Abstract
Diffuse large B-cell lymphoma (DLBCL) is commonly classified by gene expression profiling according to its cell of origin (COO) into activated B-cell (ABC)-like and germinal center B-cell (GCB)-like subgroups. Here we report the application of label-free nano-liquid chromatography - Sequential Window Acquisition of all THeoretical fragment-ion spectra – mass spectrometry (nanoLC-SWATH-MS) to the COO classification of DLBCL in formalin-fixed paraffin-embedded (FFPE) tissue. To generate a protein signature capable of predicting Affymetrix-based GCB scores, the summed log2-transformed fragment ion intensities of 780 proteins quantified in a training set of 42 DLBCL cases were used as independent variables in a penalized zero-sum elastic net regression model with variable selection. The eight-protein signature obtained showed an excellent correlation (r = 0.873) between predicted and true GCB scores and yielded only 9 (21.4%) minor discrepancies between the three classifications: ABC, GCB, and unclassified. The robustness of the model was validated successfully in two independent cohorts of 42 and 31 DLBCL cases, the latter cohort comprising only patients aged >75 years, with Pearson correlation coefficients of 0.846 and 0.815, respectively, between predicted and NanoString nCounter based GCB scores. We further show that the 8-protein signature is directly transferable to both a triple quadrupole and a Q Exactive quadrupole-Orbitrap mass spectrometer, thus obviating the need for proprietary instrumentation and reagents. This method may therefore be used for robust and competitive classification of DLBCLs on the protein level.
Highlights
Diffuse large B-cell lymphoma (DLBCL) is commonly classified by gene expression profiling according to its cell of origin (COO) into activated B-cell (ABC)-like and germinal center B-cell (GCB)-like subgroups
Using a reference point insensitive regularized regression method with variable selection[18,19], we report here the identification of an eight-protein signature that yields excellent correlation with GCB predictor scores derived from both Affymetrix GeneChip and NanoString nCounter gene expression data and carries the advantage of being transferable to other analytical platforms as long as the abundance of the signature proteins can be measured within the linear dynamic ranges of the respective methods
Albeit slightly modified protocol[17,22], a single 10-μm thick formalin-fixed paraffin-embedded (FFPE) tissue section per DLBCL case was first deparaffinized in xylene and rehydrated in ethanol, before proteins were extracted and subjected to gel-assisted tryptic digestion according to Fischer and Kessler[23]
Summary
Diffuse large B-cell lymphoma (DLBCL) is commonly classified by gene expression profiling according to its cell of origin (COO) into activated B-cell (ABC)-like and germinal center B-cell (GCB)-like subgroups. We report the application of label-free nano-liquid chromatography - Sequential Window Acquisition of all THeoretical fragment-ion spectra – mass spectrometry (nanoLC-SWATH-MS) to the COO classification of DLBCL in formalin-fixed paraffin-embedded (FFPE) tissue. SWATH-MS combines the advantages of untargeted shotgun proteomics by covering hundreds to thousands of protein groups in a single analysis and selected reaction monitoring mass spectrometry by yielding highly reproducible and consistent data This was recently demonstrated for a comparative analysis of cryopreserved and FFPE tissue sections of Burkitt’s lymphoma and DLBCL with more than 90% of the proteins that differed significantly between these two lymphomas showing the same direction of regulation regardless of tissue preservation[17]. Using a reference point insensitive regularized regression method with variable selection[18,19], we report here the identification of an eight-protein signature that yields excellent correlation with GCB predictor scores derived from both Affymetrix GeneChip and NanoString nCounter gene expression data and carries the advantage of being transferable to other analytical platforms as long as the abundance of the signature proteins can be measured within the linear dynamic ranges of the respective methods
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