Abstract
BackgroundTreatment 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.MethodsFifteen 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.ResultsPrognostic 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.ConclusionAs 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.
Highlights
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
Gene expression profiling (GEP) enables cell-of-origin classification of Diffuse large B-cell lymphoma (DLBCL) into two histologically indistinguishable molecular subclasses: the activated B-cell-like (ABC) and the germinal center B-cell-like (GCB), which reflect a subset of the normal B-cell differentiation stages
Identification of miRNAs associated with drug-specific response Triplicate dose-response experiments were analyzed using area under the dose-response curve for rituximab, cyclophosphamide, doxorubicin, and vincristine, respectively, taking the individual cell line doubling time into account [21]
Summary
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. Diffuse large B-cell lymphoma (DLBCL) is the most common type of malignant lymphoma, accounting for 30–40% of all newly diagnosed non-Hodgkin lymphomas It is a highly aggressive and heterogeneous disease with respect to clinical presentation, tumor biology, and prognosis [1]. Gene expression profiling (GEP) enables cell-of-origin classification of DLBCL into two histologically indistinguishable molecular subclasses: the activated B-cell-like (ABC) and the germinal center B-cell-like (GCB), which reflect a subset of the normal B-cell differentiation stages These subclasses differ in pathogenesis, genetic aberrations, and survival outcome [2, 3] and have entered clinical prognostic evaluation, complementing the international prognostic index (IPI), which has been the gold standard for decades [3, 4]. Identification of biomarkers predictive for single drug components of R-CHOP is of great importance when attempting to improve clinical outcome
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