Classifying the Canine B-cell Lymphoma Cell line CLBL1
Ontology highlight
ABSTRACT:
ORGANISM(S): Canis lupus familiaris
PROVIDER: GSE81110 | GEO | 2016/05/05
SECONDARY ACCESSION(S): PRJNA320548
REPOSITORIES: GEO
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Project description:In this study, we present the first comparison of global transcriptional changes taking place in canine lymphoma and human diffuse large B-cell lymphoma (DLBCL), with particular reference to the nuclear factor-kappaB (NF-κB) pathway. Microarray data generated from lymph nodes diagnosed with canine DLBCL and healthy lymph nodes were used for differential expression analysis, co-expression analysis and pathway analysis, and compared with analysis of publicly available microarray data from human healthy and DLBCL lymph nodes. The comparisons between species at gene level were performed by mapping the probesets in canine microarrays to orthologous genes in humans and vice versa. A considerable number of differentially expressed genes between canine lymphoma and healthy lymph node samples were also found differentially expressed between human DLBCL and healthy lymph node samples. Principal component analysis (PCA) using a literature derived 120 NF-κB target gene set mapped to 199 orthologous canine array probesets and 259 human array probesets clearly separated the healthy and cancer samples in both canine and human datasets. The analysis demonstrated that for both human and canine DLBCL there is activation of the NF-κB/p65 canonical pathway rather than the alternative pathway. This has therapeutic implications for the use of specific pathway inhibitors for the treatment of DLBCL for both species and also indicates that naturally occurring canine lymphoma could be used as a model to study therapeutic strategies for human lymphoma. The model was further validated by the identification of molecular signatures that could sub-classify canine DLBCL into activated B-cell-like (ABC) or germinal centre B-cell-like (GCB) types equivalent to human subtypes.
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Project description:The most frequent mature aggressive B-cell lymphomas are diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL). Patients suffering from molecularly defined BL (mBL) but treated with a regimen developed for DLBCL show an unfavorable outcome compared to mBL treated with chemotherapy regimens for BL. Distinguishing BL from DLBCL by conventional histopathology is challenging in lymphomas that have features common to both diseases (aggressive B-cell lymphoma unclassifiable with features of DLBCL and BL [intermediates]). Moreover, DLBCL are a heterogeneous group of lymphomas comprising distinct molecular subtypes: the activated B-cell (ABC)-like, the germinal center B-cell-like (GCB) and the unclassifyable subtype as defined by gene expression profiling (GEP). Attempts to replace GEP with techniques applicable to formalin-fixed paraffin-embedded (FFPE) tissue led to algorithms for immunohistochemical stainings (IHS). Disappointingly, the algorithms yielded conflicting results with respect to their prognostic potential, raising concerns about their validity. Furthermore, IHS algorithms did not provide a fully resolved classification: They did not identify mBL; nor did they separate ABC from unclassified DLBCL. We used digital multiplexed gene expression (DMGE) with FFPE derived RNA to classify agressive B-cell lymphomas. Our assay comprised only 30 genes (10 for the detection of mBL and 20 for the detection of ABC and GCB). We chose these genes by reanalysis of the microarray data reported in a previous study. 39 samples from mature aggressive B-cell lymphomas were analyzed using DMGE (nCounter, NanoString Technologies Inc., Seattle, WA, USA) of FFPE- and fresh-frozen derived RNA. All cases were previously characterized by the Molecular Mechanisms of Malignant Lymphoma (MMML) consortium using the Affymetrix GeneChip technology (gold standard of classification). 29 diffuse large B-Cell lymphoma samples were hybridized to HGU133A Affymetrix GeneChips. In addition, this study contains 22 already published samples whereas 11 of them contribute to GSE22470, 6 contribute to GSE10172, 3 to GSE44164 and 2 to GSE4475. No re-normalisation of published samples was performed. The dataset representing: (1) 11 samples from GSE22470, (2) 6 samples from GSE10172, (3) 3 samples from GSE44164 and (4) 2 samples from GSE4475 is linked below as a supplementary file.
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