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Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

BACKGROUND: The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier a...

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Autores principales: Wallden, Brett, Storhoff, James, Nielsen, Torsten, Dowidar, Naeem, Schaper, Carl, Ferree, Sean, Liu, Shuzhen, Leung, Samuel, Geiss, Gary, Snider, Jacqueline, Vickery, Tammi, Davies, Sherri R., Mardis, Elaine R., Gnant, Michael, Sestak, Ivana, Ellis, Matthew J., Perou, Charles M., Bernard, Philip S., Parker, Joel S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4546262/
https://www.ncbi.nlm.nih.gov/pubmed/26297356
http://dx.doi.org/10.1186/s12920-015-0129-6
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author Wallden, Brett
Storhoff, James
Nielsen, Torsten
Dowidar, Naeem
Schaper, Carl
Ferree, Sean
Liu, Shuzhen
Leung, Samuel
Geiss, Gary
Snider, Jacqueline
Vickery, Tammi
Davies, Sherri R.
Mardis, Elaine R.
Gnant, Michael
Sestak, Ivana
Ellis, Matthew J.
Perou, Charles M.
Bernard, Philip S.
Parker, Joel S.
author_facet Wallden, Brett
Storhoff, James
Nielsen, Torsten
Dowidar, Naeem
Schaper, Carl
Ferree, Sean
Liu, Shuzhen
Leung, Samuel
Geiss, Gary
Snider, Jacqueline
Vickery, Tammi
Davies, Sherri R.
Mardis, Elaine R.
Gnant, Michael
Sestak, Ivana
Ellis, Matthew J.
Perou, Charles M.
Bernard, Philip S.
Parker, Joel S.
author_sort Wallden, Brett
collection PubMed
description BACKGROUND: The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. METHODS: 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. RESULTS: The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. CONCLUSIONS: The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-015-0129-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-45462622015-08-23 Development and verification of the PAM50-based Prosigna breast cancer gene signature assay Wallden, Brett Storhoff, James Nielsen, Torsten Dowidar, Naeem Schaper, Carl Ferree, Sean Liu, Shuzhen Leung, Samuel Geiss, Gary Snider, Jacqueline Vickery, Tammi Davies, Sherri R. Mardis, Elaine R. Gnant, Michael Sestak, Ivana Ellis, Matthew J. Perou, Charles M. Bernard, Philip S. Parker, Joel S. BMC Med Genomics Research Article BACKGROUND: The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. METHODS: 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. RESULTS: The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. CONCLUSIONS: The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-015-0129-6) contains supplementary material, which is available to authorized users. BioMed Central 2015-08-22 /pmc/articles/PMC4546262/ /pubmed/26297356 http://dx.doi.org/10.1186/s12920-015-0129-6 Text en © Wallden et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Wallden, Brett
Storhoff, James
Nielsen, Torsten
Dowidar, Naeem
Schaper, Carl
Ferree, Sean
Liu, Shuzhen
Leung, Samuel
Geiss, Gary
Snider, Jacqueline
Vickery, Tammi
Davies, Sherri R.
Mardis, Elaine R.
Gnant, Michael
Sestak, Ivana
Ellis, Matthew J.
Perou, Charles M.
Bernard, Philip S.
Parker, Joel S.
Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
title Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
title_full Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
title_fullStr Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
title_full_unstemmed Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
title_short Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
title_sort development and verification of the pam50-based prosigna breast cancer gene signature assay
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4546262/
https://www.ncbi.nlm.nih.gov/pubmed/26297356
http://dx.doi.org/10.1186/s12920-015-0129-6
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