Tryptophan fluorescence and machine learning to study the aggressiveness of prostate cancer cell lines: A pilot study

We report on the use of label-free, native fluorescence (NFL) spectroscopy and machine learning (ML) algorithms to study the correlation of relative tryptophan levels with prostate cancer aggressiveness. Three extensively studied prostate cancer cell lines were used; PC3, an aggressive, androgen-resistant line, with a high tendency to metastasize in vivo, DU-145, a less aggressive cancer cell line, also androgen-resistant, and LNCaP, an androgen sensitive line, which has a low tendency to metastasize. Using an excitation of 300nm, differences in the NFL spectral profiles from these cell lines were found to correlate with changes in the relative concentrations of tryptophan and reduced nicotinamide adenine dinucleotide (NADH). The use of ML may present a powerful tool for the assessment of the likelihood of a cancer to metastasize. This technique could aid in the decision whether to use highly aggressive adjuvant chemotherapy or radiation therapy after surgical resection of a prostate cancer.