A B-cell-associated gene signature classification of diffuse large B-cell lymphoma by NanoString technology

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.

OriginalsprogEngelsk
TidsskriftBlood advances
Vol/bind2
Udgave nummer13
Sider (fra-til)1542-1546
Antal sider5
ISSN2473-9529
DOI
StatusUdgivet - 2018

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Lymphoma, Large B-Cell, Diffuse
B-Lymphocytes
Technology
Genes
Germinal Center
Gene Expression Profiling
Immunotherapy
Confidence Intervals
Biopsy
Gene Expression
Drug Therapy
Therapeutics

Citer dette

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title = "A B-cell-associated gene signature classification of diffuse large B-cell lymphoma by NanoString technology",
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.",
author = "Michaelsen, {Thomas Yssing} and Julia Richter and Br{\o}ndum, {Rasmus Froberg} and Wolfram Klapper and Johnsen, {Hans Erik} and Mads Albertsen and Karen Dybk{\ae}r and Martin B{\o}gsted",
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A B-cell-associated gene signature classification of diffuse large B-cell lymphoma by NanoString technology. / Michaelsen, Thomas Yssing; Richter, Julia; Brøndum, Rasmus Froberg; Klapper, Wolfram; Johnsen, Hans Erik; Albertsen, Mads; Dybkær, Karen; Bøgsted, Martin.

I: Blood advances, Bind 2, Nr. 13, 2018, s. 1542-1546.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - A B-cell-associated gene signature classification of diffuse large B-cell lymphoma by NanoString technology

AU - Michaelsen, Thomas Yssing

AU - Richter, Julia

AU - Brøndum, Rasmus Froberg

AU - Klapper, Wolfram

AU - Johnsen, Hans Erik

AU - Albertsen, Mads

AU - Dybkær, Karen

AU - Bøgsted, Martin

N1 - This article has been found as a 'Free Version' from the Publisher on November 12th 2018. When the access to the article closes, please notify vbn@aub.aau.dk.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - http://www.bloodadvances.org/content/bloodoa/2/13/1542.full.pdf?sso-checked=true

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DO - 10.1182/bloodadvances.2018017988

M3 - Journal article

VL - 2

SP - 1542

EP - 1546

JO - Blood advances

JF - Blood advances

SN - 2473-9529

IS - 13

ER -