Journal
CURRENT CANCER DRUG TARGETS
Volume 15, Issue 8, Pages 665-683Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/156800961508151001101742
Keywords
Breast cancer; dimerization; HER2; molecular subtype; negative predictive biomarkers; survival; trastuzumab; tumor heterogeneity
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Funding
- OTKA [K108655]
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The introduction of trastuzumab for anti-HER2 therapy dramatically changed the clinical outcome for HER2 (ERBB2, neu) positive breast cancer patients. Today, patients eligible for trastuzumab are selected using HER2 expression/amplification status of the primary tumor. However, acquired and inherent resistance to anti-HER2 therapy in these patients poses a significant challenge, and better patient stratification will be needed to improve clinical response. Here, we provide a wide-ranging overview of potential biomarkers capable of stratifying patients regarding their response to trastuzumab. These include HER2 amplification, impaired access to the binding site (p95HER2, Delta 16HER-2, MUC4), augmented signaling through other ERBB family receptors (HER1, HER3, HER4) and their ligands, activation of HER2 targets by alternate heterodimers (EphA2, IGF-1R, GDF15, MUC1*), signaling triggered by downstream members (PIK3CA, PTEN, SRC, mTOR), altered expression of cell cycle and apoptotic regulators (CDKs, p27(kip1), Bcl-2), hormone receptor status, resistance to antibody-dependent cellular cytotoxicity (Fc gamma R), and altered miRNA expression signatures. Multigenic molecular profile analyses have revealed further genes not directly associated with classical oncogenic pathways. Although numerous biomarkers have shown promise in pre-clinical studies, many have delivered controversial results when evaluated in clinical trials. One of the keys for targeting ERBB2 will be to consider the entire ERBB family and downstream associated pathways responsible for the malignant transformation. The heterogeneity of the disease is likely to represent a significant obstacle to accurately predicting the course of resistance. The future most probably involves the incorporation of multiple biomarkers into a unified predictor enabling selection of patients for superior targeted drug administration.
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