scholarly journals Incorporation of digital gene expression profiling for cell-of-origin determination (Lymph2Cx testing) into the routine work-up of diffuse large B cell lymphoma

2019 ◽  
Vol 12 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Ryan S. Robetorye ◽  
Colleen A. Ramsower ◽  
Allison C. Rosenthal ◽  
Tameson K. Yip ◽  
Amy J. Wendel Spiczka ◽  
...  
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.


Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3652-3652
Author(s):  
Luis F. Porrata ◽  
Kay M. Ristow ◽  
Joseph P. Colgan ◽  
Thomas M. Habermann ◽  
Thomas E. Witzig ◽  
...  

Abstract Abstract 3652 Purpose: Gene expression profiling has shown biologically distinct cell of origin categories for diffuse large B-cell lymphoma (DLBCL): germinal center B-cell like (GCB), and activated-B-cell like (ABC). GCB, DLBCL patients experienced better clinical outcomes compared with ABC, DLBCL patients. However, gene microarray technology is not broadly available in a non-research setting. Absolute lymphocyte count (ALC) at diagnosis is a prognostic factor for survival in DLBCL. Recently, gene-expression profiling and immunohistochemistry-based studies in non-Hosgkin's lymphoma demonstrate the important role of monocytes and their progeny, particularly lymphoma-associated macrophages, in promoting lymphomagenesis. thus, we studied if the peripheral blood absolute lymphocyte count/absolute monocyte count at diagnosis (ALC/AMC-DX), as a surrogate biomarker of the host response against the cell of origin in DLBCL, affects survival in DLBCL. Patients and Methods: We perfromed a retrospective analysis of the association between ALC/AMC-DX and survival in 131 consecutive DLBCL patients that were followed at Mayo Clinic from 2004 to 2010, with available tissue immunostaining for CD10, BCL6, MUM 1, and BCL 2 (Hans' algorithm) to identified GCB and ABC DLBCL patients. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were used for ALC/AMC-DX cut-off value determientaion and proportional-hazards models wee used to compare survival based on the ALC/AMC-DX. Results: The cohort included 91 (70%) GCb DLBCL patients and 40 (30%) ABC, DLBCL patients. All patients were treated with rituximab, cyclosphosphamide, adriamycin, vincristine, and prednisone (R-CHOP-21). The median follow-up period was 2.1 years (range 0.1–6.9 years). An ALC/AMC-DX >= 1.5 was the best cut-off value for survival with an empircal AUC of 0.83 (95% CI, 0.77 to 0.89), a sensitivity of 83% (95% CL, 72% to 92%) and specificity of 79% (95%CI, 72% to 85%). The cut-off value for ALC/AMC-DX >= 1.5 was validated by the k-fold cross validation method, showing a cross validation ROC with an AUC of 0.89 (95%CI, 0.80 to 0.95) for an ALC/AMC-DX >=1.5. Using Kaplan-Meier analysis, the overall survival (OS), defined as the time from diagnosis to last follow-up or death due to any cause; and progression-free survival (PFS), defined as the time from diagnosis to death of any cause, relapse, progression, or last follow-up, based on ALC/AMC-DX >= 1.5 were evaluated. patients with an ALC/AMC-DX >=1.5 experienced a superior OS and PFS compared with patients with an ALC/AMC-DX < 1.5: [OS: median was not reached vs 20.8 months, 5-years OS rates of 83% (95%CI, 70% to 95%) vs 36% (95%CI, 20% to 55%), p < 0.0001, respectively; and PFS: median was not reached vs 10.8 months, 5-years PFS rates of 70% (95%CI, 58% to 88%) vs 28% (95% CI, 17% to 46%), p < 0.0001, respectively]. Multivariate Cox stepwise regression analysis identified cell of origin, International Prognostic Index (IPI) and ALC/AMC-DX as the strongest predictors for OS and PFS, with ALC/AMC-DX out-performing cell of origin and IPI: OS [HR = 0.17, 95%CI, 0.07 to 0.36, p < 0.0001] and PFS [HR=0.21, 955CI, 0.11 to 0.39, p < 0.001]. conclusion: ALC/AMC-DX is independent of the cell of origin to predict survival and provides a single biomarker to predict clinical outcomes in DLBCL. Confirmatory studies are required. Disclosures: No relevant conflicts of interest to declare.


2017 ◽  
Vol 182 (3) ◽  
pp. 453-456 ◽  
Author(s):  
Luciano Cascione ◽  
Andrea Rinaldi ◽  
Annalisa Chiappella ◽  
Ivo Kwee ◽  
Giovannino Ciccone ◽  
...  

