Protein profile study of breast-tissue homogenates by HPLC-LIF.

Proteomics is a promising approach for molecular understanding of neoplastic processes including response to treatment. Widely used 2D-gel electrophoresis/Liquid chromatography coupled with mass spectrometry (LC-MS) are time consuming and not cost effective. We have developed a high-sensitivity (femto/subfemtomoles of protein/20 mul) High Performance Liquid Chromatography-Laser Induced Fluorescence HPLC-LIF instrument for studying protein profiles of biological samples. In this study, we have explored the feasibility of classifying breast tissues by multivariate analysis of chromatographic data. We have analyzed 13 normal, 17 malignant, 5 benign and 4 post-treatment breast-tissue homogenates. Data was analyzed by Principal Component Analysis PCA in both unsupervised and supervised modes on derivative and baseline-corrected chromatograms. Our findings suggest that PCA of derivative chromatograms gives better classification. Thus, the HPLC-LIF instrument is not only suitable for generation of chromatographic data using femto/subfemto moles of proteins but the data can also be used for objective diagnosis via multivariate analysis. Prospectively, identified fractions can be collected and analyzed by biochemical and/or MS methods.

Autofluorescence of breast tissues: evaluation of discriminating algorithms for diagnosis of normal, benign, and malignant conditions.

OBJECTIVE: We evaluated different discriminating algorithms for classifying laser-induced fluorescence spectra of normal, benign, and malignant breast tissues that were obtained with 325-nm excitation. BACKGROUND DATA: Mammography and histopathology are the conventional gold standard methods of screening and diagnosis of breast cancers, respectively. The former is prone to a high rate of false-positive results and poses the risk of repeated exposure to ionizing radiation, whereas the latter suffers from subjective interpretations of morphological features. Thus the development of a more reliable detection and screening methodology is of great interest to those practicing breast cancer management. Several studies have demonstrated the efficacy of optical spectroscopy in diagnosing cancer and other biomedical applications. MATERIALS AND METHODS: Autofluorescence spectra of normal, benign, and malignant breast tissues, with 325-nm excitation, were recorded. The data were subjected to diverse discriminating algorithms ranging from intensities and ratios of curve-resolved bands to principal components analysis (PCA)-derived parameters. RESULTS: Intensity plots of collagen and NADPH, two known fluorescent biomarkers, yielded accurate classification of the different tissue types. PCA was carried out on both unsupervised and supervised methods, and both approaches yielded accurate classification. In the case of the supervised classification, the developed standard sets were verified and evaluated. The limit test approach provided unambiguous and objective classification, and this method also has the advantage of being user-friendly, so untrained personnel can directly compare unknown spectra against standard sets to make diagnoses instantly, objectively, and unambiguously. CONCLUSION: The results obtained in this study further support the efficacy of 325-nm-induced autofluorescence, and demonstrate the suitability of limit test analysis as a means of objectively and unambiguously classifying breast tissues.

Biochemical correlation of Raman spectra of normal, benign and malignant breast tissues: a spectral deconvolution study.

The aim of this study was to understand and correlate spectral features and biochemical changes in normal, fibroadenoma and infiltrating ductal carcinoma of breast tissues using Raman spectra that were part of the spectroscopic models developed and evaluated by us earlier. Spectra were subjected to curve fitting and intensities plots of resultant curve resolved bands were computed. This study has revealed that fat (1301 and 1440 cm(-1)), collagen (1246, 1271, and 1671 cm(-1)) and DNA (1340 and 1480 cm(-1)) bands have strong presence in normal, benign and malignant breast tissues, respectively. Intensity plots of various combinations of curved resolved bands were also explored to classify tissue types. Combinations of fat (1301 cm(-1)) and collagen (1246, 1271, and 1671 cm(-1))/amide I; DNA (1340 cm(-1)) and fat (1301 cm(-1)); collagen (1271 cm(-1)) and DNA (1480 cm(-1)) are found to be good discriminating parameters. These results are in tune with findings of earlier studies carried out on western population as well as our molecular biological understanding of normal tissues and neoplastic processes. Thus the finding of this study further demonstrates the efficacy Raman spectroscopic approaches in diagnostic applications as well as in understanding molecular phenomenon in breast cancers.

Raman spectroscopy of breast tissues.

