16.9–56.5) months in the independent validation cohort, respectively. Relapse occurred in 44.8% (246/549) patients, and the 1- and 3-year RFS were 76.9% and 54.7%, respectively. Furthermore, 40.3% (221/549) patients died and the 1- and 3-year OS were 85.8% and 60.4%, respectively (Supplementary Table S6).

Figure 1.

The study flowchart. The diagnostic and exclusion criteria are described in Supplementary Fig. S1.

Figure 1.

The study flowchart. The diagnostic and exclusion criteria are described in Supplementary Fig. S1.

Close modal
Table 1.

Clinicopathologic parameters of patients with T-LBL classified as low- or high-risk for poor survival based on the CpG-based classifier in the training, internal testing, and independent validation cohorts.

Training cohortInternal testing cohortIndependent validation cohort
PatientsLow riskHigh riskPatientsLow riskHigh riskPatientsLow riskHigh risk
(n = 160)(n = 96)(n = 64)Pa(n = 68)(n = 37)(n = 31)Pa(n = 321)(n = 180)(n = 141)Pa
Sex, n (%) 
 Male 111 (69) 64 (58) 47 (42) 0.363 49 (72) 29 (59) 20 (41) 0.205 240 (75) 130 (54) 110 (46) 0.236 
 Female 49 (31) 32 (65) 17 (35)  19 (28) 8 (42) 11 (58)  81 (25) 50 (62) 31 (38)  
Age (years), n (%) 
 ≤45 127 (79) 74 (58) 53 (42) 0.380 57 (84) 31 (54) 26 (46) 0.992 241 (75) 161 (67) 102 (43) 0.113 
 >45 33 (21) 22 (67) 11 (33)  11 (16) 6 (55) 5 (45)  80 (25) 41 (51) 39 (49)  
ECOG-PS, n (%) 
 <2 136 (85) 84 (62) 52 (38) 0.278 58 (85) 31 (53) 27 (47) 0.701 274 (85) 162 (59) 112 (41) 0.008 
 ≥2 24 (15) 12 (50) 12 (50)  10 (15) 6 (60) 4 (40)  47 (15) 18 (38) 29 (62)  
Effusion, pleural and/or pericardia, n (%) 
 Yes 107 (67) 61 (57) 46 (43) 0.273 39 (57) 20 (51) 19 (49) 0.548 200 (62) 114 (57) 86 (43) 0.668 
 No 53 (35) 35 (66) 18 (34)  29 (43) 17 (59) 12 (41)  121 (38) 66 (55) 55 (45)  
CNS involvement, n (%) 
 Yes 13 (8) 8 (62) 5 (38) 0.906 5 (7) 2 (40) 3 (60) 0.501 24 (7) 9 (38) 15 (62) 0.057 
 No 147 (93) 88 (60) 59 (40)  63 (93) 35 (56) 28 (44)  297 (93) 171 (58) 126 (42)  
Mediastinal involvement, n (%) 
 Yes 142 (89) 86 (61) 56 (39) 0.683 55 (81) 28 (51) 27 (49) 0.233 288 (90) 162 (56) 126 (44) 0.852 
 No 18 (11) 10 (56) 8 (44)  13 (19) 9 (69) 4 (31)  33 (10) 18 (55) 15 (45)  
Bone marrow involvement, n (%) 
 Yes 63 (39) 35 (56) 28 (44) 0.355 20 (29) 12 (60) 8 (40) 0.550 101 (31) 52 (51) 49 (49) 0.435 
 No 97 (61) 61 (63) 36 (37)  48 (71) 25 (52) 23 (48)  220 (69) 118 (54) 92 (46)  
LDH concentration, n (%) 
 Normal 77 (48) 50 (65) 27 (35) 0.220 33 (49) 20 (61) 13 (39) 0.319 109 (34) 69 (63) 40 (37) 0.061 
 Elevated 83 (52) 46 (55) 37 (45)  35 (65) 17 (49) 18 (51)  212 (66) 111 (52) 101 (48)  
Ann Arbor stage, n (%) 
 ≤2 20 (13) 13 (65) 7 (35) 0.626 8 (12) 4 (50) 4 (50) 0.790 12 (4) 8 (60) 4 (40) 0.451 
 >2 140 (87) 83 (59) 57 (41)  60 (88) 33 (55) 27 (45)  309 (96) 172 (56) 137 (44)  
N/F status, n (%) 
 Mutated 83 (52) 53 (64) 30 (36) 0.301 39 (57) 25 (64) 14 (36) 0.063 168 (52) 100 (60) 68 (40) 0.192 
 Wild-type 77 (48) 43 (56) 34 (44)  29 (43) 12 (41) 17 (59)  153 (48) 80 (52) 73 (48)  
HSCT at CR1, n (%) 
 Yes 25 (16) 19 (76) 6 (24) 0.075 12 (18) 6 (50) 6 (50) 0.735 126 (39) 76 (60) 50 (40) 0.218 
 No 135 (84) 77 (57) 58 (43)  56 (82) 31 (55) 25 (45)  195 (61) 104 (53) 91 (47)  
