The preclinical study of the mechanism of action of anticancer small molecules is challenging due to the complexity of cancer biology and the fragmentary nature of available data. With the aim of identifying a protein subset characterizing the cellular activity of anticancer peptides, we used differential mass spectrometry to identify proteomic changes induced by two peptides, LR and [d-Gln(4)]LR, that inhibit cell growth and compared them with the changes induced by a known drug, pemetrexed, targeting the same enzyme, thymidylate synthase. The quantification of the proteome of an ovarian cancer cell model treated with LR yielded a differentially expressed protein data set with respect to untreated cells. This core set was expanded by bioinformatic data interpretation, the biologically relevant proteins were selected, and their differential expression was validated on three cis-platinum sensitive and resistant ovarian cancer cell lines. Via clustering of the protein network features, a broader view of the peptides' cellular activity was obtained. Differences from the mechanism of action of pemetrexed were inferred from different modulation of the selected proteins. The protein subset identification represents a method of general applicability to characterize the cellular activity of preclinical compounds and a tool for monitoring the cellular activity of novel drug candidates.
Keywords: anticancer drugs; bioinformatics; drug targets; folate pathway; human thymidylate synthase peptidic inhibitors; label-free quantification; ovarian cancer; pemetrexed.