Raman spectroscopic analysis to identify chemical changes associated with different subtypes of breast cancer tissue samples

• Main Author: Talari, A. C. S
• Format: Dissertation
• Language: English
• Published: University of Sheffield 2015

Breast cancer incidence rates are increasing in women worldwide with the highest rates reported in developed countries. A combination of screening approaches, Immuno-histopathology and gene profiling analyses are currently used for diagnosis and typing but have their own limitations in understanding disease and its subtypes. Raman spectroscopy (RS) has attained great attention from biomedical researchers due to its non-invasive and non-destructive detection approach. Chemometrics is one of the powerful tools used in spectroscopic research to enhance its sensitivity. RS was used to characterise and differentiate two breast cancer and one normal breast cell lines (MDA-MB-436, MCF-7 and MCF-10A) and spectra of the cell lines have revealed basic differences in the concentration of biochemical compounds such as lipids, nucleic acids and proteins Raman peaks were found to differ in intensity and principal component analysis (PCA) was able to identify variations that lead to accurate and reliable separation of the three cell lines. Linear discriminant analysis (LDA) model of three cell lines was predicted with 100% sensitivity and 91% specificity. RS studies were extended from single cells to multiple cell spheroids. Human breast cancer cell lines (T-47D) were grown as spheroids and a combination of RS and Cluster analysis were employed to understand biochemical fingerprint and differentiation of normal proliferating, hypoxic and necrotic regions of spheroids. These variations may be useful in identifying new spectral markers and further understanding of cancer metabolism. Finally, Human breast biopsies on Tissue microarray (TMA) slide were analysed using RS and Chemometrics approaches. Biopsies were classified as luminal A, luminal B, HER2 and triple negative subtypes to understand chemical changes associated with breast cancer subtypes. Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter dataset biochemical changes associated with lipids, collagen and nucleic acid content. In summary, RS has offered great potential understanding breast cancer from cell line level to multicellular spheroid to higher architecture of tissue. This study has explored new area to understand biochemical fingerprint of breast biopsies, which is complementary to current trends of molecular profiling and immuno histopathological approaches.