Induction chemotherapy, n (%) 
 BFM protocol 131 (82) 75 (57) 56 (42) 0.132 52 (76) 26 (50) 26 (50) 0.188 192 (60) 111 (58) 81 (42) 0.444 
 Hyper-CVAD protocol 29 (18) 21 (72) 8 (28)  16 (24) 11 (69) 5 (31)  129 (40) 69 (53) 60 (47)  
Training cohortInternal testing cohortIndependent validation cohort
PatientsLow riskHigh riskPatientsLow riskHigh riskPatientsLow riskHigh risk
(n = 160)(n = 96)(n = 64)Pa(n = 68)(n = 37)(n = 31)Pa(n = 321)(n = 180)(n = 141)Pa
Sex, n (%) 
 Male 111 (69) 64 (58) 47 (42) 0.363 49 (72) 29 (59) 20 (41) 0.205 240 (75) 130 (54) 110 (46) 0.236 
 Female 49 (31) 32 (65) 17 (35)  19 (28) 8 (42) 11 (58)  81 (25) 50 (62) 31 (38)  
Age (years), n (%) 
 ≤45 127 (79) 74 (58) 53 (42) 0.380 57 (84) 31 (54) 26 (46) 0.992 241 (75) 161 (67) 102 (43) 0.113 
 >45 33 (21) 22 (67) 11 (33)  11 (16) 6 (55) 5 (45)  80 (25) 41 (51) 39 (49)  
ECOG-PS, n (%) 
 <2 136 (85) 84 (62) 52 (38) 0.278 58 (85) 31 (53) 27 (47) 0.701 274 (85) 162 (59) 112 (41) 0.008 
 ≥2 24 (15) 12 (50) 12 (50)  10 (15) 6 (60) 4 (40)  47 (15) 18 (38) 29 (62)  
Effusion, pleural and/or pericardia, n (%) 
 Yes 107 (67) 61 (57) 46 (43) 0.273 39 (57) 20 (51) 19 (49) 0.548 200 (62) 114 (57) 86 (43) 0.668 
 No 53 (35) 35 (66) 18 (34)  29 (43) 17 (59) 12 (41)  121 (38) 66 (55) 55 (45)  
CNS involvement, n (%) 
 Yes 13 (8) 8 (62) 5 (38) 0.906 5 (7) 2 (40) 3 (60) 0.501 24 (7) 9 (38) 15 (62) 0.057 
 No 147 (93) 88 (60) 59 (40)  63 (93) 35 (56) 28 (44)  297 (93) 171 (58) 126 (42)  
Mediastinal involvement, n (%) 
 Yes 142 (89) 86 (61) 56 (39) 0.683 55 (81) 28 (51) 27 (49) 0.233 288 (90) 162 (56) 126 (44) 0.852 
 No 18 (11) 10 (56) 8 (44)  13 (19) 9 (69) 4 (31)  33 (10) 18 (55) 15 (45)  
Bone marrow involvement, n (%) 
 Yes 63 (39) 35 (56) 28 (44) 0.355 20 (29) 12 (60) 8 (40) 0.550 101 (31) 52 (51) 49 (49) 0.435 
 No 97 (61) 61 (63) 36 (37)  48 (71) 25 (52) 23 (48)  220 (69) 118 (54) 92 (46)  
LDH concentration, n (%) 
 Normal 77 (48) 50 (65) 27 (35) 0.220 33 (49) 20 (61) 13 (39) 0.319 109 (34) 69 (63) 40 (37) 0.061 
 Elevated 83 (52) 46 (55) 37 (45)  35 (65) 17 (49) 18 (51)  212 (66) 111 (52) 101 (48)  
Ann Arbor stage, n (%) 
 ≤2 20 (13) 13 (65) 7 (35) 0.626 8 (12) 4 (50) 4 (50) 0.790 12 (4) 8 (60) 4 (40) 0.451 
 >2 140 (87) 83 (59) 57 (41)  60 (88) 33 (55) 27 (45)  309 (96) 172 (56) 137 (44)  
N/F status, n (%) 
 Mutated 83 (52) 53 (64) 30 (36) 0.301 39 (57) 25 (64) 14 (36) 0.063 168 (52) 100 (60) 68 (40) 0.192 
 Wild-type 77 (48) 43 (56) 34 (44)  29 (43) 12 (41) 17 (59)  153 (48) 80 (52) 73 (48)  
HSCT at CR1, n (%) 
 Yes 25 (16) 19 (76) 6 (24) 0.075 12 (18) 6 (50) 6 (50) 0.735 126 (39) 76 (60) 50 (40) 0.218 
 No 135 (84) 77 (57) 58 (43)  56 (82) 31 (55) 25 (45)  195 (61) 104 (53) 91 (47)  
Induction chemotherapy, n (%) 
 BFM protocol 131 (82) 75 (57) 56 (42) 0.132 52 (76) 26 (50) 26 (50) 0.188 192 (60) 111 (58) 81 (42) 0.444 
 Hyper-CVAD protocol 29 (18) 21 (72) 8 (28)  16 (24) 11 (69) 5 (31)  129 (40) 69 (53) 60 (47)  

Abbreviations: BFM, Berlin–Frankfurt–Muenster; CR1, first complete remission; hyper-CVAD, hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone; LDH, lactate dehydrogenase; N/F, NOTCH1 and FBXW7.

aP value was calculated by χ2 test.

