scholarly journals A Microarray Platform-Independent Classification Tool for Cell of Origin Class Allows Comparative Analysis of Gene Expression in Diffuse Large B-cell Lymphoma

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e55895 ◽  
Author(s):  
Matthew A. Care ◽  
Sharon Barrans ◽  
Lisa Worrillow ◽  
Andrew Jack ◽  
David R. Westhead ◽  
...  
2019 ◽  
Vol 37 ◽  
pp. 353-353
Author(s):  
M. Rodriguez ◽  
I. Fernandez-Miranda ◽  
R. Mondejar ◽  
J. Capote ◽  
S. Rodriguez-Pinilla ◽  
...  

Author(s):  
David W. Scott

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma worldwide and consists of a heterogeneous group of cancers classified together on the basis of shared morphology, immunophenotype, and aggressive clinical behavior. It is now recognized that this malignancy comprises at least two distinct molecular subtypes identified by gene expression profiling: the activated B-cell-like (ABC) and the germinal center B-cell-like (GCB) groups—the cell-of-origin (COO) classification. These two groups have different genetic mutation landscapes, pathobiology, and outcomes following treatment. Evidence is accumulating that novel agents have selective activity in one or the other COO group, making COO a predictive biomarker. Thus, there is now a pressing need for accurate and robust methods to assign COO, to support clinical trials, and ultimately guide treatment decisions for patients. The “gold standard” methods for COO are based on gene expression profiling (GEP) of RNA from fresh frozen tissue using microarray technology, which is an impractical solution when formalin-fixed paraffin-embedded tissue (FFPET) biopsies are the standard diagnostic material. This review outlines the history of the COO classification before examining the practical implementation of COO assays applicable to FFPET biopsies. The immunohistochemistry (IHC)-based algorithms and gene expression–based assays suitable for the highly degraded RNA from FFPET are discussed. Finally, the technical and practical challenges that still need to be addressed are outlined before robust gene expression–based assays are used in the routine management of patients with DLBCL.


2017 ◽  
Vol 179 (1) ◽  
pp. 116-119 ◽  
Author(s):  
Monika Szczepanowski ◽  
Jonas Lange ◽  
Christian W. Kohler ◽  
Neus Masque-Soler ◽  
Martin Zimmermann ◽  
...  

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 622-622 ◽  
Author(s):  
Ash A Alizadeh ◽  
Andrew J Gentles ◽  
Alvaro J. Alencar ◽  
Holbrook E Kohrt ◽  
Roch Houot ◽  
...  

Abstract Abstract 622 Background: Several gene-expression signatures are predictive of prognosis in diffuse large-B-cell lymphoma (DLBCL), but the lack of practical methods for a genome-scale analysis has restricted their routine clinical applicability. Methods: We studied genes whose expression had been reported to predict survival in DLBCL, attempting to validate genes and prognostic models with robust survival associations that are amenable to rapid diagnostic testing. Results: Among a previously described set of 6-genes shown to predict survival independent of measurement platform or therapy era (Lossos, et al. 2004 NEJM 350:1828), we identified LMO2 as the single gene with strongest independent prognostic value in 3 independent cohorts of patients with DLBCL. To assess the independent contribution of other genes in predicting survival, using existing microarray gene expression data (Lenz, et al. 2008 NEJM 359:2313), we evaluated all pairwise models that included LMO2 toward construction of a robust bivariate survival predictor. Among 54674 possible models, one combining expression of LMO2 with TNFRSF9 (encoding 4-1BB, also known as CD137) emerged as among the best in cross-validation when assessed in training (n=233) and test (n=181) cohorts. This bivariate predictor remained prognostic in both CHOP (p=1.7e-6) and R-CHOP (p=6.5e-8) therapy eras, was highly independent of the International Prognostic Index, Cell-of-Origin classification, 6-gene predictive model, ‘stromal' model, and added significantly to their prognostic power. While LMO2 expression was highly restricted to tumor cells and was linked to Cell-of-Origin (GCB, p=2.2e-16), TNFRSF9 expression was highest in non-tumor cells (P=0.02), particularly in an activated subset of infiltrating CD8 T-cells. To validate this bivariate model and devise a practical diagnostic assay, we used quantitative real-time polymerase-chain-reaction to measure the expression of LMO2 and TNFRSF9 as well as other components of the 6-gene model (BCL2, BCL6, FN1, CCL3, and CCND2) in diagnostic formalin fixed and paraffin-embedded samples of lymphoma from an independent set of 147 patients with de novo DLBCL treated with R-CHOP. The IPI distribution for these patients was: 0-1 factor (n=70), 2 factors (n=40), 3 factors (n=26), ≥4 factors (n=11). In univariate and multivariate analyses of this independent cohort, LMO2 and TNFRSF9 expression remained individually prognostic of both progression free and overall survival. The bivariate model combining LMO2/TNFRSF9 could be used to stratify distinct risk groups for overall survival (p=0.004), and remained independent of IPI. Conclusion: Measurement of the expression of two genes integrating contributions of tumor cells and the tumor microenvironment is sufficient to predict overall survival in patients with DLBCL treated with R-CHOP. Disclosures: Advani: Seattle Genetics, Inc.: Research Funding.


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