Expression of DBC1 and Androgen Receptor Predict Poor Prognosis in Diffuse Large B Cell Lymphoma

BACKGROUND: Recently, deleted in breast cancer 1 (DBC1) has been suggested as a poor prognostic indicator of various human cancers and may possibly have a role as a coactivator of androgen receptor (AR). However, their roles in lymphoma are still unknown. MATERIALS AND METHODS: We investigated the effect of the expression of DBC1 and AR in diffuse large B cell lymphoma (DLBCL). Immunohistochemical expression of DBC1 and AR were evaluated in 101 DLBCL samples by tissue microarray. RESULTS: Positive expression of DBC1 and AR was seen in 73% and 70% of DLBCL, respectively. In total DLBCL patients, DBC1 and AR expression were significantly associated with high clinical stage, elevated serum lactate dehydrogenase levels, and high international prognostic index scores, and they predicted shorter overall survival (OS) and relapse-free survival (RFS) by univariate analysis. DBC1 expression was also an independent prognostic indicator by multivariate analysis (OS, P = .017; RFS, P = .004). Especially, both DBC1 and AR expression significantly correlated with shorter OS and RFS in non-germinal center B cell (non-GCB)-type DLBCL by univariate analysis. In multivariate analysis, DBC1 expression was an independent prognostic predictor for OS (P = .035) and AR expression significantly correlated with RFS (P = .005). CONCLUSION: We demonstrate that the expression of DBC1 and AR are significant prognostic indicators for DLBCL patients, especially for unfavorable non-GCB-type DLBCL.     

Expression of microRNAs in diffuse large B cell lymphoma is associated with immunophenotype, survival and transformation from follicular lymphoma

MicroRNAs are naturally occurring small RNA species that regulate gene expression and are frequently abnormally expressed in cancers. However, the role of microRNAs in lymphoma is poorly understood. Therefore, we undertook a comprehensive study of microRNA expression in two of the most common lymphomas: diffuse large B-cell lymphoma (DLBCL) ( n = 80) and follicular lymphoma (FCL) ( n = 18) using microarrays containing probes for 464 human microRNAs. Unsupervised cluster analysis revealed distinct expression patterns between these two lymphomas and specific microRNA signatures (including members of the miR-17–92 cluster) were derived that correctly predicted lymphoma type in >95% of cases. Furthermore, we identified microRNAs in de novo DLBCL ( n = 64) associated with germinal centre-like and non-germinal centre-like immunophenotypes, international prognostic index status and event-free survival in CHOP and rituximab (R)-CHOP treated patients. Despite the indolent nature of FCL a significant proportion of cases undergo high-grade transformation to more aggressive DLBCL. In order to see if transformation is associated with changes in microRNA expression we compared transformed DLBCL cases ( n = 16) with de novo DLBCL, as well as FCL cases that underwent subsequent transformation ( n = 7) with FCL cases that had not transformed at a median follow-up of 60 months ( n = 11). Differential expression of 12 microRNAs correctly predicted >85% of transformed versus de novo DLBCL cases; six microRNAs ( miR-223, 217, 222 , 221 and let-7i and 7b) were found which could similarly predict or transformation in FCL ( P < 0.05). These data suggest that microRNAs have potential as diagnostic and prognostic markers in these lymphomas and may be used to identify FCL patients at risk of high-grade transformation.     

Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.     

Meiotic and epigenetic aberrations in Dnmt3L-deficient male germ cells

The DNA methyltransferase-like protein Dnmt3L is necessary for the establishment of genomic imprints in oogenesis and for normal spermatogenesis (Bourc'his et al., 2001; Hata et al., 2002). Also, a paternally imprinted gene, H19, loses DNA methylation in Dnmt3L-/- spermatogonia (Bourc'his and Bestor, 2004; Kaneda et al., 2004). To determine the reason for the impaired spermatogenesis in the Dnmt3L-/- testes, we have carried out a series of histological and molecular studies. We show here that Dnmt3L-/- germ cells were arrested and died around the early meiotic stage. A microarray-based gene expression-profiling analysis revealed that various gonad-specific and/or sex-chromosome-linked genes were downregulated in the Dnmt3L-/- testes. In contrast, expression of retrovirus-like intracisternal A-particle (IAP) sequences was upregulated; consistent with this observation, a specific IAP copy showed complete loss of DNA methylation. These findings indicate that Dnmt3L regulates germ cell-specific gene expression and IAP suppression, which are critical for male germ cell proliferation and meiosis.     

