Cargando…
NanoString-based breast cancer risk prediction for women with sclerosing adenosis
PURPOSE: Sclerosing adenosis (SA), found in ¼ of benign breast disease (BBD) biopsies, is a histological feature characterized by lobulocentric proliferation of acini and stromal fibrosis and confers a two-fold increase in breast cancer risk compared to women in the general population. We evaluated...
Autores principales: | , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668350/ https://www.ncbi.nlm.nih.gov/pubmed/28798985 http://dx.doi.org/10.1007/s10549-017-4441-z |
_version_ | 1783275655897546752 |
---|---|
author | Winham, Stacey J. Mehner, Christine Heinzen, Ethan P. Broderick, Brendan T. Stallings-Mann, Melody Nassar, Aziza Vierkant, Robert A. Hoskin, Tanya L. Frank, Ryan D. Wang, Chen Denison, Lori A. Vachon, Celine M. Frost, Marlene H. Hartmann, Lynn C. Aubrey Thompson, E. Sherman, Mark E. Visscher, Daniel W. Degnim, Amy C. Radisky, Derek C. |
author_facet | Winham, Stacey J. Mehner, Christine Heinzen, Ethan P. Broderick, Brendan T. Stallings-Mann, Melody Nassar, Aziza Vierkant, Robert A. Hoskin, Tanya L. Frank, Ryan D. Wang, Chen Denison, Lori A. Vachon, Celine M. Frost, Marlene H. Hartmann, Lynn C. Aubrey Thompson, E. Sherman, Mark E. Visscher, Daniel W. Degnim, Amy C. Radisky, Derek C. |
author_sort | Winham, Stacey J. |
collection | PubMed |
description | PURPOSE: Sclerosing adenosis (SA), found in ¼ of benign breast disease (BBD) biopsies, is a histological feature characterized by lobulocentric proliferation of acini and stromal fibrosis and confers a two-fold increase in breast cancer risk compared to women in the general population. We evaluated a NanoString-based gene expression assay to model breast cancer risk using RNA derived from formalin-fixed, paraffin-embedded (FFPE) biopsies with SA. METHODS: The study group consisted of 151 women diagnosed with SA between 1967 and 2001 within the Mayo BBD cohort, of which 37 subsequently developed cancer within 10 years (cases) and 114 did not (controls). RNA was isolated from benign breast biopsies, and NanoString-based methods were used to assess expression levels of 61 genes, including 35 identified by previous array-based profiling experiments and 26 from biological insight. Diagonal linear discriminant analysis of these data was used to predict cancer within 10 years. Predictive performance was assessed with receiver operating characteristic area under the curve (ROC-AUC) values estimated from 5-fold cross-validation. RESULTS: Gene expression prediction models achieved cross-validated ROC-AUC estimates ranging from 0.66 to 0.70. Performing univariate associations within each of the five folds consistently identified genes DLK2, EXOC6, KIT, RGS12, and SORBS2 as significant; a model with only these five genes showed cross-validated ROC-AUC of 0.75, which compared favorably to risk prediction using established clinical models (Gail/BCRAT: 0.57; BBD-BC: 0.67). CONCLUSIONS: Our results demonstrate that biomarkers of breast cancer risk can be detected in benign breast tissue years prior to cancer development in women with SA. These markers can be assessed using assay methods optimized for RNA derived from FFPE biopsy tissues which are commonly available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-017-4441-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5668350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-56683502017-11-16 NanoString-based breast cancer risk prediction for women with sclerosing adenosis Winham, Stacey J. Mehner, Christine Heinzen, Ethan P. Broderick, Brendan T. Stallings-Mann, Melody Nassar, Aziza Vierkant, Robert A. Hoskin, Tanya L. Frank, Ryan D. Wang, Chen Denison, Lori A. Vachon, Celine M. Frost, Marlene H. Hartmann, Lynn C. Aubrey Thompson, E. Sherman, Mark E. Visscher, Daniel W. Degnim, Amy C. Radisky, Derek C. Breast Cancer Res Treat Brief Report PURPOSE: Sclerosing adenosis (SA), found in ¼ of benign breast disease (BBD) biopsies, is a histological feature characterized by lobulocentric proliferation of acini and stromal fibrosis and confers a two-fold increase in breast cancer risk compared to women in the general population. We evaluated a NanoString-based gene expression assay to model breast cancer risk using RNA derived from formalin-fixed, paraffin-embedded (FFPE) biopsies