A Gene Panel,Including LRP12, Is Frequently Hypermethylated in Major Types of B-Cell Lymphoma

Epigenetic modifications and DNA methylation in particular, have been recognized as important mechanisms to alter gene expression in malignant cells. Here, we identified candidate genes which were upregulated after an epigenetic treatment of B-cell lymphoma cell lines (Burkitt''s lymphoma, BL; Follicular lymphoma, FL; Diffuse large B-cell lymphoma, DLBCL activated B-cell like, ABC; and germinal center like, GCB) and simultaneously expressed at low levels in samples from lymphoma patients. Qualitative methylation analysis of 24 candidate genes in cell lines revealed five methylated genes (BMP7, BMPER, CDH1, DUSP4 and LRP12), which were further subjected to quantitative methylation analysis in clinical samples from 59 lymphoma patients (BL, FL, DLBCL ABC and GCB; and primary mediastinal B-cell lymphoma, PMBL). The genes LRP12 and CDH1 showed the highest methylation frequencies (94% and 92%, respectively). BMPER (58%), DUSP4 (32%) and BMP7 (22%), were also frequently methylated in patient samples. Importantly, all gene promoters were unmethylated in various control samples (CD19+ peripheral blood B cells, peripheral blood mononuclear cells and tonsils) as well as in follicular hyperplasia samples, underscoring a high specificity. The combination of LRP12 and CDH1 methylation could successfully discriminate between the vast majority of the lymphoma and control samples, emphasized by receiver operating characteristic analysis with a c-statistic of 0.999. These two genes represent promising epigenetic markers which may be suitable for monitoring of B-cell lymphoma.     

Prognostic impact of C-REL expression in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) with a germinal center B-cell (GCB) phenotype is believed to confer a better prognosis than DLBCL with an activated B-cell (ABC) phenotype. Previous studies have suggested that nuclear factor-κB (NF-κB) activation plays an important role in the ABC subtype of DLBCL, whereas c-REL amplification is associated with the GCB subtype. Using immunohistochemical techniques, we examined 68 newly diagnosed de novo DLBCL cases (median follow-up 44 months, range 1 to 142 months) for the expression of c-REL, BCL-6, CD10, and MUM1/IRF4. Forty-four (65%) cases demonstrated positive c-REL nuclear expression. In this cohort of patients, the GCB phenotype was associated with a better overall survival (OS) than the non-GCB phenotype (Kaplan–Meier survival (KMS) analysis, p = 0.016, Breslow–Gehan–Wilcoxon test). In general, c-REL nuclear expression did not correlate with GCB vs. non-GCB phenotype, International Prognostic Index score, or OS. However, cases with a GCB phenotype and negative nuclear c-REL demonstrated better OS than cases with a GCB phenotype and positive nuclear c-REL (KMS analysis, p = 0.045, Breslow–Gehan–Wilcoxon test), whereas in cases with non-GCB phenotype, the expression of c-REL did not significantly impact the prognosis. These results suggest that c-REL nuclear expression may be a prognostic factor in DLBCL and it may improve patient risk stratification in combination with GCB/non-GCB phenotyping.     

Phosphorylated IκBα Predicts Poor Prognosis in Activated B-Cell Lymphoma and Its Inhibition with Thymoquinone Induces Apoptosis via ROS Release

Activated B-cell lymphoma (ABC), one of the three subtypes of Diffuse Large B-cell Lymphoma (DLBCL) has the worst survival rate after upfront chemotherapy and is characterized by constitutively activated NFκB. We therefore studied the role of NFκB In a cohort of clinical DLBCL samples and ABC cell lines. In our clinical tissue microarray cohort of DLBCL samples, p-IκBα was detected in 38.3% of ABC DLBCL and was an independent prognostic marker for poor survival. In vitro, we found that Thymoquinone (TQ), a natural compound isolated from Nigella sativa caused release of ROS in ABC cells. TQ-mediated release of ROS in turn inhibited NFκB activity by dephosphorylating IκBα and decreased translocation of p65 subunit of NFκB in the nuclear compartment in ABC cell lines. This led to inhibition of cell viability and induction of mitochondrial dependent apoptosis in ABC-DLBCL cell lines. Additionally, TQ treatment also caused up-regulation of death receptor 5 (DR5), however, up-regulation of DR5 did not play a role in TQ-induced apoptosis. Finally, combination of sub-optimal doses of TQ and TRAIL induced efficient apoptosis in ABC-DLBCL cell lines. These data show that p-IκBα can be used as a prognostic marker and target for therapy in this aggressive sub-type of DLBCL and TQ may play an important role in the management of DLBCL in the future.     