A strategy for full interrogation of prognostic gene expression patterns: exploring the biology of diffuse large B cell lymphoma

Background

Gene expression profiling yields quantitative data on gene expression used to create prognostic models that accurately predict patient outcome in diffuse large B cell lymphoma (DLBCL). Often, data are analyzed with genes classified by whether they fall above or below the median expression level. We sought to determine whether examining multiple cut-points might be a more powerful technique to investigate the association of gene expression with outcome.

Methodology/Principal Findings

We explored gene expression profiling data using variable cut-point analysis for 36 genes with reported prognostic value in DLBCL. We plotted two-group survival logrank test statistics against corresponding cut-points of the gene expression levels and smooth estimates of the hazard ratio of death versus gene expression levels. To facilitate comparisons we also standardized the expression of each of the genes by the fraction of patients that would be identified by any cut-point. A multiple comparison adjusted permutation p-value identified 3 different patterns of significance: 1) genes with significant cut-point points below the median, whose loss is associated with poor outcome (e.g. HLA-DR); 2) genes with significant cut-points above the median, whose over-expression is associated with poor outcome (e.g. CCND2); and 3) genes with significant cut-points on either side of the median, (e.g. extracellular molecules such as FN1).

Conclusions/Significance

Variable cut-point analysis with permutation p-value calculation can be used to identify significant genes that would not otherwise be identified with median cut-points and may suggest biological patterns of gene effects.     

Long non-coding RNA as a novel biomarker and therapeutic target in aggressive B-cell non-Hodgkin lymphoma: A systematic review

Cancer initiation and progression have been associated with dysregulated long non-coding RNA (lncRNA) expression. However, the lncRNA expression profile in aggressive B-cell non-Hodgkin lymphoma (NHL) has not been comprehensively characterized. This systematic review aims to evaluate the role of lncRNAs as a biomarker to investigate their future potential in the diagnosis, real-time measurement of response to therapy and prognosis in aggressive B-cell NHL. We searched PubMed, Web of Science, Embase and Scopus databases using the keywords “long non-coding RNA”, “Diffuse large B-cell lymphoma”, “Burkitt's lymphoma” and “Mantle cell lymphoma”. We included studies on human subjects that measured the level of lncRNAs in samples from patients with aggressive B-cell NHL. We screened 608 papers, and 51 papers were included. The most studied aggressive B-cell NHL was diffuse large B-cell lymphoma (DLBCL). At least 79 lncRNAs were involved in the pathogenesis of aggressive B-cell NHL. Targeting lncRNAs could affect cell proliferation, viability, apoptosis, migration and invasion in aggressive B-cell NHL cell lines. Dysregulation of lncRNAs had prognostic (e.g. overall survival) and diagnostic values in patients with DLBCL, Burkitt's lymphoma (BL), or mantle cell lymphoma (MCL). Furthermore, dysregulation of lncRNAs was associated with response to treatments, such as CHOP-like chemotherapy regimens, in these patients. LncRNAs could be promising biomarkers for the diagnosis, prognosis and response to therapy in patients with aggressive B-cell NHL. Additionally, lncRNAs could be potential therapeutic targets for patients with aggressive B-cell NHL like DLBCL, MCL or BL.     

Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes

The positive and negative predictive values are standard ways of quantifying predictive accuracy when both the outcome and the prognostic factor are binary. Methods for comparing the predictive values of two or more binary factors have been discussed previously (Leisenring et al., 2000, Biometrics 56, 345-351). We propose extending the standard definitions of the predictive values to accommodate prognostic factors that are measured on a continuous scale and suggest a corresponding graphical method to summarize predictive accuracy. Drawing on the work of Leisenring et al. we make use of a marginal regression framework and discuss methods for estimating these predictive value functions and their differences within this framework. The methods presented in this paper have the potential to be useful in a number of areas including the design of clinical trials and health policy analysis.     

