Quantitative Measurement of HER2 Expression in Non-Small Cell Lung Cancer With a High-Sensitivity Assay

Recently, low human epidermal growth factor receptor 2 (HER2) protein expression has been proposed as a predictive biomarker for response to the antibody-drug conjugate trastuzumab deruxtecan (T-DXd) in metastatic breast cancer. HER2 expression in non-small cell lung cancer (NSCLC) patients has never been carefully measured, and little is known about the frequency of cases with unamplified but detectable levels of the protein. Although some HER2-targeted therapies have been studied in NSCLC patients, they have been restricted to those with genomic ERBB2 gene alterations, which only represent relatively rare cases of NSCLC. Still, emerging investigations of T-DXd in NSCLC have shown promise in patients with unamplified HER2. Taken together, we hypothesize that there may be many cases of NSCLC with levels of HER2 protein expression comparable with levels seen in breast cancer that benefit from T-DXd. Here, we used a previously validated, analytic, quantitative immunofluorescence (QIF) assay that is more sensitive than legacy clinical HER2 immunohistochemistry assays. We measured HER2 protein levels in NSCLC cases to determine the proportion of cases with detectable HER2 expression. Using cell line calibration microarrays alongside our QIF method enabled us to convert HER2 signal into units of attomoles per mm2. We found that over 63% of the 741 analyzed NSCLC cases exhibited HER2 expression above the limit of detection, with more than 17% of them exceeding the lower limit of quantification. Although the threshold for response to T-DXd in breast cancer is still unknown, many cases of NSCLC have expression in a range comparable to breast cancer cases with immunohistochemistry scores of 1 & thorn; or 2 & thorn;. Our assay could potentially select NSCLC cases with a detectable target (ie, HER2) that might benefit from HER2 antibody-drug conjugates, irrespective of ERBB2 genomic alterations. (c) 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.