Detection of ESR1 and PIK3CA actionable mutations in breast cancer using liquid biopsy: A step toward precision medicine.

Abstract

e13070 Background: The new targeted treatment options for HR+/HER2- recurrent breast cancer patients, has enhanced the value of biomarker analysis by NGS. The aim of this study was to evaluate the utility of liquid biopsy in identifying actionable and resistance-associated variants in relapsed breast cancer patients treated with hormone therapy. Methods: Plasma samples were collected from 1011 metastatic breast cancer (BC) patients. cfDNA was extracted and then analyzed using the commercially available gene panel Oncomine™ Breast cfDNA with the Ion GeneStudio S5 Prime NGS platform (ThermoFisher Scientific). The assay targets genes with therapeutic relevance, with the current study focusing on the mutational status of the ESR1 and PIK3CA genes. Results: ESR1 and PIK3CA actionable variations were detected in 42% of the BC patients. Variations in the PIK3CA gene were found in 27% of patients, with the most common ones being H1047X (12%), E545X (8%), and E542K (5%). ESR1 variation were identified in 24% of patients, with hotspot mutations D538G (11%) and Y537S (8%) being the most frequent . Notably, 9% of patients exhibited co-mutations in both ESR1 and PIK3CA genes. Additionally, 13% of ESR1 -positive patients carried multiple ESR1 variations, while multiple PIK3CA alterations were detected in 10% of PIK3CA -positive patients. Conclusions: The detection of variations in genes with approved therapies, such as ESR1 and PIK3CA , is essential for the implementation of precision oncology in breast cancer patients. PIK3CA and ESR1 mutations opt breast cancer patients for on-label therapy while ESR1 mutations are also associated with acquired resistance to endocrine therapy necessitating therapeutic modifications. Molecular analysis of ctDNA highlights the feasibility of integrating liquid biopsy into routine molecular pathology, as it demonstrates high sensitivity and specificity thus offering real-time insights for dynamic therapeutic adjustments. Its incorporation into clinical oncology workflows marks a significant step forward in personalized medicine, enhancing the possibility of application of on-label therapies hence improving patient outcomes.