It is demonstrated that a novel multivariate analysis technique can discriminate with accuracies in the range 81–97% between Fourier transform infrared (FTIR) images of esophageal cancer OE19 and OE21 cell lines, and between esophageal cancer associated myofibroblast (CAM) and adjacent tissue myofibroblast (ATM) cells. The latter cells are morphologically indistinguishable but are known to have functionally important differences in their capacity to stimulate cancer cell growth; this report provides the first accurate spectral discrimination between CAM and ATM cells taken from the same patient. Rapid and accurate discrimination between cell types was achieved, and key wavenumbers were identified which uniquely discriminate between all four cell types. This metrics-based analysis (MA) method is shown to be unique for distinguishing between cancer stromal cells from the same patient. The key wavenumbers differ significantly from those typically found to discriminate between various esophageal cell and tissue types. A comparison is made between the MA and the established Random Forest method, and the advantages of the MA are discussed. Crucially the findings suggest a novel method that allows cancer staging based discrimination of the stromal cell types that provide the niche for tumor development.
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Condensed Matter Physics