Literature DB >> 21447351

Association between breast cancer subtypes and response to neoadjuvant anastrozole.

Anita K Dunbier1, Helen Anderson, Zara Ghazoui, Janine Salter, Joel S Parker, Charles M Perou, Ian E Smith, Mitch Dowsett.   

Abstract

Considerable heterogeneity exists amongst oestrogen receptor positive (ER+ve) breast cancer in both its molecular profile and response to therapy. Attempts to better define variation amongst breast tumours have led to the definition of four main "intrinsic" subtypes of breast cancer with two of these classes, Luminal A and B, composed almost entirely of ER+ve cancers. In this study we set out to investigate the significance of intrinsic subtypes within a group of ER+ve breast cancers treated with neoadjuvant anastrozole. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with anastrozole was analyzed on Illumina 48K microarrays. Gene-expression based subtypes and risk of relapse (ROR) scores for tumours pre- and post-treatment were determined using the PAM50 method. Amongst pre-treatment samples, all intrinsic subtypes were found to be present, although luminal groups were represented most highly. Luminal A and B tumours obtained similar benefit from treatment, as measured by the proportional fall in the proliferation marker Ki67 upon treatment (mean suppression=75.5% vs 75.7%). Tumours classified as basal and Her2-like showed poor reductions in Ki67 upon treatment. Residual Ki67 staining after two weeks remained higher in the Luminal B group. ROR score was significantly associated with anti-proliferative response to AI and with clinical response. These results suggest that in the short-term, Luminal A and B tumours may gain similar benefit from an AI but that the higher residual Ki67 level seen in Luminal B is indicative of poorer long term outcome.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21447351     DOI: 10.1016/j.steroids.2011.02.025

Source DB:  PubMed          Journal:  Steroids        ISSN: 0039-128X            Impact factor:   2.668


  18 in total

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2.  Everolimus plus Exemestane for Hormone Receptor-Positive Advanced Breast Cancer: A PAM50 Intrinsic Subtype Analysis of BOLERO-2.

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Journal:  Oncologist       Date:  2019-01-24

3.  Age-specific changes in intrinsic breast cancer subtypes: a focus on older women.

Authors:  Emily O Jenkins; Allison M Deal; Carey K Anders; Aleix Prat; Charles M Perou; Lisa A Carey; Hyman B Muss
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Review 4.  Therapeutic Implications of the Molecular and Immune Landscape of Triple-Negative Breast Cancer.

Authors:  Ana C Gregório; Manuela Lacerda; Paulo Figueiredo; Sérgio Simões; Sérgio Dias; João Nuno Moreira
Journal:  Pathol Oncol Res       Date:  2017-09-14       Impact factor: 3.201

5.  Intrinsic Subtypes and Gene Expression Profiles in Primary and Metastatic Breast Cancer.

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Journal:  Cancer Res       Date:  2017-03-01       Impact factor: 12.701

6.  Systematically defining single-gene determinants of response to neoadjuvant chemotherapy reveals specific biomarkers.

Authors:  Agnieszka K Witkiewicz; Uthra Balaji; Erik S Knudsen
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7.  A PAM50-Based Chemoendocrine Score for Hormone Receptor-Positive Breast Cancer with an Intermediate Risk of Relapse.

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Journal:  Clin Cancer Res       Date:  2016-11-30       Impact factor: 12.531

Review 8.  Signatures of tumor-immune interactions as biomarkers for breast cancer prognosis.

Authors:  Masoud H Manjili; Kayvan Najarian; Xiang-Yang Wang
Journal:  Future Oncol       Date:  2012-06       Impact factor: 3.404

9.  Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics.

Authors:  Carol Sweeney; Philip S Bernard; Rachel E Factor; Marilyn L Kwan; Laurel A Habel; Charles P Quesenberry; Kaylynn Shakespear; Erin K Weltzien; Inge J Stijleman; Carole A Davis; Mark T W Ebbert; Adrienne Castillo; Lawrence H Kushi; Bette J Caan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-02-12       Impact factor: 4.254

10.  Predicting drug responsiveness in human cancers using genetically engineered mice.

Authors:  Jerry Usary; Wei Zhao; David Darr; Patrick J Roberts; Mei Liu; Lorraine Balletta; Olga Karginova; Jamie Jordan; Austin Combest; Arlene Bridges; Aleix Prat; Maggie C U Cheang; Jason I Herschkowitz; Jeffrey M Rosen; William Zamboni; Norman E Sharpless; Charles M Perou
Journal:  Clin Cancer Res       Date:  2013-06-18       Impact factor: 12.531

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