Microarray profiling of candidate CpGs

Our microarrays assay identified 419 differential CpGs; 28 and 29 relapse-related CpGs were selected by LASSO logistic regression and SVM-RFE algorithm, respectively, and 13 relapse-related CpGs were selected simultaneously by both algorithms (Fig. 2AC; Supplementary Table S4). To evaluate the reproducibility of the Illumina 850K Beadchip, we used pyrosequencing method to compare the methylation values in the same group of 49 samples. Consistent with other studies, the methylation value of three randomly selected CpGs between methylation microarray and pyrosequencing array exhibited good concordance (Supplementary Fig. S2). Hierarchical clustering of the 44 candidate CpG successfully separated the 49 samples into relapse and relapse-free clusters (except 2 relapse patients assigned to relapse-free clusters; Fig. 2D).

Figure 2.

Construction of CpG-based classifier. LASSO-Logistic algorithm (A) and SVM-RFE algorithm (B) were used to select relapse-related CpGs in 49 T-LBL tissues based on methylation 850K microarray analysis. C, Twenty-eight and 29 relapse-related CpGs were selected by LASSO-Logistic regression and SVM-RFE algorithm, respectively, of which 13 relapse-related CpGs were selected simultaneously by both algorithms. D, Cluster analysis of relapse-related CpGs were selected from the LASSO-Logistic or SVM-RFE algorithms. E, LASSO Cox regression analysis to select RFS-associated CpGs in the training cohort. A vertical line is drawn at the optimal value by 1-SE criteria to generate four nonzero coefficient CpGs. The genes corresponding to four nonzero coefficient CpGs are marked with red. CELF2, CUGBP Elav-like family member 2; HOXC5, homeobox C5; NA, unannotated CpGs; PRR36, Proline rich 36; SRPRB, SRP receptor subunit beta.

Figure 2.

Construction of CpG-based classifier. LASSO-Logistic algorithm (A) and SVM-RFE algorithm (B) were used to select relapse-related CpGs in 49 T-LBL tissues based on methylation 850K microarray analysis. C, Twenty-eight and 29 relapse-related CpGs were selected by LASSO-Logistic regression and SVM-RFE algorithm, respectively, of which 13 relapse-related CpGs were selected simultaneously by both algorithms. D, Cluster analysis of relapse-related CpGs were selected from the LASSO-Logistic or SVM-RFE algorithms. E, LASSO Cox regression analysis to select RFS-associated CpGs in the training cohort. A vertical line is drawn at the optimal value by 1-SE criteria to generate four nonzero coefficient CpGs. The genes corresponding to four nonzero coefficient CpGs are marked with red. CELF2, CUGBP Elav-like family member 2; HOXC5, homeobox C5; NA, unannotated CpGs; PRR36, Proline rich 36; SRPRB, SRP receptor subunit beta.

Close modal

Building and validating the CpG-based classifier

Forty-three candidate CpGs in the training cohort were quantified by pyrosequencing (Supplementary Table S7). Using LASSO Cox regression models, we built a four-CpG–based classifier (cg12373951, cg10323688, cg14569644, and cg01589972), corresponding to SRP receptor subunit beta (SRPRB), CUGBP Elav-like family member 2 (CELF2), homeobox C5 (HOXC5), and proline rich 36 (PRR36; Fig. 2E), for predicting RFS according to the weighted coefficients in the training cohort (Supplementary Fig. S7). The risk score was calculated as follows:

Risk score = (0.812 × methylation level of SRPRB) + (0.253 × methylation level of CELF2) + (1.044 × methylation level of HOXC5) − (0.163 × methylation level of PRR36).

The optimal cutoff value was determined to be 1.05 by X-tile plots (Supplementary Figs. S8 and S9) and stratified the training cohort into the high-risk group (64 patients, 40%) and low-risk group (96 patients, 60%). The high-risk group had significantly shorter RFS (HR = 4.869; 95% CI, 2.914–8.135; P < 0.001) and OS (HR = 4.662; 95% CI, 2.629–8.267; P < 0.001) in the training cohort (Fig. 3). Time-dependent ROC analysis showed that the CpG-based classifier exhibited good predictive accuracy (Fig. 3). The AUC of the classifier at 3 years was 0.820 (95% CI, 0.752–0.888).

Figure 3.

