The feasibility of gene expression profiling generated in fine-needle aspiration specimens from patients with follicular lymphoma and diffuse large B-cell lymphoma

Cancer ◽  
2005 ◽  
Vol 108 (1) ◽  
pp. 10-20 ◽  
Author(s):  
Andre Goy ◽  
John Stewart ◽  
Bedia A. Barkoh ◽  
Yvonne K. Remache ◽  
Ruth Katz ◽  
...  
Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 4315-4315
Author(s):  
Andre Goy ◽  
John Stewart ◽  
Bedia Barkoh ◽  
Yvonne Remache ◽  
Susan Hart ◽  
...  

Abstract We tested the feasibility of performing gene expression profiling using amplified RNA from Fine Needle Aspiration (FNA) obtained from lymph nodes. Twenty-four samples from patients with a diagnosis of Follicular Lymphoma (FL) or Large B-Cell Lymphoma (LBCL) were obtained after informed consent. The diagnosis were performed by two pathologists and classified into two groups (10 LBCL and 14 FL) using conventional morphology and immunophenotyping. Total RNA was extracted using RNaqueous (Ambion, Austin, TX). Total RNA (100ng /per sample) was subjected to 2 cycles of standard double-stranded cDNA synthesis using Superscript Choice System (Invitrogen, Grand Island, NY) and in vitro transcription for target amplification as per GeneChip’s Eukaryotic Small Sample Target Labeling protocol (Affymetrix, Inc, Santa Clara, CA). The biotinylated cRNA from each sample was hybridized to Affymetrix HG-U133A chips. Gene expression profiling results were first analyzed by Principal Component Analysis (PCA) using a list of 146 probe sets representing 62 genes characteristic of Acivated B-Cell (ABC) or Germinal Center (GC) signature. This analysis identified 5 LBCL cases with ABC cell signature (Fig 1). Using a list of 207 probe sets representing 113 genes involved in FL transformation (K Elenitoba-Johnson, PNAS 2003), PCA analysis identified two overlapping clusters corresponding to FL and GC-DLBCL. To further improve this classification, we generated a list of 82 genes differentially expressed between FL and GC-LBCL. Using this list of genes, PCA analysis demonstrated a clear separation between FL and GC-LBCL (Fig 2). These results demonstrate that comprehensive transcription profiles can be performed in patients with NHL using RNA obtained from FNA and that one can distinguish among FL and LBCL lymphoma genetic subtypes. Figure Figure


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Selin Merdan ◽  
Kritika Subramanian ◽  
Turgay Ayer ◽  
Johan Van Weyenbergh ◽  
Andres Chang ◽  
...  

AbstractThe clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model’s biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated.


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.


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