with SA. METHODS: The study group consisted of 151 women diagnosed with SA between 1967 and 2001 within the Mayo BBD cohort, of which 37 subsequently developed cancer within 10 years (cases) and 114 did not (controls). RNA was isolated from benign breast biopsies, and NanoString-based methods were used to assess expression levels of 61 genes, including 35 identified by previous array-based profiling experiments and 26 from biological insight. Diagonal linear discriminant analysis of these data was used to predict cancer within 10 years. Predictive performance was assessed with receiver operating characteristic area under the curve (ROC-AUC) values estimated from 5-fold cross-validation. RESULTS: Gene expression prediction models achieved cross-validated ROC-AUC estimates ranging from 0.66 to 0.70. Performing univariate associations within each of the five folds consistently identified genes DLK2, EXOC6, KIT, RGS12, and SORBS2 as significant; a model with only these five genes showed cross-validated ROC-AUC of 0.75, which compared favorably to risk prediction using established clinical models (Gail/BCRAT: 0.57; BBD-BC: 0.67). CONCLUSIONS: Our results demonstrate that biomarkers of breast cancer risk can be detected in benign breast tissue years prior to cancer development in women with SA. These markers can be assessed using assay methods optimized for RNA derived from FFPE biopsy tissues which are commonly available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-017-4441-z) contains supplementary material, which is available to authorized users. Springer US 2017-08-10 2017 /pmc/articles/PMC5668350/ /pubmed/28798985 http://dx.doi.org/10.1007/s10549-017-4441-z Text en © The Author(s) 2017 Open AccessThis 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. |
spellingShingle | Brief Report Winham, Stacey J. Mehner, Christine Heinzen, Ethan P. Broderick, Brendan T. Stallings-Mann, Melody Nassar, Aziza Vierkant, Robert A. Hoskin, Tanya L. Frank, Ryan D. Wang, Chen Denison, Lori A. Vachon, Celine M. Frost, Marlene H. Hartmann, Lynn C. Aubrey Thompson, E. Sherman, Mark E. Visscher, Daniel W. Degnim, Amy C. Radisky, Derek C. NanoString-based breast cancer risk prediction for women with sclerosing adenosis |
title | NanoString-based breast cancer risk prediction for women with sclerosing adenosis |
title_full | NanoString-based breast cancer risk prediction for women with sclerosing adenosis |
title_fullStr | NanoString-based breast cancer risk prediction for women with sclerosing adenosis |
title_full_unstemmed | NanoString-based breast cancer risk prediction for women with sclerosing adenosis |
title_short | NanoString-based breast cancer risk prediction for women with sclerosing adenosis |
title_sort | nanostring-based breast cancer risk prediction for women with sclerosing adenosis |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668350/ https://www.ncbi.nlm.nih.gov/pubmed/28798985 http://dx.doi.org/10.1007/s10549-017-4441-z |
work_keys_str_mv | AT winhamstaceyj nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT mehnerchristine nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT heinzenethanp nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT broderickbrendant nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT stallingsmannmelody nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT nassaraziza nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT vierkantroberta nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT hoskintanyal nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT frankryand nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT wangchen nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT denisonloria nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT vachoncelinem nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT frostmarleneh nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT hartmannlynnc nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT aubreythompsone nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT shermanmarke nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT visscherdanielw nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT degnimamyc nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis AT radiskyderekc nanostringbasedbreastcancerriskpredictionforwomenwithsclerosingadenosis |