No abstract

The scope of the work undertaken in this paper was to explore the feasibility and reliability of using the Raman signature of aromatic amino acids as a marker in the detection of the presence of breast cancer and perhaps, even the prediction of cancer development in very early stages of cancer onset. To be able to assess this hypothesis, we collected most recent and relevant literature in which Raman spectroscopy was used as an analytical tool in the evaluation of breast cell lines and breast tissue, re-analyzed all the Raman spectra, and extracted all spectral bands from each spectrum that were indicative of aromatic amino acids. The criteria for the consideration of the various papers for this study, and hence, the inclusion of the data that they contained were two-fold: (1) The papers had to focus on the characterization of breast tissue with Raman spectroscopy, and (2) the spectra provided within these papers included the spectral range of 500–1200 cm−1, which constitutes the characteristic region for aromatic amino acid vibrational modes. After all the papers that satisfied these criteria were collected, the relevant spectra from each paper were extracted, processed, normalized. All data were then plotted without bias in order to decide whether there is a pattern that can shed light on a possible diagnostic classification. Remarkably, we have been able to demonstrate that cancerous breast tissues and cells decidedly exhibit overexpression of aromatic amino acids and that the difference between the extent of their presence in cancerous cells and healthy cells is overwhelming. On the basis of this analysis, we conclude that it is possible to use the signature Raman bands of aromatic amino acids as a biomarker for the detection, evaluation and diagnosis of breast cancer.

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The use of surface-enhanced Raman spectroscopy (SERS) to delineate between the breast epithelial cell lines MCF10A, SK-BR-3, and MDA-MB-231 is explored utilizing varied morphologies of gold nanoparticles. The nanoparticles studied had spherical, star-like, and quasi-fractal (nanocaltrop) morphologies and possessed varying degrees of surface inhomogeneity and complexity. The efficacy of Raman enhancement of these nanoparticles was a function of their size, their surface morphology, and the associated density of "hot spots," as well as their cellular uptake. The spherical and star-like nanoparticles provided strong signal enhancement that allowed for the discernment among the three cell phenotypes based solely on the acquired Raman spectra. The presence of overlapping Raman band spectral regions, as well as unique spectral bands, suggests that the underlying biological differences between these cells can be accessed without the need for tagging the nanoparticles or for specific cell targeting, demonstrating the potential ubiquity of this technique in imaging any cancer. This work provides clear evidence for the potential application of SERS as a tool for mapping cancerous lesions, possibly during surgery and under histopathological analysis.