Molecular Abnormalities in Breast Cancer

Sex hormones – estrogen in particular – are strongly implicated in breast cancer. Mutations in a number of genes such as BRCA1/2 that confer risk for the disease are thought to lead to elevated levels of estrogen and progesterone1. Increased exposure to estrogen and progestierone through the use of hormonal birth control is also associated with a higher risk for the disease2. Breast cancers are primarily classified by the presence or absence of estrogen receptor, progesterone receptor, and the non-hormone-related molecule human epidermal growth factor receptor 2 (HER2). Therapies targeting these receptors are among the most common treatments for the disease.

Recent findings have suggested additional molecular targets for drug development. Next generation sequencing studies from almost 900 breast cancers have identified hundreds of mutations whose corresponding protein activity falls into one of two categories: activation of the PI3K/AKT pathway and repression of the JUN/MAPK pathway3,4,5,6. The resulting effects on the cell cycle are thought to disrupt the normal balance of cell proliferation and differentiation in mammary epithelium cells7.

A February 2014 study published in the Journal of Clinical Investigation reports a new classification scheme for breast cancers that is able to predict patient outcome8. It is based not on the characteristics of tumor cells but on the molecular phenotype of normal breast cells. Using over 15,000 healthy breast cells, a total of 11 cell types were identified. Tumor cells were subsequently classified into four major subtypes according to the presence or absence of vitamin D, androgen, and estrogen receptor expression. Expression of all three receptor types was associated with the best clinical outcome, while expression of none was associated with the worst.

New research suggests that a variety of molecular abnormalities are present in breast cancer cells. A number of potential therapeutic targets that have recently been identified will hopefully lead to novel treatments in the future.

Detection of human BRC1 in FFPE breast carcinoma by IHC.

Detection of human BRC1 in FFPE breast carcinoma by IHC. Antibody: Rabbit anti-BRC1 (IHC-00278). Secondary: HRP-conjugated goat anti-rabbit IgG (A120-501P). Substrate: DAB.

Detection of human ErbB2 (red), commonly referred to as HER2, in FFPE human breast carcinoma by IHC-IF.

Detection of human ErbB2 (red), commonly referred to as HER2, in FFPE human breast carcinoma by IHC-IF. Antibody: Rabbit anti-ErbB2 (IHC-00032). Secondary: DyLight® 594-conjugated goat anti-rabbit IgG (A120-201D4). Counterstain: DAPI (blue).


1. Knutson TP, Lange CA. 2014. Tracking progesterone receptor-mediated actions in breast cancer. Pharmacol Ther. Apr;142(1):114-125.

2. Hunter DJ, Colditz GA, Hankinson SE, Malspeis S, Spiegelman D, Chen, W, Stampfer MJ, Willett WC. 2010. Oral contraceptive use and breast cancer: a prospective study of young women. Cancer Epidemiol Biomarkers Prev. Oct;19(10):2496–2502.

3. Cancer Genome Atlas Network. 2012. Comprehensive molecular portraits of human breast tumours. Nature. Oct 4;490(7418):61–70.

4. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, Turashvili G, Ding J, Tse K, Haffari G, et al. 2012. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. Apr 4;486(7403):385–389.

5. Banerji S, Cibulskis K, Rangel-Escareno K, Brown KK, Carter SL, Frederick AM, Lawrence MS, Sivachenko AY, Sougnez C, et al. 2012. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. June 21;486(7403):405–409.

6. Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, Van Tine BA, Hoog J, Goiffon RJ, Goldstein TC, et al. 2012. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature. Jun 10;486(7403):353–360.

7. Guille A, Chaffanet M, Birnbaum D. 2013. Signaling pathway switch in breast cancer. Cancer Cell Int. 2013;13(1):66.

8. Santagata S, Thakkar A, Ergonul A, Wang B, Woo T, Hu R, Harrell JC, McNamara G, Schwede M, Culhane AC, et al. 2014. Taxonomy of breast cancer based on normal cell phenotype predicts outcome. J Clin Invest. Feb 3;124(2):859-870.