Clinicopathologic Analysis of Localized Nasal/Paranasal Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) comprises 2 molecularly distinct subgroups of non-germinal center B-cell-like (non-GCB) and germinal center B-cell-like (GCB) DLBCLs, with the former showing relatively poor prognosis. In the present study, we analyzed the clinicopathological features of 39 patients with localized nasal/paranasal DLBCL. Immunohistochemistry-based subclassification revealed that 11 patients (28%) were of the GCB-type according to Hans’ algorithm and 11 (28%) were of the GCB-type according to Choi’s algorithm. According to both Hans’ and Choi’s algorithms, the non-GCB type was predominant. Nevertheless, prognosis was good. Overall survival did not differ significantly between the GCB and non-GCB subgroups (Hans’ algorithm: p = 0.57, Choi’s algorithm: p = 0.99). Furthermore, the prognosis of localized nasal/paranasal DLBCL was better than that of other localized extranodal DLBCLs. The prognosis of extranodal DLBCL is usually considered poorer than that of nodal DLBCL. However, in our study, no difference was noted between patients with localized nasal/paranasal DLBCL and patients with localized nodal DLBCL. In conclusion, although the non-GCB subtype is thought to show poor prognosis, in our study, the prognosis for localized nasal/paranasal DLBCL patients was good irrespective of subclassification.     

Epstein-Barr virus-induced gene 3 (EBI3): a novel diagnosis marker in Burkitt lymphoma and diffuse large B-cell lymphoma

The distinction between Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL), two types of mature aggressive B-cell lymphomas that require distinct treatments, can be difficult because of forms showing features intermediate between DLBCL and BL (here called BL/DLBCL). They can be discriminated by the presence of c-myc translocations characteristic of BL. However, these are not exclusive of BL and when present in DLBCL are associated with lower survival. In this study, we show that Epstein-Barr virus-induced gene 3 (EBI3) is differentially expressed among BL and DLBCL. Analysis of gene expression data from 502 cases of aggressive mature B-cell lymphomas available on Gene Expression Omnibus and immunohistochemical analysis of 184 cases of BL, BL/DLBCL or DLBCL, showed that EBI3 was not expressed in EBV-positive or -negative BL cases, whereas it was expressed by over 30% of tumoral cells in nearly 80% of DLBCL cases, independently of their subtypes. In addition, we show that c-myc overexpression represses EBI3 expression, and that DLBCL or BL/DLBCL cases with c-myc translocations have lower expression of EBI3. Thus, EBI3 immunohistochemistry could be useful to discriminate BL from DLBCL, and to identify cases of BL/DLBCL or DLBCL with potential c-myc translocations.     

Optimization based tumor classification from microarray gene expression data

Background

An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.

Methodology/Principal Findings

We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.

Conclusions/Significance

The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.     

Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines

Background

Recent reports indicate that in vitro drug screens combined with gene expression profiles (GEP) of cancer cell lines may generate informative signatures predicting the clinical outcome of chemotherapy. In multiple myeloma (MM) a range of new drugs have been introduced and now challenge conventional therapy including high dose melphalan. Consequently, the generation of predictive signatures for response to melphalan may have a clinical impact. The hypothesis is that melphalan screens and GEPs of B-cell cancer cell lines combined with multivariate statistics may provide predictive clinical information.

Materials and Methods

Microarray based GEPs and a melphalan growth inhibition screen of 59 cancer cell lines were downloaded from the National Cancer Institute database. Equivalent data were generated for 18 B-cell cancer cell lines. Linear discriminant analyses (LDA), sparse partial least squares (SPLS) and pairwise comparisons of cell line data were used to build resistance signatures from both cell line panels. A melphalan resistance index was defined and estimated for each MM patient in a publicly available clinical data set and evaluated retrospectively by Cox proportional hazards and Kaplan-Meier survival analysis.

Principal Findings

Both cell line panels performed well with respect to internal validation of the SPLS approach but only the B-cell panel was able to predict a significantly higher risk of relapse and death with increasing resistance index in the clinical data sets. The most sensitive and resistant cell lines, MOLP-2 and RPMI-8226 LR5, respectively, had high leverage, which suggests their differentially expressed genes to possess important predictive value.

Conclusion

The present study presents a melphalan resistance index generated by analysis of a B-cell panel of cancer cell lines. However, the resistance index needs to be functionally validated and correlated to known MM biomarkers in independent data sets in order to better understand the mechanism underlying the preparedness to melphalan resistance.     

MicroRNA Profiling of Primary Cutaneous Large B-Cell Lymphomas

Aberrant expression of microRNAs is widely accepted to be pathogenetically involved in nodal diffuse large B-cell lymphomas (DLBCLs). However, the microRNAs profiles of primary cutaneous large B-cell lymphomas (PCLBCLs) are not yet described. Its two main subtypes, i.e., primary cutaneous diffuse large B-cell lymphoma, leg type (PCLBCL-LT) and primary cutaneous follicle center lymphoma (PCFCL) are characterized by an activated B-cell (ABC)-genotype and a germinal center B-cell (GCB)-genotype, respectively. We performed high-throughput sequencing analysis on frozen tumor biopsies from 19 cases of PCFCL and PCLBCL-LT to establish microRNA profiles. Cluster analysis of the complete microRNome could not distinguish between the two subtypes, but 16 single microRNAs were found to be differentially expressed. Single microRNA RT-qPCR was conducted on formalin-fixed paraffin-embedded tumor biopsies of 20 additional cases, confirming higher expression of miR-9-5p, miR-31-5p, miR-129-2-3p and miR-214-3p in PCFCL as compared to PCLBCL-LT. MicroRNAs previously described to be higher expressed in ABC-type as compared to GCB-type nodal DLBCL were not differentially expressed between PCFCL and PCLBCL-LT. In conclusion, PCFCL and PCLBCL-LT differ in their microRNA profiles. In contrast to their gene expression profile, they only show slight resemblance with the microRNA profiles found in GCB- and ABC-type nodal DLBCL.     

