Breast cancer chemotherapy treatment monitoring based on serum sample Raman spectroscopy.

In this paper, breast cancer patients were monitored throughout their chemotherapy treatments (CHT), with blood serum sample Raman spectroscopy and multivariate analysis, approximately for a year. First of all, we discriminate between healthy and clinically diagnosed breast cancer patients. Breast cancer detection in terms of sensitivity and specificity were 87.14% and 90.55% respectively. Although no shifts of peaks in mean spectrum of samples from breast cancer patients were found with respect to the mean spectrum from control patients, some peaks did show clear differences in intensity, the greatest disparities found at 509, 545, 1063, 1103, 1338, 1556, 1083 and 1449 cm- 1 are associated with amino acids and phospholipid, 1246 and 1654 cm- 1, corresponding to amide III and I, respectively. Other peaks of interest encountered at 450, 661, 890, 917 and 1405 cm- 1 are associated to glutathione. Then, 6 breast cancer patients were monitored during their chemotherapy treatments, the results were in complete correspondence with their medical records, enabling a detailed study of the evolution of each patient's cancer. A special interest arose in the possible correlation between the intensity of Raman peak, 450 cm- 1, corresponding to glutathione and evolution of cancer throughout CHT, i.e., glutathione appears to be a good candidate as breast cancer biomarker. The results confirmed that Raman spectroscopy and PCA are, not only a good support to current breast cancer detection techniques, but could also be excellent techniques to monitor more efficiently breast cancer patients undergoing CHT, using blood serum samples which are a lot less invasive than other methods.

Discrimination of different cancer types clustering Raman spectra by a super paramagnetic stochastic network approach.

Based in high sensitivity and specificity reported recently in detection of the cancer, the technique of Raman spectroscopy is proposed to discriminate between breast cancer, leukemia and cervical cancer using blood serum samples from patients officially diagnosed. In order to classify Raman spectra, clustering method known as Super Paramagnetic Clustering based on statistical physics concepts with a stochastic approach was implemented. Comparing firstly average Raman spectra of the three cancers, some peaks that allowed differentiating one cancer from other were identified, however, other peaks allowed concluding that there are biochemical similarities among them. According to these spectra, the band associated with amide I (1654 cm-1) and one of two shoulders assigned to amide III (1230-1282 cm-1) allowed discriminating leukemia from breast and cervical cancer, whereas band 714 cm-1 (polysaccharides) achieves to differentiate cervical cancer from leukemia and breast cancer, and bulged region, 1040 - 1100 cm-1 (phenylalanine, phospholipid) discriminated breast cancer from leukemia and cervical cancer. Subsequently, Super Paramagnetic Clustering method was applied to Raman spectra to study similarity relationships between cancers based on the biochemical composition of serum samples. Finally, as a cross check method, the standard method to classify Raman spectra of breast cancer, leukemia and cervical cancer, known as principal components analysis, was used showing excellent agreement with results of Super Paramagnetic Clustering method. Preliminary results demonstrated that Raman spectroscopy and Super Paramagnetic Clustering method can be used to discriminate between breast cancer, leukemia and cervical cancer samples using blood serum samples.

Type 2 diabetes detection based on serum sample Raman spectroscopy.

In this work, we propose to the Raman spectroscopy as a new technique for the detection of the type 2 diabetes using blood serum samples. The serum samples were obtained from 15 patients who were clinically diagnosed with type 2 diabetes mellitus and 20 healthy volunteers. The average spectra showed equally intense peaks as, 695 cm-1, the doublet of tyrosine at 828 and 853 cm-1, phenylalanine at 1002 and 1028 cm-1, the phospholipid shoulder at 1300-1345 cm-1, and proteins (amide I) at 1654 cm-1. The major differences were found at 661 and 1404 cm-1 (glutathione), 714 (polysaccharides), 605 (Phe), 545 cm-1 (tryptophan), and the shoulder of amide III at 1230-1282 cm-1, where seem to disappear in the diabetes spectrum. On the contrary, the region that is more highlighted due to that diabetes peaks are clearly more intense was 897-955 cm-1. Principal component analysis and linear discriminate analysis were employed for developing discrimination method. The first three principal components provided a classification of the samples from healthy and diabetes patients with high sensitivity and specificity. In addition, when the first principal component was plotted as a function of the Raman shift, it revealed these shifts accounted for the greatest differences between control and diabetes samples, which coincided with the shifts of spectral differences shown by mean spectra. Our results demonstrated that serum sample Raman spectroscopy promises to become a non-invasive support tool of the currently applied techniques for type 2 diabetes detection, decreasing the false-positive cases.