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Data from Detection of Elevated Plasma Levels of Epidermal Growth Factor Receptor Before Breast Cancer Diagnosis among Hormone Therapy Users

Posted on 2023-03-30 - 20:05
Abstract

Applying advanced proteomic technologies to prospectively collected specimens from large studies is one means of identifying preclinical changes in plasma proteins that are potentially relevant to the early detection of diseases such as breast cancer. We conducted 14 independent quantitative proteomics experiments comparing pooled plasma samples collected from 420 estrogen receptor–positive (ER+) breast cancer patients ≤17 months before their diagnosis and matched controls. Based on the more than 3.4 million tandem mass spectra collected in the discovery set, 503 proteins were quantified, of which 57 differentiated cases from controls with a P value of <0.1. Seven of these proteins, for which quantitative ELISA assays were available, were assessed in an independent validation set. Of these candidates, epidermal growth factor receptor (EGFR) was validated as a predictor of breast cancer risk in an independent set of preclinical plasma samples for women overall [odds ratio (OR), 1.44; P = 0.0008] and particularly for current users of estrogen plus progestin (E + P) menopausal hormone therapy (OR, 2.49; P = 0.0001). Among current E + P users, the EGFR sensitivity for breast cancer risk was 31% with 90% specificity. Whereas the sensitivity and specificity of EGFR are insufficient for a clinically useful early detection biomarker, this study suggests that proteins that are elevated preclinically in women who go on to develop breast cancer can be discovered and validated using current proteomic technologies. Further studies are warranted to examine the role of EGFR and to discover and validate other proteins that could potentially be used for early detection of breast cancer. Cancer Res; 70(21); 8598–606. ©2010 AACR.

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Cancer Research

AUTHORS (15)

  • Sharon J. Pitteri
    Lynn M. Amon
    Tina Busald Buson
    Yuzheng Zhang
    Melissa M. Johnson
    Alice Chin
    Jacob Kennedy
    Chee-Hong Wong
    Qing Zhang
    Hong Wang
    Paul D. Lampe
    Ross L. Prentice
    Martin W. McIntosh
    Samir M. Hanash
    Christopher I. Li
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