4 research outputs found

    A search for prognostic biomarkers in diffuse large B-cell lymphoma with proteomics and immunohistochemistry

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    Diffuse large B-cell lymphoma (DLBCL), the most common lymphoma in the Western world, can by gene expression profiling or immunohisto-chemistry (IHC), be divided into two subgroups according to its “cell-of-origin”. The subgroup ABC (or non-GCB) with similarities to active post-germinal centre B-cells, is associated with worse outcome. In addition, patients with primary refractory disease or early relapse have a very dismal prognosis. The aim of this thesis has been to identify novel prognostic biomarkers in a large retrospective DLBCL patient cohort by mass-spectrometry (MS)-based proteomics and IHC. Quantitative MS-based proteomics (QMS) revealed several differentially expressed proteins between refractory/relapsed patients (REF/REL) and patients with progression-free survival ≄5 years (CURED). Many ribosomal proteins were up-regulated in REF/REL patients while numerous proteins associated with the actin cytoskeleton were up-regulated in CURED patients. By using QMS we also found several up-regulated proteins in non-GCB DLBCL related to the tumour microenvironment, including interferon (IFN)-stimulated proteins. By using IHC we found a prognostic association for two proteins (CREBBP and TBLR1) that are frequently mutated in DLBCL, and for IFI16 and MNDA, both belonging to the pyrin and hematopoietic IFN-inducible nuclear (PYHIN) family. In conclusion, we have found increased expression of several proteins or groups of proteins not previously described in DLBCL and with potential prognostic impact. Further functional studies are warranted to elucidate their role in immunochemotherapy resistance

    Transcriptome sequencing of archived lymphoma specimens is feasible and clinically relevant using exome capture technology

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    Formalin-fixed, paraffin-embedded (FFPE) specimens are an underutilized resource in medical research, particularly in the setting of transcriptome sequencing, as RNA from these samples is often degraded. We took advantage of an exome capture-based RNA-sequencing protocol to explore global gene expression in paired fresh–frozen (FF) and FFPE samples from 16 diffuse large B-cell lymphoma (DLBCL) patients. While FFPE samples generated fewer mapped reads compared to their FF counterparts, these reads captured the same library complexity and had a similar number of genes expressed on average. Furthermore, gene expression demonstrated a high correlation when comparing housekeeping genes only or across the entire transcriptome (r = 0.99 for both comparisons). Differences in gene expression were primarily seen in lowly expressed genes and genes with small or large coding sequences. Using cell-of-origin classifiers and clinically relevant gene expression signatures for DLBCL, FF, and FFPE samples from the same biopsy paired nearly perfectly in clustering analysis. This was further confirmed in a validation cohort of 50 FFPE DLBCL samples. In summary, we found the biological differences between tumors to be far greater than artifacts created as a result of degraded RNA. We conclude that exome capture transcriptome sequencing data from archival samples can confidently be used for cell-of-origin classification of DLBCL samples

    Cell-of-origin determined by both gene expression profiling and immunohistochemistry is the strongest predictor of survival in patients with diffuse large B-cell lymphoma

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    The tumor cells in diffuse large B-cell lymphomas (DLBCL) are considered to originate from germinal center derived B-cells (GCB) or activated B-cells (ABC). Gene expression profiling (GEP) is preferably used to determine the cell of origin (COO). However, GEP is not widely applied in clinical practice and consequently, several algorithms based on immunohistochemistry (IHC) have been developed. Our aim was to evaluate the concordance of COO assignment between the Lymph2Cx GEP assay and the IHC-based Hans algorithm, to decide which model is the best survival predictor. Both GEP and IHC were performed in 359 homogenously treated Swedish and Danish DLBCL patients, in a retrospective multicenter cohort. The overall concordance between GEP and IHC algorithm was 72%; GEP classified 85% of cases assigned as GCB by IHC, as GCB, while 58% classified as non-GCB by IHC, were categorized as ABC by GEP. There were significant survival differences (overall survival and progression-free survival) if cases were classified by GEP, whereas if cases were categorized by IHC only progression-free survival differed significantly. Importantly, patients assigned as non-GCB/ABC both by IHC and GEP had the worst prognosis, which was also significant in multivariate analyses. Double expression of MYC and BCL2 was more common in ABC cases and was associated with a dismal outcome. In conclusion, to determine COO both by IHC and GEP is the strongest outcome predictor to identify DLBCL patients with the worst outcome
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