Gaifulina, Riana; (2017) A Study of Raman Spectroscopy as a Clinical Diagnostic Tool for the Detection of Lynch Syndrome/Hereditary NonPolyposis Colorectal Cancer (HNPCC). Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Lynch syndrome also known as hereditary non-polyposis colorectal cancer (HNPCC) is a highly penetrant hereditary form of colorectal cancer that accounts for approximately 3% of all cases. It is caused by mutations in DNA mismatch repair resulting in accelerated adenoma to carcinoma progression. The current clinical guidelines used to identify Lynch Syndrome (LS) are known to be too stringent resulting in overall underdiagnoses. Raman spectroscopy is a powerful analytical tool used to probe the molecular vibrations of a sample to provide a unique chemical fingerprint. The potential of using Raman as a diagnostic tool for discriminating LS from sporadic adenocarcinoma is explored within this thesis. A number of experimental parameters were initially optimized for use with formalin fixed paraffin embedded colonic tissue (FFPE). This has resulted in the development of a novel cost-effective backing substrate shown to be superior to the conventionally used calcium fluoride (CaF2). This substrate is a form of silanized super mirror stainless steel that was found to have a much lower Raman background, enhanced Raman signal and complete paraffin removal from FFPE tissues. Performance of the novel substrate was compared against CaF2 by acquiring large high resolution Raman maps from FFPE rat and human colonic tissue. All of the major histological features were discerned from steel mounted tissue with the benefit of clear lipid signals without paraffin obstruction. Biochemical signals were comparable to those obtained on CaF2 with no detectable irregularities. By using principal component analysis to reduce the dimensionality of the dataset it was then possible to use linear discriminant analysis to build a classification model for the discrimination of normal colonic tissue (n=10) from two pathological groups: LS (n=10) and sporadic adenocarcinoma (n=10). Using leaveone-map-out cross-validation of the model classifier has shown that LS was predicted with a sensitivity of 63% and a specificity of 89% - values that are competitive with classification techniques applied routinely in clinical practice.

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