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2935-2935
Author(s):  
Kerstin Wenzl ◽  
Bryce Manso ◽  
Yan W Asmann ◽  
Matthew J Maurer ◽  
Michelle Manske ◽  
...  

Abstract Gene expression profiling has shown that diffuse large B-cell lymphoma (DLBCL) clusters into three major subtypes based on similarity in expression patterns to their cell of origin (COO): germinal center B-cell-like (GCB-DLBCL), activated B-cell-like DLBCL (ABC-DBLCL) and primary mediastinal B-cell lymphoma (PMBCL). These subtypes of DLBCLs are associated with distinctly different overall survival rates after standard immunochemotherapy. However, clinical and prognostic heterogeneity remains within COO subsets and strategies are needed to further stratify patients to identify and target high-risk subsets. A comprehensive genomic analysis of COO on a clinically defined set of DLBCL cases has not been performed and the aim of this study was to use whole-exome sequencing (WES) data from 58 paired tumor-normal DLBCL samples to assess association of known DLBCL genomic alterations with cell COO as well as for identification of novel and relevant genetic biomarkers. To investigate genomic alterations associated with DLBCL subsets, we analyzed WES and genome wide copy number data from 58 paired tumor-normal DLBCL tumors. Gene expression profiling or Hans classification was performed to determine DLBCL COO subtype; 31 patients were classified as GCB and 27 as non-GCB. The WES data were used to 1) assess the association of known DLBCL genomic alterations with COO, and 2) identify novel alternations associated with COO. Statistical analysis was performed and the data were ranked by significance (p≤ 0.05) within each DLBCL subtype. In total, 45 genomic abnormalities were analyzed for their association with either GCB, non-GCB or both. Mutations in CREBBP, EZH2, MEF2B, FOXO1 and REL have also been reported as GCB driver mutations and we observed GCB patients with these mutations, but the mutation clustering was not wholly associated with GCB. MYC double-hits were exclusively found in the GCB-subtype group. For the non-GCB cases we found that mutations in MAP2K3 and MYD88 were significantly associated with this subtype(p< 0.05). In addition to mutational patterns, we identified several copy number alterations (CNA) across both groups. Chromosomal losses in GCB patients were found at chromosomes 10q11.21-10q24.23, 4q12-4q35.2, 3q12.1-3q29, 4p12-4p16.3, 10p11.21-10p15.3, and 14q11.2-14q24.3 whereas gains were localized to 7q11.1-7q36.3, 7p11.2-7p22.3, and 1q21-1q32.1 (p < 0.05). No CNA was observed to directly associate with non-GCB patients, however, a loss at 9p21 and gains at 9q24.1 and 18q21.33 trended with the non-GCB subtype, supporting previous reports. Loss at 10q23.31 or a gain in 2p13-2p12 have been reported as being specific for GCB and our data confirmed the association of 10q23.31 with GCB while a gain at 2p13-2p12 (REL)was found in both subtypes. To further understand genomic differences between DLBCL subtypes, we evaluated the relative percentage of each genomic feature. 18/45 (40%) were only observed in GCB patients whereas 2/45 (5%) were specific to the non-GCB subtype. The majority (25/45, 55%) overlapped between the two subtypes. Throughout our analysis we noted that 7 non-GCB cases lacked any of the driver mutations analyzed in the study. While all cases carried mutations, they consisted of low frequency mutations that were not specifically associated with COO. 2/7 cases had a gain at 9p24.1 that included CD274 and JAK2. Because 9p24.1 gains have not been fully defined in DLBCL, we reviewed all cases and identified 4 (7%) with a 9p24.1 gain, 3 of which were non-GCB and 1 GCB. One non-GCB case was EBV+ and none of the cases showed evidence of PMBCL. Of the 9p24.1 cases, three had RNAseq data available and we found that PDL1 and JAK2 expression was elevated (12 fold p< 0.01 and 7 fold p< 0.01, respectively) when compared to the 9p24.1 normal cases (n=32). While outcome was not the focus of the study, we did note that 6/7 cases that lacked driver mutations achieved event free survival at 24 months (EFS24). Taken together, this analysis has further characterized the genetic profile of each COO subtype and has identified novel GCB CNAs which require independent replication. Additionally, we identify a subgroup of non-GCB DLBCL patients that do not harbor known driver mutations and require further genomic study to better resolve the biology of these tumors. Together, these data provide insight on the genetic heterogeneity of DLBCL and identify genetic variants that may inform subtype specific therapy. Disclosures Nowakowski: Morphosys: Research Funding; Celgene: Research Funding; Bayer: Consultancy, Research Funding. Rimsza:NCI/NIH: Patents & Royalties: L.M. Rimsza is a co-inventor on a provisional patent, owned by the NCI of the NIH, using Nanostring technology for determining cell of origin in DLBCL..


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