The performance of CpG-based classifier in predicting RFS and OS and time-dependent ROC analysis was used to analyze the performance of CpG-based classifier in the training, internal testing, and independent validation cohorts. Area under the ROC curve (AUC) at 1, 3, and 5 years to access prognostic accuracy. A and B, Training cohort. C and D, Internal testing cohort. E, and F, Independent validation cohort. HR, hazards ratio; P values were calculated using the log-rank test.

Figure 3.

The performance of CpG-based classifier in predicting RFS and OS and time-dependent ROC analysis was used to analyze the performance of CpG-based classifier in the training, internal testing, and independent validation cohorts. Area under the ROC curve (AUC) at 1, 3, and 5 years to access prognostic accuracy. A and B, Training cohort. C and D, Internal testing cohort. E, and F, Independent validation cohort. HR, hazards ratio; P values were calculated using the log-rank test.

Close modal

The classifier was validated in the internal testing cohort and the independent validation cohort. At a cutoff of 1.05, the high-risk group had significantly shorter RFS in both the internal testing cohort (HR = 3.621; 95% CI, 1.644–7.793; P < 0.001) and the independent validation cohort (HR = 3.765; 95% CI, 2.679–5.293; P < 0.001) versus the low-risk group (Fig. 3). Moreover, the classifier achieved similar and stable predictive accuracy in the internal testing cohort (AUC at 3 years 0.817; 95% CI, 0.716–0.918) and the independent validation cohort (0.753; 95% CI, 0.698–0.808). The classifier also showed good predictive value in OS in the three cohorts (HRs ranging from 3.034 to 4.662; AUC at 3 years ranging from 0.742 to 0.801, Fig. 3). Patient characteristics (except ECOG-PS in the independent validation cohort) did not differ between the high-risk and low-risk group (Table 1). The stratification analysis showed that our CpG-based classifier was still a statistically significant predictive model when stratified by clinicopathological risk factors (except for CNS involvement) and genetic risk factors (Supplementary Figs. S10 and S11).

Significant variables from univariate analysis (Supplementary Figs. S12 and S13) were entered into multivariate analysis, which revealed that LDH level, ECOG-PS, CNS involvement, N/F status, and the four-CpG classifier were independent prognostic factors of RFS and OS. After adjustment, the four-CpG–based classifier remained a significant and independent prognostic factor of RFS and OS in the combined training and testing cohort and independent validation cohort (Supplementary Tables S8 and S9). Moreover, our CpG-based classifier showed higher predictive accuracy than any clinicopathological/genetic risk factors or single CpG alone (log-rank test, all P < 0.05; Supplementary Fig. S14).

Impact of type of tissues and tumor heterogeneity

To estimate the reproducibility and applicability of CpG-based classifier, we assayed methylation value of the four CpG sites in different types of tissues (FFPE tissues, fresh-frozen tissues, and fresh effusion tissues). The four-CpG methylation value of fresh-frozen tissues and fresh effusion tissues exhibited good consistency with that of FFPE tissues (all P < 0.001, all R > 0.94, Pearson correlation analysis; Supplementary Figs. S3A and S3B). To determine whether tumor heterogeneity affected the risk score, we compared the methylation level of the four candidate CpGs between different areas of the same FFPE samples and different FFPE tissues from the same patient. Tumor heterogeneity did not affect the reliability of our classifier according to Pearson correlation analysis (all P < 0.001, all R > 0.95; Supplementary Figs. S3C and S3D). Furthermore, we assayed methylation values of the four CpG sites and their corresponding mRNA levels of purported gene targets in fresh-frozen tissues, confirming that the methylation of those four CpGs could reflect the mRNA expression levels of the purported gene targets (all P < 0.001, Pearson correlation analysis; Supplementary Fig. S15)

Performance of a predictive nomogram

We generated a nomogram to predict 3-year RFS based on significant predictors of RFS via multivariate analysis of the training and internal testing cohort (the developing set). The predictors included the CpG-based classifier, LDH level, CNS involvement, ECOG-PS, and N/F status (Fig. 4A). The calibration plots showed an acceptable agreement in the two cohorts between nomogram prediction and actual observation (Supplementary Fig. S16). The AUC was 0.839 in the developing set and 0.776 in the independent validation cohort at 3 years, and both AUCs showed significantly higher predictive accuracy than the CpG-based classifier, clinicopathologic risk factor, or genetic risk factor alone (all P < 0.05, Fig. 4B; Supplementary Fig. S17).

Figure 4.

Establishing and validating a nomogram that combines the four-CpG–based classifier and ECOG-PS, LDH level, CNS involvement and N/F status to predict RFS. A, The nomogram combines the four-CpG–based classifier and ECOG-PS, LDH level, CNS involvement, and N/F status for predicting the 1- and 3-year RFS for patients after complete remission. The nomogram was developed with data from 228 cases in the training and internal testing cohort (the developing set). B and C, Time-dependent ROC curves and AUCs at 3 years were used to assess the predictive accuracy of the nomogram, compared with the four-CpG–based classifier and clinicopathologic risk factors. LDH, lactate dehydrogenase; N/F, NOTCH1 and FBXW7.

