Raman and Surface Enhanced Raman spectroscopy analysis of breast cancer cell lines with different HER2 expression profiles

Abstract

Assessing HER2 expression in breast cancer cells holds significant diagnostic and prognostic importance. Traditional methods like immunohistochemistry and in situ hybridization suffer from low sensitivity and misclassification rates. In this frame, techniques such as vibrational microscopies can ensure, together with low costs and analytical speed, both high accuracy and precision. Herein, we propose a combined Raman and SERS approach for characterizing 4 breast cancer cell lines and normal cells with varying HER2 expression levels. We show that Raman spectroscopy offers a promising alternative, providing unique molecular fingerprints for cell types based on their biochemical signatures. Its non-invasive nature and ability to detect subtle changes in cellular metabolism make it ideal for cancer cell analysis. Coupled with machine learning techniques like PCA and LDA, Raman spectroscopy can classify different breast cancer subcategories accurately. Surface Enhanced Raman Scattering (SERS) further enhances sensitivity, allowing the detection of single molecules like HER2 receptors. Overall, our results enable fast screening of cancer subpopulation in terms of HER2 concentration and macromolecule cell content. Integration of Raman spectroscopy with SERS offers precise identification and opens avenues for personalized therapies.