外周血NETs、TP53、STAT3表达与弥漫性大B细胞淋巴瘤临床病理及预后的关系分析

摘要 目的：探究外周血中性粒细胞胞外诱捕网（NETs）、TP53、信号转导与转录因子3（STAT3）表达与弥漫性大B细胞淋巴瘤（DLBCL）临床病理及预后的关系。方法：选取2020年3月-2021年12月收治的71例DLBCL患者作为研究对象,抽取患者外周静脉血,采用R-CHOP方案进行治疗,记录患者外周血NETs、TP53、STAT3表达情况并分析DLBCL患者外周血NETs、TP53、STAT3表达与其临床病理及预后的关系。结果：髓细胞组织增生蛋白（MYC）阳性在TP53阳性中的占比显著高于TP53阴性,差异有统计学意义（x²=28.844,P＜0.001）;Hans分型生发中心B细胞（GCB）在STAT3阳性中的占比显著高于STAT3阴性（x²=4.331,P=0.037）,其余差异无统计学意义（P>0.05）;随访截止至2022年6月,随访时长8~28个月,71例患者中共53例缓解DLBCL患者,其余18例为R/R DLBCL患者;NETs阳性、TP53阳性、STAT3阳性患者无进展生存期（PFS）显著低于NETs阴性、TP53阴性、STAT3阴性患者,差异有统计学意义（P<0.05）且NETs阳性、TP53阳性、STAT3阳性患者存活率均低于NETs阴性、TP53阴性、STAT3阴性患者（P<0.05）;单因素分析结果显示Ann Arbor分期、NETs、TP53、STAT3为DLBCL患者的影响因素（P<0.05）;以患者预后情况（R/R DLBCL=1,缓解DLBCL=0）为因变量,将Ann Arbor分期、NETs、TP53、STAT3单因素分析有统计学意义的因素纳入COX回归模型中,结果显示：NETs、TP53、STAT3为DLBCL患者预后的危险因素（P<0.05）。结论：TP53、STAT3表达与DLBCL临床病理存在一定相关性,临床应对DLBCL患者TP53、STAT3表达情况引起重视;NETs、TP53、STAT3表达为DLBCL预后的危险因素,可作为DLBCL患者不良预后的预测指标。     

The impact of rheumatological disorders on lymphomas and myeloma: a report on risk and survival from the UK’s population-based Haematological Malignancy Research Network

BackgroundAutoimmune inflammatory disease increases the risk of diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma (MZL), but findings for other mature B-cell malignancies are equivocal. Furthermore, it has been suggested that the increase in DLBCL is due to the activated B-cell (ABC) subtype; but data on this, and the impact of inflammatory co-morbidities on survival, are sparse and contradictory.MethodsData are from an established UK population-based cohort. Patients (n = 6834) diagnosed between 01/2009 and 08/2015 are included; DLBCL (n = 1771), myeloma (n = 1760), chronic lymphocytic leukaemia (CLL, n = 1580), MZL (n = 936), and follicular lymphoma (FL, n = 787). Information on rheumatological disorders and deaths was obtained by record-linkage to nationally compiled Hospital Episode Statistics, with age-and sex-matched individuals (n = 68,340) from the same catchment population (˜4 million people) providing the comparator.ResultsSignificantly increased risks for DLBCL (OR = 2.3, 95% CI 1.8–2.8) and MZL (OR = 2.0, 95% CI 1.5–2.7) were found for those with rheumatological disorders; the site distribution of those with/without rheumatological conditions differing for DLBCL (p = 0.007) and MZL (p = 0.002). No increases in risk were observed for the remaining mature B-cell malignancies, and no associations with survival were detected for DLBCL (age-adjusted HR = 1.2, 95% CI 0.9–1.6) or MZL (age-adjusted HR = 1.0, 95% CI 0.6–1.9). Furthermore, whilst our findings provide evidence for an association with rheumatological disease severity for DLBCL, they offer little support for the notion that the association is driven by an increase in the incidence of the ABC subtype.ConclusionOur findings support the hypothesis that the chronic activation and proliferation of specific B-cell populations which characterize autoimmune disease increase the potential for the lymphomagenic events that lead to DLBCL and MZL in both males and females; but have no impact on the development of CLL, FL or MM, or on survival.     

Combining gene signatures improves prediction of breast cancer survival

Background

Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study.

Principal Findings

To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.

Conclusion

Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.