A preliminary Raman spectroscopic study of urine: diagnosis of breast cancer in animal models

Abstract

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.

Breast Cancer Diagnosis by Surface-Enhanced Raman Scattering (SERS) of Urine

“…The confusion matrix corresponding to the PCA-LDA of SERS spectra from urine samples is presented in Figure 5b and it corresponds to a specificity of 95%, a sensitivity of 81% and an overall accuracy of 88%. For comparison, in the study by del Mistro et al on n = 20 urine samples from prostate cancer patients, the authors reported an overall accuracy of 95% [11], while in the preliminary Raman study on urine from rats with breast cancer the sensitivity and specificity reported by the authors was 80% and 72%, respectively, using unprocessed urine and 78% and 91%, respectively, using concentrated urine [10]. Thus, the results of our study are in line with previous reports on the use of Raman and SERS spectroscopy of urine for discriminating between cancer patients and controls, which reported similar figures of merit [10,11].…”

“…For instance, preliminary results in a murine model showed that Raman spectra of urine samples displayed distinguishable features in the case of rats with breast cancer [10]. Similarly, Bonifacio et al showed on n = 20 samples that the SERS spectra of urine displayed distinguishable modifications in the case of patients with prostate cancer [11].…”

Saliva Preparation Method Exploration for ATR-FTIR Spectroscopy: Towards Bio-fluid Based Disease Diagnosis

“…Moreover, most studies use centrifugation as a processing step, followed by use of the supernatant for FTIR. Our previous studies have shown the effectiveness of using completely dehydrated samples for bio-fluid based study 27 . Therefore, we used two collection and three processing methods as mentioned in the Materials and Methods section.…”

Raman Spectroscopy and Imaging for Cancer Diagnosis

“…Bhattacharjee et al employed Raman spectroscopy to diagnose breast cancer using urine in a rat model and obtained classification efficiencies of 80% and 72% by using principal component analysis and principal component-linear discriminant analysis, respectively [ 14 ]. In parallel, Elumalai et al utilized Raman spectroscopy to characterize urine of normal and oral cancer patients and found that principal component analysis-based linear discriminant analysis was able to differentiate normal patients from cancer patients with an accuracy of 93.7%, a sensitivity of 98.6%, and a specificity of 87.1% [ 15 ].…”