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From the spectroscopic analysis, using a portable commercial instrument, it is possible to obtain a diagnostic response for breast cancer, at different stages. The invention is constituted by these elements: 1. a nanostructured metallic substrate, made by gold or silver nanoparticles deposited on paper, used to amplify the spectroscopic signal; this type of SERS substrate is already known in literature, but ours has a better performance in terms of repeatability, with respect to those paper-based substrates already reported; it is easy to prepare, inexpensive, and can be prepared on large scale; innovation with respect to published procedures consists in modifying two parameters during the substrate synthesis, in particular in using concentrated (by centrifugation) nanoparticles and in using a paper with a 2 micrometers porosity; 2. the instrumental method of analysis, i.e. the use of paper-based SERS substrate to analyze biofluids such as serum. In fact, such substrate are not new (see above), but so far they have only been used with samples different from blood, serum of plasma. We were the first to find out their use with this type of samples (blood, serum and plasma). 3. a multivariate predictive model, built using a training set of spectral data from a group of patients and from a control group, capable of interpreting a spectrum in diagnostic terms. The algorithms used for such models (e.g. PCA-LDA) are already well known, but our model is unique in so far as it is built using our own data, and it has diagnostic performance parameters (sensitivity, specificity, accuracy) which are better than alternative methods (e.g. mammography).

In this contribution, we investigated whether surface-enhanced Raman scattering (SERS) of serum can be a candidate method for detecting "luminal A" breast cancer (BC) at different stages. We selected three groups of participants aged over 50 years: 20 healthy women, 20 women with early localized small BC, and 20 women affected by BC with lymph node involvement. SERS revealed clear spectral differences between these three groups. A predictive model using principal component analysis (PCA) and linear discriminant analysis (LDA) was developed based on spectral data, and its performance was estimated with cross-validation. PCA-LDA of SERS spectra could distinguish healthy from BC subjects (sensitivity, 92 %; specificity, 85 %), as well as subjects with BC at different stages, with a promising diagnostic performance (sensitivity and specificity, ≥80 %; overall accuracy, 84 %). Our data suggest that SERS spectroscopy of serum, combined with multivariate data analysis, represents a minimally invasive, easy to use, and fast approach to discriminate healthy from BC subjects and even to distinguish BC at different clinical stages.