scholarly journals Comparative analysis of gene expression platforms for cell‐of‐origin classification of diffuse large B‐cell lymphoma shows high concordance

Author(s):  
Sophia Ahmed ◽  
Paul Glover ◽  
Jan Taylor ◽  
Chulin Sha ◽  
Matthew A. Care ◽  
...  
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.


Haematologica ◽  
2017 ◽  
Vol 102 (10) ◽  
pp. e404-e406 ◽  
Author(s):  
Jean-Philippe Jais ◽  
Thierry Jo Molina ◽  
Philippe Ruminy ◽  
David Gentien ◽  
Cecile Reyes ◽  
...  

2018 ◽  
Vol 473 (3) ◽  
pp. 341-349 ◽  
Author(s):  
Sarah Reinke ◽  
Julia Richter ◽  
Falko Fend ◽  
Alfred Feller ◽  
Martin-Leo Hansmann ◽  
...  

2011 ◽  
Vol 29 (2) ◽  
pp. 200-207 ◽  
Author(s):  
Paul N. Meyer ◽  
Kai Fu ◽  
Timothy C. Greiner ◽  
Lynette M. Smith ◽  
Jan Delabie ◽  
...  

Purpose Patients with diffuse large B-cell lymphoma (DLBCL) can be divided into prognostic groups based on the cell of origin of the tumor as determined by microarray analysis. Various immunohistochemical algorithms have been developed to replicate these microarray results and/or stratify patients according to survival. This study compares some of those algorithms and also proposes some modifications. Patients and Methods Two-hundred and sixty-two cases of de novo DLBCL treated with rituximab and cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or CHOP-like therapy were examined. Results The Choi algorithm and Hans algorithm had high concordance with the microarray results. Modifications of the Choi and Hans algorithms for ease of use still retained high concordance with the microarray results. Although the Nyman and Muris algorithms had high concordance with the microarray results, each had a low value for either sensitivity or specificity. The use of LMO2 alone showed the lowest concordance with the microarray results. A new algorithm (Tally) using a combination of antibodies, but without regard to the order of examination, showed the greatest concordance with microarray results. All of the algorithms divided patients into groups with significantly different overall and event-free survivals, but with different hazard ratios. With the exception of the Nyman algorithm, this survival prediction was independent of the International Prognostic Index. Although the Muris algorithm had prognostic significance, it misclassified a large number of cases with activated B-cell type DLBCL. Conclusion The Tally algorithm showed the best concordance with the microarray data while maintaining prognostic significance and ease of use.


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

2018 ◽  
Vol 97 (12) ◽  
pp. 2363-2372 ◽  
Author(s):  
Hee Sang Hwang ◽  
Dok Hyun Yoon ◽  
Jung Yong Hong ◽  
Chan-Sik Park ◽  
Yoon Se Lee ◽  
...  

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2973-2973
Author(s):  
Karen Dybkær ◽  
Martin Bøgsted ◽  
Steffen Falgreen ◽  
Malene Krag Kjeldsen ◽  
Alexander Schmitz ◽  
...  

Abstract Background Recent findings have introduced biological classification of non-Hodgkin lymphomas as exemplified by the “activated B-cell-like” (ABC) and “germinal-center B-cell–like” (GCB) subgroups of diffuse large B-cell lymphoma (DLBCL). Aims The goal is to generate a refined cell of origin (COO) classification that includes B-cell subset associated gene signatures (BAGS) from the normal B-cell hierarchy. Methods We have combined fluorescence activated cell sorting and gene expression profiling by Gene Chip HG U133 Plus 2.0 to generate five BAGS for naïve, germinal centrocytes and centroblasts, post germinal memory B-cells, and plasmablasts of normal human tonsils. Clinical data sets are the Aalborg Project CHEPRETRO (N=89), the Lymphoma/Leukemia Molecular Profiling Project LLMPP (N=233), the International DLBCL Rituximab-CHOP Consortium MD Anderson Project IDRC (N=460), the Mayo Clinic, Brigham & Women Hospital, and Dana-Farber Cancer Institute Project MDFCI (N=88) available on the GEO website. All statistical analyses were done with R version 3.0.1 and full documentation is provided by a Sweave document. Results First, we verified the quality of the sampled B-cell subsets based on the expression patterns of differentiation molecules, transcription factors, and genes matching biological knowledge. Next, we constructed a BAGS-classifier provided by 77-115 gene probe sets, capable of assigning samples to each of the five COO subtypes. Second, we assigned individual lymphoma cases in 5 patient cohorts including a total of 1063 patient. BAGS identified COO subtypes with frequencies of 2-7 % naïve (N), 35-41 % centrocytes (CC), 18-21 % centroblasts (CB), 4-15 % memory (M), 12-18 % plasmablast (PB), and 15-16 % unclassified (UC) subtypes. The frequencies was not different between cohorts (p=0.41). Third, the BAGS subtypes was associated significantly with overall survival and time to progression for R-CHOP–treated patients in clinical cohorts from the LLMPPN (p=0.0441/0.0358) and the IDRC Program (p=0.002/8e-04). Fourth, we found a very high fraction of GCB samples to be of CC or CB subtypes. On the contrary, a major fraction of BAGS-unclassified subtypes were of the ABC class. In a multiple Cox proportional hazards model we identified the BAGS subtypes to be a prognostic variables independent of ABC/GCB subtypes but not of IPI and age. The most significant impact was observed within the GCB subclass, where the GCB-CC subtype had superior prognosis compared to the GCB-CB subtype, in accordance with individual assignments for drug specific sensitivity to hydroxydaunomycin and vincristine. Fifth, we performed genetic evaluation of the BAGS subtypes by mutation analysis within the CHEPRETRO cohort for EZH2, CD79B, and MYD88 identifying frequencies of 6.3%, 10.1% and 14.7%, respectively. The EZH2 mutation was only identified in the GCBN-CC and -CB subtypes. Mutations of CD79B and MYD88 were preferential in ABC, present in all subtypes. The CC subtype had high p53 mutation and indel frequencies, whereas the CB subtype had high Chr12q15 amplification frequencies and a complex genotype. Finally, the CC subtype expressed LMO2, NF-κB targets, CD58, Stroma1, and MHCII genes, known to have prognostic impact. Summary In summary, this study addresses an unmet medical need for a new diagnostic platform for individual DLBCL classification of “cell of origin” phenotyping attempting to design new strategies and more effective disease management. Disclosures No relevant conflicts of interest to declare.


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