Frequently increased epidermal growth factor receptor (EGFR) copy numbers and decreased BRCA1 mRNA expression in Japanese triple-negative breast cancers

<p>Abstract</p> <p>Background</p> <p>Triple-negative breast cancer (estrogen receptor-, progesterone receptor-, and HER2-negative) (TNBC) is a high risk breast cancer that lacks specific therapy targeting these proteins.</p> <p>Methods</p> <p>We studied 969 consecutive Japanese patients diagnosed with invasive breast cancer from January 1981 to December 2003, and selected TNBCs based on the immunohistochemical data. Analyses of epidermal growth factor receptor (<it>EGFR</it>) gene mutations and amplification, and <it>BRCA</it>1 mRNA expression were performed on these samples using TaqMan PCR assays. The prognostic significance of TNBCs was also explored. Median follow-up was 8.3 years.</p> <p>Results</p> <p>A total of 110 (11.3%) patients had TNBCs in our series. Genotyping of the <it>EGFR </it>gene was performed to detect 14 known <it>EGFR </it>mutations, but none was identified. However, <it>EGFR </it>gene copy number was increased in 21% of TNBCs, while only 2% of ER- and PgR-positive, HER2-negative tumors showed slightly increased <it>EGFR </it>gene copy numbers. Thirty-one percent of TNBCs stained positive for EGFR protein by immunohistochemistry. <it>BRCA1 </it>mRNA expression was also decreased in TNBCs compared with controls. Triple negativity was significantly associated with grade 3 tumors, TP53 protein accumulation, and high Ki67 expression. TNBC patients had shorter disease-free survival than non-TNBC in node-negative breast cancers.</p> <p>Conclusion</p> <p>TNBCs have an aggressive clinical course, and <it>EGFR </it>and <it>BRCA1 </it>might be candidate therapeutic targets in this disease.</p

PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2.

Background: Predict (www.predict.nhs.uk) is an online, breast cancer prognostication and treatment benefit tool. The aim of this study was to incorporate the prognostic effect of HER2 status in a new version (Predict þ ), and to compare its performance with the original Predict and Adjuvant!. Methods: The prognostic effect of HER2 status was based on an analysis of data from 10 179 breast cancer patients from 14 studies in the Breast Cancer Association Consortium. The hazard ratio estimates were incorporated into Predict. The validation study was based on 1653 patients with early-stage invasive breast cancer identified from the British Columbia Breast Cancer Outcomes Unit. Predicted overall survival (OS) and breast cancer-specific survival (BCSS) for Predict þ , Predict and Adjuvant! were compared with observed outcomes. Results: All three models performed well for both OS and BCSS. Both Predict models provided better BCSS estimates than Adjuvant!. In the subset of patients with HER2-positive tumours, Predict þ performed substantially better than the other two models for both OS and BCSS. Conclusion: Predict þ is the first clinical breast cancer prognostication tool that includes tumour HER2 status. Use of the model might lead to more accurate absolute treatment benefit predictions for individual patients