Platform independent protein-based cell-of-origin subtyping of diffuse large B-cell lymphoma in formalin-fixed paraffin-embedded tissue

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 log 2-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. Diffuse large B-cell lymphoma (DLBCLs) is commonly grouped into three distinct molecular subtypes based on the putative cell of origin (COO): the activated B-cell-like (ABC), the germinal B-cell-like (GCB), and the unclassifiable subtype as defined by array-based gene expression profiling 1. Numerous studies have confirmed the distinct differences in biology and clinical behavior of these subtypes and, therefore, determination of COO has become an integral component in the diagnosis of DLBCL, providing a basis for targeted-treatment stratification 2. GCB-like DLBCL has shown consistently better response to treatment with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) without and with the addition of rituximab (R-CHOP). However, the requirement for high quality tumor RNA from snap frozen DLBCL biopsies represented a significant obstacle to

Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping

“…Large-scale proteomics enabled clear separation of cell lines representing GCB and ABC subtypes [6,7]. Follow-up clinical studies on FFPE tumor tissues [8][9][10] have provided promising results. Nevertheless, the advantage of proteome profiling over gene expression profiling based on FFPE tumor tissue is still unclear.…”

Proteomic analysis in diffuse large B-cell lymphoma identifies dysregulated tumor microenvironment proteins in non-GCB/ABC subtype patients

“…Furthermore, for the six most important proteins in their GCB patients, three of them were also upregulated in the GCB subgroup in this study. In a recent proteomic study [12], which also could separate ABC and GCB patients by their global protein profile in FFPE samples (n ¼ 42), we could significantly reproduce 6 of 8 proteins in their separating 8-protein signature. Three other smaller studies have also performed proteomic COO analyses in DLBCL patients [9][10][11]; however, they could not reproduce well-known COO proteins and even if there was some minor overlap, none of their top-upregulated proteins in the different subgroups were upregulated in our study.…”

“…Instead, methods to directly study the global protein expression and interaction could bring new knowledge regarding the pathophysiology of DLBCL subgroups. Some global protein expression studies using a proteomic approach where non-GCB or ABC patients are compared to controls or their GCB counterpart have been published [8][9][10][11][12]. Even though these studies show promising results, the results have been diverging which at least partly could be explained by the use of different techniques and perhaps foremost the low total number of included patients (ranging from a total of 6-42 patients).…”

PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors

“…These signatures characterize GCB tumors with genetic and dark zone biological features similar to both follicular lymphomas and Burkitt lymphomas. In addition, molecular classification schemes based on transcriptomics and proteomics were proposed in [33, 34, 35, 36] and, more recently, more sophisticated genetic classifiers [37, 38]. These recent developments warrant a holistic approach for revealing different pathomechanisms underlying DLBCL and predicting treatment response.…”