Determination of platinum-resistance of women with ovarian cancer by FTIR spectroscopy combined with multivariate analyses and machine learning methods.

Patients with high-grade ovarian cancer have a poor prognosis, thus effective treatment remains an unmet medical issue of high importance. Moreover, finding the reason for resistance to cisplatin is a crucial task for the improvement of anti-cancer drugs. In this study, we showed for the first time a chemical difference in a serum collected from platinum-resistance and platinum-sensitive women suffering from ovarian cancer using Fourier Transform InfraRed (FTIR) spectroscopy followed by a data analysis by Principal Component Analysis (PCA), Hierarchical Component Analysis (HCA) and 4 different machine learning algorithms. Obtained results showed a shift of POsymmetric vibrations, amide III and amide II were observed on the FTIR spectrum of the serum collected from platinum-resistance women in comparison with the spectrum of the serum from platinum-sensitive women. Furthermore, PCA analysis clearly demonstrated the most important role of amide II and amide I in the differentiation of platinum-sensitive and platinum-resistance women. In addition, machine learning algorithms showed the important role of wavenumber at 1631 cm(amide I) and wavenumber at 2993 cm (asymmetric stretching CH vibrations). The accuracy of the obtained results was above 92%. Summarizing, FTIR can be used in detection platinum-resistance phenomena.