Oncology
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
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 Recent changes to subgroup distinctions have
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
introduced a degree of diagnostic ambiguity, especially in borderline cases. Inaccurate determination of HER2 status (for instance misclassifying HER2-low or -ultralow tumours as IHC 0/HER2-negative) could potentially lead to patients being denied beneficial therapies, or being exposed to less effective, toxic treatments unnecessarily. Accurate and consistent IHC classification therefore plays a central role in the diagnosis and appropriate treatment of breast cancer, underscoring the need for reliable testing methods that can resolve subtle distinctions between subgroups.
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),
Challenges facing subgroup classification The standard workflow for assessing HER2 status begins with IHC, which detects HER2 protein expression on tumour cells. IHC results are scored from 0 to 3+ based on the intensity and completeness of membrane staining.11 l 0 or 1+ is considered HER2-negative l 2+ is equivocal, requiring confirmatory testing
l 3+ is HER2-positive
If a tumour scores IHC 2+, reflex testing is performed using in situ hybridisation (ISH) – such as fluorescence (FISH) or chromogenic (CISH) techniques – to determine whether the HER2 gene is amplified.11
HER2 amplification confirms
eligibility for HER2-targeted therapies (Table 1). The main issue with this approach is that it is only designed to detect HER2 overexpression, and decide whether a patient is eligible for anti- HER2 therapy. As a result, the current diagnostic tools struggle to provide the level of precision now needed to identify subtle differences at the lower end of the expression range and guide modern treatment decisions. Heterogeneity of HER2 expression or amplification within a tumour, and tumours with low level amplification, can also limit the reproducibility of subgroup classification.4
Disadvantages of IHC Trastuzumab trials in the late 1990s used IHC and FISH to select HER2+ patients and, while these trials proved the drug’s efficacy, they did not demonstrate the superiority of the IHC classification method. Western blotting and PCR were also considered as potential techniques, but were dropped due to logistical issues. IHC therefore ultimately became the standard, not because it was the most accurate or predictive method, but rather because it was the most practical technique
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