Figure 4.

Establishing and validating a nomogram that combines the four-CpG–based classifier and ECOG-PS, LDH level, CNS involvement and N/F status to predict RFS. A, The nomogram combines the four-CpG–based classifier and ECOG-PS, LDH level, CNS involvement, and N/F status for predicting the 1- and 3-year RFS for patients after complete remission. The nomogram was developed with data from 228 cases in the training and internal testing cohort (the developing set). B and C, Time-dependent ROC curves and AUCs at 3 years were used to assess the predictive accuracy of the nomogram, compared with the four-CpG–based classifier and clinicopathologic risk factors. LDH, lactate dehydrogenase; N/F, NOTCH1 and FBXW7.

Close modal

The developing set and the independent validation cohort were categorized into four subgroups using total scores according to maximization of prognostic difference (score: 0–38.5, 38.6–138.4, 138.5–190.7, and ≥190.8); each subgroup represented a distinct prognosis (Fig. 5A and B; Supplementary Fig. S18). Four therapeutic schedules (BFM, BFM followed by HSCT, hyper-CVAD, and hyper-CVAD followed by HSCT) yielded comparable RFS or OS of 549 patients (Fig. 5C). Meanwhile, patients with a nomogram score ≥138.5 receiving HSCT had longer RFS and OS versus those not receiving HSCT after intensive chemotherapy (Fig. 5D and E; Supplementary Figs. S19–S21). Patients receiving BFM followed by HSCT had significantly longer OS (HR = 2.017; 95% CI, 1.022–3.981; P = 0.043) but comparable RFS (HR = 1.589; 95% CI, 0.800–3.158; P = 0.186) versus those receiving hyper-CVAD followed by HSCT (Fig. 5E). These results indicated that the nomogram successfully stratified risk of patients and could help clinicians identify patients who benefited from a certain therapeutic schedule.

Figure 5.

Performance of the nomogram in stratifying risk of patients and predicting benefits of which types of therapeutic schedules. The patients in the developing set [the combined training and internal testing cohort (A)] and the independent validation cohort (B) were grouped into four subgroups by total score (0–38.5, 38.6–138.4, 138.5–190.7, and ≥190.8). C–E, Kaplan–Meier survival curves of RFS for patients stratified by four types of therapeutic schedules (BFM; BFM+HSCT; hyper-CVAD; hyper-CVAD+HSCT) in the entire cohort and/or in patients with a nomogram score ≥138.5. hyper-CVAD, hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone; P values were calculated using the log-rank test.

Figure 5.

Performance of the nomogram in stratifying risk of patients and predicting benefits of which types of therapeutic schedules. The patients in the developing set [the combined training and internal testing cohort (A)] and the independent validation cohort (B) were grouped into four subgroups by total score (0–38.5, 38.6–138.4, 138.5–190.7, and ≥190.8). C–E, Kaplan–Meier survival curves of RFS for patients stratified by four types of therapeutic schedules (BFM; BFM+HSCT; hyper-CVAD; hyper-CVAD+HSCT) in the entire cohort and/or in patients with a nomogram score ≥138.5. hyper-CVAD, hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone; P values were calculated using the log-rank test.

Close modal

In this study, we successfully built a four-CpG–based classifier in the training cohort via methylation microarrays and further validated the classifier in the internal testing cohort and the independent validation cohort. The classifier was of good predictive value of RFS and OS for adults with T-LBL, and performed better than conventional clinical and genetic risk factors. We also built a nomogram based on the classifier and other clinical and genetic predictors of relapse, which has good clinical applications.

Until now, there has been no study on the relation between genome-wide CpG profiles and prognosis of T-LBL. Only scarce reports showed aberrant gene methylations in T-LBL tissues (12). SOCS3 hypermethylation enhances JAK–STAT signaling and contributes to T-LBL development (27). In this study, we used microarray analysis to assess the predictive value of CpGs for patients with T-LBL. To avoid losing important markers, we used two distinct algorithms to select 44 relapse-related CpGs in the discovery phase. Four CpGs most intimately associated with RFS were finally identified by LASSO Cox regression in the training phase. All four CpGs in our classifier were identified by LASSO logistic regression, and SRPRB and CELF2 were not identified using SVM-RFE, which may be due to the different mathematical principles of the two methods. LASSO regression shrinks the coefficient estimates towards zero, whereas SVM-RFE uses the maximum internal principle in SVM to eliminate the feature recursively. The predictive value of the CpG-based classifier was further validated in the internal testing cohort and the independent validation cohort. Moreover, the predictive value of the classifier was compared with single CpGs, confirming the effectiveness of the combined strategy.

