• Shanks, Jonathan H.
• Brown, Mick
• Clarke, Noel
• Gardner, Peter
• Hart, Claire Alexandra
• Vickerman, John C.
• Miyan, Jaleel
• Dwyer, John
• Gazi, Ehsan
• Lockyer, Nicholas P.

Abstract

At present, a prognosis for prostate cancer (CaP) is determined by its accurate assessment of disease grade and stage. Histopathological typing using the Gleason grading system is the most universally accepted approach for grading CaP and provides an indication as to the aggressiveness of the tumour at the time of presentation. However, this system is based upon a visual criterion of pattern recognition that is operator dependent and subject to intra- and inter-observer variability, which can result in inappropriate patient management. Thus, there is a need for a molecular based diagnostic technique to grade tissue samples in a reliable and reproducible manner. In this paper we report a prototype diagnostic classifier for Gleason graded CaP tissue, based upon the integration of FTIR microspectroscopy with linear discriminant analysis (LDA). Blind testing of this model demonstrates 80% agreement of FTIR-LDA grade to histology, for the specimens analysed. We also study the effects of connective tissue absorption upon the area ratio of peaks at A1030 cm-1/A1080 cm-1 which we use as a criterion to biospectroscopically map and distinguish areas of benign from malignant tissue. In addition, imaging time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been applied to study freeze-dried, freeze-fractured prostate cancer cells in vitro. Preliminary results demonstrate localisation of various species including K, Ca and Mg within the cytoplasm that are present at millimolar concentrations and vital to cell physiology. The soft ionisation technique employed also permits for molecular information to be obtained and this has been used to evaluate chemically, different fracture planes within the analysis area.

Topics

• impedance spectroscopy
• spectrometry
• selective ion monitoring
• secondary ion mass spectrometry
• ionisation