Genomic profiling using array comparative genomic hybridization define distinct subtypes of diffuse large b-cell lymphoma: a review of the literature

ABSTRACT: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin Lymphoma comprising of greater than 30 % of adult non-Hodgkin Lymphomas. DLBCL represents a diverse set of lymphomas, defined as diffuse proliferation of large B lymphoid cells. Numerous cytogenetic studies including karyotypes and fluorescent in situ hybridization (FISH), as well as morphological, biological, clinical, microarray and sequencing technologies have attempted to categorize DLBCL into morphological variants, molecular and immunophenotypic subgroups, as well as distinct disease entities. Despite such efforts, most lymphoma remains undistinguishable and falls into DLBCL, not otherwise specified (DLBCL-NOS). The advent of microarray-based studies (chromosome, RNA, gene expression, etc) has provided a plethora of high-resolution data that could potentially facilitate the finer classification of DLBCL. This review covers the microarray data currently published for DLBCL. We will focus on these types of data; 1) array based CGH; 2) classical CGH; and 3) gene expression profiling studies. The aims of this review were three-fold: (1) to catalog chromosome loci that are present in at least 20 % or more of distinct DLBCL subtypes; a detailed list of gains and losses for different subtypes was generated in a table form to illustrate specific chromosome loci affected in selected subtypes; (2) to determine common and distinct copy number alterations among the different subtypes and based on this information, characteristic and similar chromosome loci for the different subtypes were depicted in two separate chromosome ideograms; and, (3) to list re-classified subtypes and those that remained indistinguishable after review of the microarray data. To the best of our knowledge, this is the first effort to compile and review available literatures on microarray analysis data and their practical utility in classifying DLBCL subtypes. Although conventional cytogenetic methods such as Karyotypes and FISH have played a major role in classification schemes of lymphomas, better classification models are clearly needed to further understanding the biology, disease outcome and therapeutic management of DLBCL. In summary, microarray data reviewed here can provide better subtype specific classifications models for DLBCL.     

Linear regression and two-class classification with gene expression data

MOTIVATION: Using gene expression data to classify (or predict) tumor types has received much research attention recently. Due to some special features of gene expression data, several new methods have been proposed, including the weighted voting scheme of Golub et al., the compound covariate method of Hedenfalk et al. (originally proposed by Tukey), and the shrunken centroids method of Tibshirani et al. These methods look different and are more or less ad hoc. RESULTS: We point out a close connection of the three methods with a linear regression model. Casting the classification problem in the general framework of linear regression naturally leads to new alternatives, such as partial least squares (PLS) methods and penalized PLS (PPLS) methods. Using two real data sets, we show the competitive performance of our new methods when compared with the other three methods.     

Prediction of treatment response using gene expression profiles

This paper concerns prediction of clinical outcome from gene expression profiles using work in a different area, nonlinear system identification. In particular, the approach can predict long-term treatment response from data of a landmark article by Golub et al. (Golub, T. R.; Slonim, D. K.; Tamayo, P.; Huard, C.; Gaasenbeek, M.; Mesirov, J. P. et al. Science 1999, 286, 531-537) that has not previously been achieved with these data. The present paper shows that, for these data, gene expression profiles taken at time of diagnosis of acute myeloid leukemia contain information predictive of eventual response to chemotherapy. This was not evident in previous work; indeed, the Golub et al. article did not find a set of genes strongly correlated with clinical outcome. However, the present approach can accurately predict outcome class of gene expression profiles even when the genes do not have large differences in expression levels between the classes.     

C-MYC Aberrations as Prognostic Factors in Diffuse Large B-cell Lymphoma: A Meta-Analysis of Epidemiological Studies

Objectives

Various studies have investigated the prognostic value of C-MYC aberrations in diffuse large B-cell lymphoma (DLBCL). However, the role of C-MYC as an independent prognostic factor in clinical practice remains controversial. A systematic review and meta-analysis were performed to clarify the clinical significance of C-MYC aberrations in DLBCL patients.

Methods

The pooled hazard ratios (HRs) for overall survival (OS) and event-free survival (EFS) were calculated as the main effect size estimates. The procedure was conducted according to the Cochrane handbook and PRISMA guidelines, including the use of a heterogeneity test, publication bias assessment, and meta-regression, as well as subgroup analyses.