The four CpG markers in our classifier showed novel association with T-LBL. According to Methylation 850K annotation, cg12373951 and cg14569644 were located at 5′UTR region, cg10323688 and cg 01589972 were located at TSS (transcription start site)1500 region. All four CpGs were associated with CIMP (CpG Island Methylator Phenotype) status. Upregulated SRPRB increases cell apoptosis rate and decreases NF-κB expression and phosphorylation in pancreatic ductal adenocarcinoma (28). CELF2 expression is increased in response to T-cell signaling through the combined regulation of transcription and mRNA stability (29). CELF2 controls lymphoid enhancer-binding factor 1 (LEF1) alternative splicing and aberrant expression of LEF1 is implicated in tumorigenesis and cancer cell proliferation, migration, and invasion (30, 31). HOXC5 is involved in lymphomagenesis and is a key upstream regulator of Wnt/BMP signaling (32, 33). HOXC5 upregulation can be detected in cells representing mature stages of lymphoid cell differentiation, but not in leukemic cells (with activated hTERT expression) representing immature stages of lymphoid differentiation (34). The function of PRR36 remains unknown. Further studies on SRPRB, CELF2, HOXC5, and PRR36 will provide new insights into T-LBL. The regulation of the protein metabolic process, and the cellular differentiation and development process involving PI3K-AKT, HIF-1, insulin, and RNA transport signaling pathways should be elucidated to understand the mechanism of T-LBL.

In addition, we built a nomogram including LDH level, CNS involvement, EOCG-PS, N/F status, and the CpG-based classifier to predict individual relapse risks. The performance of the nomogram was verified in the independent validation cohorts. To the best of our knowledge, this is the first nomogram for predicting survival of patients with T-LBL that is based on a large database. Both physicians and patients could perform an individualized survival prediction through this easy-to-use scoring system.

To date, the standard chemotherapy protocol for adult T-LBL has not been established. The BFM protocol and hyper-CVAD regimens are most commonly used in adult T-LBL. It is still unknown which chemotherapeutic protocol is superior. Moreover, HSCT is often used to consolidate CR1 after induction therapy, but which subset of patients that could benefit from which type of chemotherapy followed by HSCT remains unclear (35). In this study, using the nomogram, we showed that BFM followed by HSCT provided a survival benefit to patients with a nomogram score ≥138.5. This tool could allow for better identification of patients who benefit from a therapeutic schedule.

The limitations of our study should be noted. All the specimens were obtained from patients in China. Further studies are required to determine if our CpG-based classifier can be applied to other ethnic groups. The biological mechanism of the candidate markers such as SRPRB, CELF2, HOXC5, and PRR36 are still unknown; such studies may suggest treatment strategies. Although our retrospective study may be subject to biases, due to the scarcity of the disease and inclusion of a large-scale of adults with T-LBL in this study, our retrospective study still has a strong guiding value in treatment of adults with T-LBL.

In conclusion, our CpG-based classifier is a useful predictive biomarker for predicting relapse of T-LBL in adult patients. A nomogram incorporating the CpG-based classifier may help predict individual relapse risk and help clinicians manage patients with T-LBL.

No potential conflicts of interest were disclosed.

The key raw data have been uploaded onto the Research Data Deposit public platform (RDD), with the approval RDD number of (RDDA2019001073). The microarray data have been deposited online under accession number (GSE131677).

Conception and design: Q.-Q. Cai, X.-P. Tian, D. Xie

Development of methodology: Q.-Q. Cai, X.-P. Tian, D. Xie

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Q.-Q. Cai, X.-P. Tian, N. Su, L. Wang, W.-J. Huang, Y.-H. Liu, X. Zhang, H.-Q. Huang, T.-Y. Lin, S.-Y. Ma, H.-L. Rao, M. Li, F. Liu, F. Zhang, L.-Y. Zhong, L. Liang, X.-L. Lan, J. Li, B. Liao, Z.-H. Li, Q.-L. Tang, Q. Liang, C.-K. Shao, Q.-L. Zhai, R.-F. Cheng, K. Ru, X. Gu, X.-N. Lin, K. Yi, Y.-R. Shuang, X.-D. Chen, W. Dong, C. Sun, W. Sang, H. Liu, Z.-G. Zhu, J. Rao, Q.-N. Guo, Y. Zhou, X.-L. Meng, Y. Zhu, C.-L. Hu, Y.-R. Jiang, Y. Zhang, H.-Y. Gao, Z.-J. Xia, X.-Y. Pan, L. Hai, G.-W. Li, L.-Y. Song, T.-B. Kang

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Q.-Q. Cai, X.-P. Tian, L. Wang, N. Su, D. Xie

Writing, review, and/or revision of the manuscript: Q.-Q. Cai, X.-P. Tian, N. Su, W.-J. Huang, D. Xie

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Q.-Q. Cai, X.-P. Tian, D. Xie

