Accurate classification of diffuse large B-cell lymphoma into germinal center and activated B-cell subtypes using a nuclease protection assay on formalin-fixed, paraffin-embedded tissues

Abstract

Classification of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin (COO) subtypes based on gene expression profiles has well-established prognostic value. These subtypes, termed germinal center B cell (GCB) and activated B cell (ABC) also have different genetic alterations and overexpression of different pathways that may serve as therapeutic targets. Thus, accurate classification is essential for analysis of clinical trial results and planning new trials by using targeted agents. The current standard for COO classification uses gene expression profiling (GEP) of snap frozen tissues, and a Bayesian predictor algorithm. However, this is generally not feasible. In this study, we investigated whether the qNPA technique could be used for accurate classification of COO by using formalin-fixed, paraffin-embedded (FFPE) tissues. We analyzed expression levels of 14 genes in 121 cases of R-CHOP-treated DLBCL that had previously undergone GEP by using the Affymetrix U133 Plus 2.0 microarray and had matching FFPE blocks. Results were evaluated by using the previously published algorithm with a leave-one-out cross-validation approach. These results were compared with COO classification based on frozen tissue GEP profiles. For each case, a probability statistic was generated indicating the likelihood that the classification by using qNPA was accurate. When data were dichotomized into GCB or non-GCB, overall accuracy was 92%. The qNPA technique accurately categorized DLBCL into GCB and ABC subtypes, as defined by GEP. This approach is quantifiable, applicable to FFPE tissues with no technical failures, and has potential for significant impact on DLBCL research and clinical trial development.

Figure 1

Schematic showing qNPA technology. In the first part of the assay, the Lysis Buffer is added to patient sample to disrupt the sample. Next detection probes, specifically designed for the genes of interest, hybridize to all mRNA. An S1 nuclease destroys non-specific RNA leaving a stoichiometric amount of target-mRNA/probe duplexes. Base hydrolysis releases probe from duplexes. The second part of the assay uses a universal array of 16 different anchor oligonucleotides printed into the bottom of each well of a 96-well plate. Custom programming linkers are first added, each designed to hybridize to a specific anchor with one end and capture a gene-specific nuclease protection probe complimentary to the mRNA of interest on the other. The surviving probes from the first part of the assay are then transferred to an ArrayPlate^R well, detection linker added, and both probes and detection linkers captured onto the array. HRP-labeled detection probe is added followed by chemiluminescent substrate. Finally, the plate is imaged to measure the expression levels.

Figure 2

Two-way comparisons of probability scores derived from different models for ABC and GCB distinction. On the left, 2 sets of probability scores derived from Affymetrix data are shown, one with 12 genes and the other with 187 genes with a linear regression R-scale value of 0.94 and p-value of < 0.0001. On the right, is a comparison of the predictor scores from the 12 gene model using qNPA versus the 187 gene model using Affymetrix with a linear regression R-scale value of 0.89 and p-value of < 0.0001. The amount of scatter in the plots is similar indicating that most of the variability in the results was due to the use of 12 (vs. 187) genes rather than a difference in the technical platform. GCB = germinal center B cell, ABC = activated B cell, Affy = Affymetrix, Uncl = unclassifiable