Breast cancer is one of the leading female cancers. The major drawback of the gold standard of screening, mammography, is the high rate of false reports, aside from the risk from repeated exposure to harmful ionizing radiations. Histopathology, the gold standard of diagnosis, is time consuming and often prone to subjective interpretations. Molecular level diagnosis 'omics' is becoming increasingly popular; among these is metabolomics, diagnosis based on 'metabolic fingerprinting'. In the present article we review a Raman spectroscopic approach to metabolic fingerprinting in breast cancer detection. This review opens with a brief background on anatomical and etiological aspects of breast cancers. We present an overview of conventional detection approaches in breast cancer screening and diagnosis methods, followed by a concise note on the basics of optical spectroscopy and its applications in the screening/diagnosis of breast malignancy. We present the recent developments in Raman spectroscopic diagnosis of breast cancers and also share our experience in Raman spectroscopic classification of normal, benign and malignant breast tissues. Perspectives and current status of Raman spectroscopic screening/diagnosis of breast cancers are also discussed.

Discrimination of normal and malignant mucosal tissues of the colon by Raman spectroscopy.

OBJECTIVE: The objective of this study was to evaluate the applicability of the discrimination parameters Mahalanobis distance, spectral residuals, and limit tests, developed by this group to differentiate normal from malignant colon tissues. BACKGROUND DATA: Colon cancers are diagnosed using fiberoptic endoscopic localization and a subsequent histopathological examination of biopsied tissue, which is highly dependent on the skill and experience of the investigator. There exists a risk of missing significant lesions, especially with carcinoma in situ lesions. Raman spectroscopy, which is sensitive to biochemical variations in the samples and amenable to multivariate statistical tools, can lead to rapid and objective detection of colon cancer. METHODS: A total of 102 spectra from 11 normal and 11 malignant ex vivo colon tissues were recorded by conventional near infrared (NIR) Raman spectroscopy (excitation wavelength of 785 nm). Spectral data were analyzed by principal components analysis (PCA) and other discriminating parameters, namely Mahalanobis distance, spectral residuals, and a multiparametric limit test approach. RESULTS: Mean malignant spectra exhibit relatively stronger bands, suggesting the presence of additional biomolecules such as protein (stronger amide III and I), lipids (1,100, 1,300 cm(1)), and DNA (1,340, 1,470 cm(1)) versus those seen in normal tissue. Mean normal spectra indicate the presence of disordered structures (hump at 1,247 cm(1)). Scores of factor 1 gave good discrimination, and this is further fine-tuned by employing Mahalanobis distance and spectral residuals as discriminating parameters. A limit test approach provided unambiguous objective discrimination. CONCLUSION: This study further supports the efficacy of Raman spectroscopy, in combination with a limit test, for discrimination of normal and malignant colon tissues. The multiparametric limit test approach is user-friendly, and a clinician or minimally trained individual could directly compare the unknown spectra against the available standard sets to make the decision instantly, objectively, and unambiguously.

Discrimination of normal, benign, and malignant breast tissues by Raman spectroscopy.

Breast cancers are the leading cancers among females. Diagnosis by fine needle aspiration cytology (FNAC) is the gold standard. The widely practiced screening method, mammography, suffers from high false positive results and repeated exposure to harmful ionizing radiation. As with all other cancers survival rates are shown to heavily depend on stage of the cancers (Stage 0, 95%; Stage IV, 75%). Hence development of more reliable screening and diagnosis methodology is of considerable interest in breast cancer management. Raman spectra of normal, benign, and malignant breast tissue show significant differences. Spectral differences between normal and diseased breast tissues are more pronounced than between the two pathological conditions, malignant and benign tissues. Based on spectral profiles, the presence of lipids (1078, 1267, 1301, 1440, 1654, 1746 cm(-1)) is indicated in normal tissue and proteins (stronger amide I, red shifted DeltaCH2, broad and strong amide III, 1002, 1033, 1530, 1556 cm(-1)) are found in benign and malignant tissues. The major differences between benign and malignant tissue spectra are malignant tissues seem to have an excess of lipids (1082, 1301, 1440 cm(-1)) and presence of excess proteins (amide I, amide III, red shifted DeltaCH2, 1033, 1002 cm(-1)) is indicated in benign spectra. The multivariate statistical tool, principal components analysis (PCA) is employed for developing discrimination methods. A score of factor 1 provided a reasonable classification of all three tissue types. The analysis is further fine-tuned by employing Mahalanobis distance and spectral residuals as discriminating parameters. This approach is tested both retrospectively and prospectively. The limit test, which provides the most unambiguous discrimination, is also considered and this approach clearly discriminated all three tissue types. These results further support the efficacy of Raman spectroscopic methods in discriminating normal and diseased breast tissues.