Guiding targeted therapies for breast cancer

Vinicio Tassani outlines the advantages of RNA-based detection methods over traditional techniques in refining breast cancer classification, and argues that quantitative methods can improve accuracy, reduce misclassification and better align patients with the most suitable therapies.

Human epidermal growth factor receptor 2 (HER2) is one of the most important markers for stratifying breast cancer patients and determining the optimal therapeutic pathway.1 HER2 is a transmembrane receptor tyrosine kinase that plays a key role in cell growth and survival, and was first identified as a breast cancer marker in 2005.2,3 Tumours have typically been classified as either HER2-positive or -negative, with HER2 positivity being defined as protein overexpression or equivocal expression with evidence of HER2 gene amplification.4 HER2-positive breast cancer constitutes 13 to 15 per cent of all cases, and shows variable response to anti-HER2 therapies such as trastuzumab, pertuzumab and trastuzumab emtansine (T-DM1).5-7

In recent years, the DESTINY-Breast04 trial revealed the clinical relevance of a third subgroup, HER2-low, defined by immunohistochemistry (IHC) scores of 1+ or 2+ without HER2 gene amplification.8 More recently still, researchers have begun to recognise a potential fourth category — HER2-ultralow — comprising tumours with minimal focal staining that fall just above the IHC 0 threshold, often involving incomplete membrane staining in fewer than 10 per cent of cells.9

These two newer subgroups previously fell within the HER2-negative group, and therefore were ineligible for targeted anti-HER2 therapy.8 However, trastuzumab deruxtecan (T-DXd), an antibody drug conjugate (ADC), has shown clinical benefit in HER2-low tumours. HER2-ultralow tumours are not yet formally defined in clinical guidelines, but may also respond to emerging ADCs.10

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