A chemocentric approach to the identification of cancer targets

Abstract

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided.

Figure 1. Schematic flowchart of the DIVISS approach applied in this work leading to the identification of 115 proteins of potential relevance to cancer.

Figure 2

a) Correlation of two independent viability values determined for the same compound and b) distribution of viability values for the chemical library of 30,000 compounds.

Figure 3

a) Distribution of the cytotoxicity (IC50 values) of the selected compounds on HCT116 and MRC5 cells and b) distribution of the selective cytotoxicity against HCT116. NT means “non toxic”.

Figure 4

a) Venn diagram of the protein targets predicted for the selective cytotoxic compounds to HCT116 and MRC-5 cell lines; b) distribution across protein families of the 115 targets predicted to interact uniquely with selective cytotoxic compounds to tumor cells; and c) distribution across enzyme classes of the 67 enzymes present in the list of 115 putative cancer targets.

Figure 5. Distribution of oncogene probabilities for the proteins predicted uniquely for compounds selective to HCT116 (black) and MRC-5 (light grey) and the proteins found in both selective sets (dark grey).

NA collects all proteins for which oncogene probabilities were not available from CGPrio .

Figure 6. Profiles of experimental affinity data of the 20 drugs, among 4,819, hitting more than 5 targets found solely in tumor selective compounds.

Only affinities above 1 µM are considered. Color coding reflects pAffinity ranges: white 6–7; light grey 7–8; dark grey 8–9; black >9. Color codes for targets refer to HDACs (yellow), kinases (orange), and other (green).