Abstract PO4-14-08: A gene expression-based classifier for HER2-low breast cancer

Abstract Background: The definition of HER2-low breast cancer from clinical trials of antibody-conjugated drugs (ADCs) relies on immunohistochemistry scoring (IHC). However, in daily-practice the accuracy of IHC is hindered by inter-observer variability in assessing HER2 non-overexpressing status. Here, we aimed to identify breast cancer primary tumors with low HER2 expression by leveraging gene expression profiling. Materials and methods: A discovery approach was applied to gene expression profile of internal INT1 (n= 125) and INT2 (n= 84) datasets. We identified specific differently expressed genes (DEGs) according to HER2 IHC categories 0, 1+, 2+ and 3+. Principal Component Analysis (PCA) was used to generate a HER2-low signature whose performance was confirmed in the independent INT3 (n= 95), and TCGA and GSE20194 publicly available datasets. The association between the HER2-low signature and HER2 IHC categories was evaluated by non-parametric Kruskal-Wallis (KW) test with post hoc pair wise comparisons (i.e. contrasts); the HER2-low signature discriminatory capability was assessed by estimating the area under the receiver operating characteristic (ROC) curve (AUC) with its corresponding 95% Confidence Interval (CI). Gene Ontology and KEGG analyses were performed to enrich the DEGs for functional information. Results: A 20-gene HER2-low signature, consisting of both up-regulated (n=11) and down-regulated (n=9) genes, was computed based on the DEGs according to HER2 IHC categories as follows: 11 genes specific for the 1+ category, 8 genes for the 2+ category, and 1 gene for the HER2-low category. HER2-low signature genes were significantly enriched with lipid and steroid metabolism pathways, peptidase regulation, and humoral immune response. The HER2-low signature levels demonstrated a significant association with HER2 IHC categories (KW p-value 0.001) and were distributed in a bell-shaped pattern across IHC categories (low values 0 and 3+; high values 1+ and 2+), effectively distinguishing HER2-low from 0 (contrast p-value 0.001) and 3+ (contrast p-value 0.001). Notably, the signature levels were significantly higher in tumors scored with 1+ as compared to 0 (contrast p =0.002). The HER2-low signature association with IHC categories and discriminatory capability was confirmed in the independent INT3 and TCGA datasets with higher values in HER2-low compared to both 0 and 3+. The HER2-low signature achieved an AUC value of 0.72 (95%CI 0.62-0.83) in differentiating HER2 0/3+ from HER2 1+/2+ categories, which is worth noting in light of the individual ERBB2 mRNA AUC value of 0.48 (95%CI 0.34-0.62). Conclusions: The 20-gene HER2-low signature was generated by maximizing the differences in gene expression between tumors with different HER2 status according to IHC. In contrast to the previously published single ERBB2 gene expression assessment, our data presents compelling evidence for effectively distinguishing HER2-low tumors, including those scored as 1+ from HER2-0 tumors. Our signature holds potential in selecting novel candidates for ADC therapy. Citation Format: Serena Di Cosimo, Sara Pizzamiglio, Chiara Maura Ciniselli, Valeria Duroni, Vera Cappelletti, Loris de Cecco, Maria Carmen De Santis, Rosaria Orlandi, Marilena Iorio, Marco Silvestri, Giancarlo Pruneri, Paolo Verderio. A gene expression-based classifier for HER2-low breast cancer abstract. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-14-08.