Prognosis of breast cancer, the most common cancer in females worldwide, has been shown to improve with early detection. Owing to disadvantages like low sensitivity, specificity, tedious sample preparation, long output times and inter-observer variance of currently available screening/diagnostic tools, rapid and objective alternatives such as Raman spectroscopy (RS) are being extensively explored. Body fluid (serum and saliva) based RS assays have shown promising results in diagnosis of oral, lung and nasopharyngeal cancers. The current study aims to explore the feasibility of breast cancer diagnosis using urine based RS. In this study, spectra were acquired from unprocessed as well as concentrated urine of controls (C) and breast tumor bearing (T) rats and analyzed using Principal Component Analysis (PCA) and Principal Component-Linear Discriminant Analysis (PC-LDA). Classification efficiencies of 80% and 72% using unprocessed urine and 78% and 91% using concentrated urine for C and T rats were achieved. Thus, results suggest the possibility of breast cancer diagnosis using urine based RS. Further, spectra were also acquired from concentrated urine samples collected prior to breast tumor development (TT) in rats and from rats that did not develop tumors despite carcinogen treatment (NTT). Concentrated urine of NTT rats could be classified as 'normal' (C or NTT) with ∼83% efficiency whereas concentrated urine from visibly and palpably normal rats that eventually developed tumor (TT rats) could be classified as 'abnormal' (TT or T) with ∼72.5% efficiency using PC-LDA. These results suggest the possibility of detecting biochemical changes occurring prior to tumor development using urine based RS.

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Risk of recurrence is a major problem in breast cancer management. Currently available prognostic markers have several disadvantages including low sensitivity and specificity, highlighting the need for new prognostic techniques. One of the candidate techniques is serum-based Raman spectroscopy (RS). In this study, feasibility of using RS to distinguish 'pre' from 'post' breast tumor resection serum in rats was explored. Spectral analysis suggests change in proteins and amino acid profiles in 'post' compared to 'pre-surgical' group. Principal-Component-Linear-Discriminant-Analysis shows 87% and 91% classification efficiency for 'pre' and 'post-surgical' groups respectively. Thus, the study further supports efficacy of RS for theranostic applications.

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