Results

Twenty-four eligible studies enrolling 4662 patients were included in this meta-analysis. According to the nature of C-MYC aberrations (gene, protein, and mRNA), studies were divided into several subgroups. For DLBCL patients with C-MYC gene abnormalities, the combined HR was 2.22 (95% confidence interval, 1.89 to 2.61) for OS and 2.29 (95% confidence interval, 1.81 to 2.90) for EFS, compared to patients without C-MYC gene abnormalities. For DLBCL patients with overexpression of C-MYC protein and C-MYC mRNA, pooled HRs for OS were 2.13 and 1.62, respectively. C-MYC aberrations appeared to play an independent role among other well-known prognostic factors in DLBCL. Addition of rituximab could not overcome the inferior prognosis conferred by C-MYC.

Conclusion

The present systematic review and meta-analysis confirm the prognostic value of C-MYC aberrations. Screening of C-MYC should have definite prognostic meaning for DLBCL stratification, thus guaranteeing a more tailored therapy.     

Blood Lymphocyte-to-Monocyte Ratio Identifies High-Risk Patients in Diffuse Large B-Cell Lymphoma Treated with R-CHOP

Background

Recent research has shown a correlation between immune microenvironment and lymphoma biology. This study aims to investigate the prognostic significance of the immunologically relevant lymphocyte-to-monocyte ratio (LMR), in diffuse large B-cell lymphoma (DLBCL) in the rituximab era.

Methodology/Principal Findings

We analyzed retrospective data from 438 newly diagnosed DLBCL patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. We randomly selected 200 patients (training set) to generate a cutoff value for LMR by receiver operating characteristic (ROC) curve analysis. LMR was then analyzed in a testing set (n = 238) and in all patients (n = 438) for validation. The LMR cutoff value for survival analysis determined by ROC curve in the training set was 2.6. Patients with low LMR tended to have more adverse clinical characteristics. Low LMR at diagnosis was associated with worse survival in DLBCL, and could also identify high-risk patients in the low-risk IPI category. Multivariate analysis identified LMR as an independent prognostic factor of survival in the testing set and in all patients.

Conclusions/Significance

Baseline LMR, a surrogate biomarker of the immune microenvironment, is an effective prognostic factor in DLBCL patients treated with R-CHOP therapy. Future prospective studies are required to confirm our findings.     

CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data

MOTIVATION: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction. RESULTS: Validity of the method is demonstrated on two publicly available datasets on diffuse large B-cell lymphoma (DLBCL) and on adenocarcinoma of the lung. The method successfully identifies long and short survivors, with high sensitivity and specificity. We compare our method with two alternative methods from the literature, demonstrating superior results of our approach. In addition, we show that CASPAR can further refine predictions made using clinical scoring systems such as the International Prognostic Index (IPI) for DLBCL and clinical staging for lung cancer, thus providing an additional tool for the clinician. An analysis of the genes identified confirms previously published results, and furthermore, new candidate genes correlated with survival are identified.     

The Expression of CD30 Based on Immunohistochemistry Predicts Inferior Outcome in Patients with Diffuse Large B-Cell Lymphoma

The prognostic value of CD30 expression indiffuse large B-cell lymphoma (DLBCL)remains controversial. Herein, we performed this retrospective study to investigate the clinical and prognostic significance of CD30 expression in patients with DLBCL.Among all the 146 patients, the expression of CD30 was observed in 23 cases (15.7%).The DLBCL patients with CD30 expression showed more likely to present B symptoms, bone marrow involvement, non-germinal centre B-cell-like (Non-GCB) DLBCL, BCL-2 and Ki-67overexpression(p<0.05). Patients with CD30 expression showed significantly poor overall and event-free survivalcompared with CD30 negative patients(p = 0.031 and 0.041, respectively), especially those with the high intermediate/high-risk international prognostic index (IPI)(p = 0.001 and 0.007, respectively). The prognostic value of CD30expression retained in DLBCL patients treated with eitherCHOP (cyclophosphamide, doxorubicin, vincristine,prednisone) or R-CHOP(rituximab+CHOP). The multivariate analysisrevealed that the expression of CD30 remained an unfavorable factor for both overall and event-free survival (p = 0.001 and 0.002, respectively).In conclusion, these data suggest that CD30 is expressed predominantly in Non-GCBDLBCL. The expression of CD30 implied poor outcomein DLBCL patientstreated with either CHOP or R-CHOP, especially those with the high intermediate/high-risk IPI, possibly indicating that anti-CD30 monoclonal antibody could be of clinical interest.     

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.