ArticleBlood advances2018
A B-cell-associated gene signature classification of diffuse large B-cell lymphoma by NanoString technology.
Article in Blood advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Pareto task inference analysis reveals cellular trade-offs in diffuse large B-Cell lymphoma transcriptomic data.Frontiers in systems biology · 2024Article
- Acquisition of a glycosylated B-cell receptor drives follicular lymphoma toward a dark zone phenotype.Blood advances · 2023Article
- Baseline tumor gene expression signatures correlate with chemoimmunotherapy treatment responsiveness in canine B cell lymphoma.PloS one · 2023Article
- Review
- Article
- Frequent mutated B2M, EZH2, IRF8, and TNFRSF14 in primary bone diffuse large B-cell lymphoma reflect a GCB phenotype.Blood advances · 2021Article
- A bendamustine resistance gene signature in diffuse large B-cell lymphoma and multiple myeloma.Cancer drug resistance (Alhambra, Calif.) · 2021Article
- Single-cell analysis of germinal-center B cells informs on lymphoma cell of origin and outcome.The Journal of experimental medicine · 2020Article
- Tissue microRNA expression profiling in hepatic and pulmonary metastatic melanoma.Melanoma research · 2020Article
- Molecular Complexity of Diffuse Large B-Cell Lymphoma: Can It Be a Roadmap for Precision Medicine?Cancers · 2020Review
- A multiple myeloma classification system that associates normal B-cell subset phenotypes with prognosis.Blood advances · 2018Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gene expression profiling (GEP) by microarrays of diffuse large B-cell lymphoma (DLBCL) has enabled the categorization of DLBCL into activated B-cell-like and germinal center B-cell-like subclasses. However, as this does not fully embrace the great diversity of B-cell subtypes, we recently developed a gene expression assay for B-cell-associated gene signature (BAGS) classification. To facilitate quick and easy-to-use BAGS profiling, we developed in this study the NanoString-based BAGS2Clinic assay. Microarray data from 4 different cohorts (n = 970) were used to select genes and train the assay. The locked assay was validated in an independent cohort of 88 sample biopsies. The assay showed good correspondence with the original BAGS classifier, with an overall accuracy of 84% (95% confidence interval, 72% to 93%) and a subtype-specific accuracy ranging between 80% and 99%. BAGS classification has the potential to provide valuable insight into tumor biology as well as differences in resistance to immuno- and chemotherapy that can lead to novel treatment strategies for DLBCL patients. BAGS2Clinic can facilitate this and the implementation of BAGS classification as a routine clinical tool to improve prognosis and treatment guidance for DLBCL patients.
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