Study supervision: Q.-Q. Cai, D. Xie

This work was supported by grants from National Key R&D Program of China (2017YFC1309001, 2016YFC1302305, to D. Xie); National Natural Science Foundation of China (81603137, 81973384, to X.-P. Tian; 81672686, to Q.-Q. Cai); Special Support Program of Sun Yat-sen University Cancer Center (PT19020401, to Q.-Q. Cai).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Portell
CA
,
Sweetenham
JW
. 
Adult lymphoblastic lymphoma
.
Cancer J
2012
;
18
:
432
8
.
2.
Burkhardt
B
,
Mueller
S
,
Khanam
T
,
Perkins
SL
. 
Current status and future directions of T-lymphoblastic lymphoma in children and adolescents
.
Br J Haematol
2016
;
173
:
545
59
.
3.
Raetz
EA
,
Perkins
SL
,
Bhojwani
D
,
Smock
K
,
Philip
M
,
Carroll
WL
, et al
Gene expression profiling reveals intrinsic differences between T-cell acute lymphoblastic leukemia and T-cell lymphoblastic lymphoma
.
Pediatr Blood Cancer
2006
;
47
:
130
40
.
4.
Huguet
F
,
Leguay
T
,
Raffoux
E
,
Thomas
X
,
Beldjord
K
,
Delabesse
E
, et al
Pediatric-inspired therapy in adults with Philadelphia chromosome-negative acute lymphoblastic leukemia: the GRAALL-2003 study
.
J Clin Oncol
2009
;
27
:
911
8
.
5.
Thomas
DA
,
O'Brien
S
,
Cortes
J
,
Giles
FJ
,
Faderl
S
,
Verstovsek
S
, et al
Outcome with the hyper-CVAD regimens in lymphoblastic lymphoma
.
Blood
2004
;
104
:
1624
30
.
6.
Hoelzer
D
,
Gokbuget
N
,
Digel
W
,
Faak
T
,
Kneba
M
,
Reutzel
R
, et al
Outcome of adult patients with T-lymphoblastic lymphoma treated according to protocols for acute lymphoblastic leukemia
.
Blood
2002
;
99
:
4379
85
.
7.
Sweetenham
JW
. 
Treatment of lymphoblastic lymphoma in adults
.
Oncology
2009
;
23
:
1015
20
.
8.
Tian
XP
,
Huang
WJ
,
Huang
HQ
,
Liu
YH
,
Wang
L
,
Zhang
X
, et al
Prognostic and predictive value of a microRNA signature in adults with T-cell lymphoblastic lymphoma
.
Leukemia
2019
;
33
:
2454
65
.
9.
Song
KW
,
Barnett
MJ
,
Gascoyne
RD
,
Chhanabhai
M
,
Forrest
DL
,
Hogge
DE
, et al
Primary therapy for adults with T-cell lymphoblastic lymphoma with hematopoietic stem-cell transplantation results in favorable outcomes
.
Ann Oncol
2007
;
18
:
535
40
.
10.
Gu
Y
,
Pan
Y
,
Meng
B
,
Guan
B
,
Fu
K
,
Sun
B
, et al
High levels of bcl-2 protein expression do not correlate with genetic abnormalities but predict worse prognosis in patients with lymphoblastic lymphoma
.
Tumour Biol
2013
;
34
:
1441
50
.
11.
Lepretre
S
,
Touzart
A
,
Vermeulin
T
,
Picquenot
JM
,
Tanguy-Schmidt
A
,
Salles
G
, et al
Pediatric-like acute lymphoblastic leukemia therapy in adults with lymphoblastic lymphoma: the GRAALL-LYSA LL03 study
.
J Clin Oncol
2016
;
34
:
572
80
.
12.
Bond
J
,
Touzart
A
,
Lepretre
S
,
Graux
C
,
Bargetzi
M
,
Lhermitte
L
, et al
DNMT3A mutation is associated with increased age and adverse outcome in adult T-cell acute lymphoblastic leukemia
.
Haematologica
2019
;
104
:
1617
25
.
13.
Jeschke
J
,
Bizet
M
,
Desmedt
C
,
Calonne
E
,
Dedeurwaerder
S
,
Garaud
S
, et al
DNA methylation-based immune response signature improves patient diagnosis in multiple cancers
.
J Clin Invest
2017
;
127
:
3090
102
.
14.
Vardiman
JW
,
Thiele
J
,
Arber
DA
,
Brunning
RD
,
Borowitz
MJ
,
Porwit
A
, et al
The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes
.
Blood
2009
;
114
:
937
51
.
15.
Burkhardt
B
,
Oschlies
I
,
Klapper
W
,
Zimmermann
M
,
Woessmann
W
,
Meinhardt
A
, et al
Non-Hodgkin's lymphoma in adolescents: experiences in 378 adolescent NHL patients treated according to pediatric NHL-BFM protocols
.
Leukemia
2011
;
25
:
153
60
.
16.
Kantarjian
HM
,
O'Brien
S
,
Smith
TL
,
Cortes
J
,
