MicroRNAs associated to single drug components of R-CHOP identifies diffuse large B-cell lymphoma patients with poor outcome and adds prognostic value to the international prognostic index

Abstract

Background: Treatment resistance is a major clinical challenge of diffuse large B-cell lymphoma (DLBCL) where approximately 40% of the patients have refractory disease or relapse. Since DLBCL is characterized by great clinical and molecular heterogeneity, the purpose of the present study was to investigate whether miRNAs associated to single drug components of R-CHOP can improve robustness of individual markers and serve as a prognostic classifier. Methods: Fifteen DLBCL cell lines were tested for sensitivity towards single drug compounds of the standard treatment R-CHOP: rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). For each drug, cell lines were ranked using the area under the dose-response curve and grouped as either sensitive, intermediate or resistant. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs associated to growth response after exposure towards single drugs of R-CHOP. Using the Affymetrix HG-U133 platform, expression levels of miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers predicting disease progression were build using multiple Cox regression or random survival forest. Classifiers were validated and ranked by repeated cross-validation. Results: Prognostic accuracies were assessed by Brier Scores and time-varying area under the ROC curves, which revealed better performance of multivariate Cox models compared to random survival forest models. The Cox model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high-and low-risk of disease progression. In addition, combination of the Cox miRNA-panel and IPI substantially increased prognostic performance in GCB classified patients. Conclusion: As a proof of concept, we found that expression data of drug associated miRNAs display prognostic utility and adding these to IPI improves prognostic stratification of GCB-DLBCL patients treated with R-CHOP.

TNB-486 induces potent tumor cell cytotoxicity coupled with low cytokine release in preclinical models of B-NHL

“…At an effector to target ratio (E:T) of 5:1, TNB-486 induced concentration-dependent TDCC of multiple CD19+ tumor cell lines derived from leukemia or lymphoma origin, namely, Ramos, Daudi, Raji, Nalm-6, WSU-DLCL2, SU-DHL-4, or RI-1 (or RIVA). Among these, RI-1 is a DLBCL cell line known to be resistant to rituximab, 26 and WSU-DLCL2 is derived from a patient with aggressive lymphoma and is resistant and refractory to chemotherapy agents. 27 The maximum average percent lysis induced by TNB-486 ranged from 35% to 82% between the different tumor cell lines ( Figure 4c ).…”

A bendamustine resistance gene signature in diffuse large B-cell lymphoma and multiple myeloma

“…Table 1. Information on cell lines used in this study [34,38,39] Cell culture supplements: The bendamustine REGS is based on regularized multivariate regression analysis with cross-validated tuning parameters and can assign probability of resistance to a specific drug utilizing GEP and doseresponse data from cell lines [38] . For the development of the bendamustine REGS, we followed a previously described approach [35] .…”

Long Non-Coding RNAs in Diffuse Large B-Cell Lymphoma

“…The cell lines were cultured under standard conditions at 37 °C in a humidified atmosphere of 95% air and 5% CO2 with RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (P/S) for no longer than 20 passages. All cell lines were authenticated by DNA barcoding, as previously described [ 95 , 96 ].…”

“… RNA-seq analysis of three rituximab-resistant and three rituximab-sensitive DLBCL cell lines. ( A ) Sample information for the DLBCL cell lines used in this study [ 95 , 96 ]. ( B ) Volcano plot of RNA-seq data.…”