Giles
FJ
,
Beran
M
, et al
Results of treatment with hyper-CVAD, a dose-intensive regimen, in adult acute lymphocytic leukemia
.
J Clin Oncol
2000
;
18
:
547
61
.
17.
Cheson
BD
,
Pfistner
B
,
Juweid
ME
,
Gascoyne
RD
,
Specht
L
,
Horning
SJ
, et al
Revised response criteria for malignant lymphoma
.
J Clin Oncol
2007
;
25
:
579
86
.
18.
Dobbin
KK
,
Zhao
Y
,
Simon
RM
. 
How large a training set is needed to develop a classifier for microarray data?
Clin Cancer Res
2008
;
14
:
108
14
.
19.
Butcher
LM
,
Beck
S
. 
Probe Lasso: a novel method to rope in differentially methylated regions with 450K DNA methylation data
.
Methods
2015
;
72
:
21
8
.
20.
Huang
ML
,
Hung
YH
,
Lee
WM
,
Li
RK
,
Jiang
BR
. 
SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier
.
Sci World J
2014
;
2014
:
795624
.
21.
Gui
J
,
Li
H
. 
Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data
.
Bioinformatics
2005
;
21
:
3001
8
.
22.
Goeman
JJ
. 
L1 penalized estimation in the Cox proportional hazards model
.
Biom J
2010
;
52
:
70
84
.
23.
Camp
RL
,
Dolled-Filhart
M
,
Rimm
DL
. 
X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization
.
Clin Cancer Res
2004
;
10
:
7252
9
.
24.
Assenov
Y
,
Muller
F
,
Lutsik
P
,
Walter
J
,
Lengauer
T
,
Bock
C
. 
Comprehensive analysis of DNA methylation data with RnBeads
.
Nat Methods
2014
;
11
:
1138
40
.
25.
Teschendorff
AE
,
Marabita
F
,
Lechner
M
,
Bartlett
T
,
Tegner
J
,
Gomez-Cabrero
D
, et al
A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data
.
Bioinformatics
2013
;
29
:
189
96
.
26.
Royo
JL
,
Hidalgo
M
,
Ruiz
A
. 
Pyrosequencing protocol using a universal biotinylated primer for mutation detection and SNP genotyping
.
Nat Protoc
2007
;
2
:
1734
9
.
27.
Roncero
AM
,
Lopez-Nieva
P
,
Cobos-Fernandez
MA
,
Villa-Morales
M
,
Gonzalez-Sanchez
L
,
Lopez-Lorenzo
JL
, et al
Contribution of JAK2 mutations to T-cell lymphoblastic lymphoma development
.
Leukemia
2016
;
30
:
94
103
.
28.
Ma
Q
,
Wu
X
,
Wu
J
,
Liang
Z
,
Liu
T
. 
SERP1 is a novel marker of poor prognosis in pancreatic ductal adenocarcinoma patients via anti-apoptosis and regulating SRPRB/NF-kappaB axis
.
Int J Oncol
2017
;
51
:
1104
14
.
29.
Mallory
MJ
,
Allon
SJ
,
Qiu
J
,
Gazzara
MR
,
Tapescu
I
,
Martinez
NM
, et al
Induced transcription and stability of CELF2 mRNA drives widespread alternative splicing during T-cell signaling
.
Proc Natl Acad Sci U S A
2015
;
112
:
E2139
48
.
30.
Mallory
MJ
,
Jackson
J
,
Weber
B
,
Chi
A
,
Heyd
F
,
Lynch
KW
. 
Signal- and development-dependent alternative splicing of LEF1 in T cells is controlled by CELF2
.
Mol Cell Biol
2011
;
31
:
2184
95
.
31.
Santiago
L
,
Daniels
G
,
Wang
D
,
Deng
FM
,
Lee
P
. 
Wnt signaling pathway protein LEF1 in cancer, as a biomarker for prognosis and a target for treatment
.
Am J Cancer Res
2017
;
7
:
1389
406
.
32.
Bijl
J
,
van Oostveen
JW
,
Kreike
M
,
Rieger
E
,
van der Raaij-Helmer
LM
,
Walboomers
JM
, et al
Expression of HOXC4, HOXC5, and HOXC6 in human lymphoid cell lines, leukemias, and benign and malignant lymphoid tissue
.
Blood
1996
;
87
:
1737
45
.
33.
Hrycaj
SM
,
Dye
BR
,
Baker
NC
,
Larsen
BM
,
Burke
AC
,
Spence
JR
, et al
Hox5 genes regulate the Wnt2/2b-Bmp4-signaling axis during lung development
.
Cell Rep
2015
;
12
:
903
12
.
34.
Yan
T
,
Ooi
WF
,
Qamra
A
,
Cheung
A
,
Ma
D
,
Sundaram
GM
, et al
HoxC5 and miR-615–3p target newly evolved genomic regions to repress hTERT and inhibit tumorigenesis
.
Nat Commun
2018
;
9
:
100
.
35.
Cortelazzo
S
,
Ponzoni
M
,
Ferreri
AJ
,
Hoelzer
D
. 
Lymphoblastic lymphoma
.
Crit Rev Oncol Hematol
2011
;
79
:
330
43
.

Supplementary data