Covalent targeting of acquired cysteines in cancer

Abstract

The thiolate side chain of cysteine has a unique functionality that drug hunters and chemical biologists have begun to exploit. For example, targeting cysteine residues in the ATP-binding pockets of kinases with thiol-reactive molecules has afforded increased selectivity and potency to drugs like imbrutinib, which inhibits the oncogene BTK, and CO-1686 and AZD9291 that target oncogenic mutant EGFR. Recently, disulfide libraries and targeted GDP-mimetics have been used to selectively label the G12C oncogenic mutation in KRAS. We reasoned that other oncogenes contain mutations to cysteine, and thus screened the Catalog of Somatic Mutations in Cancer for frequently acquired cysteines. Here, we describe the most common mutations and discuss how these mutations could be potential targets for cysteine-directed personalized therapeutics.

Figure 1. The most frequently acquired cysteines in cancer

Pie charts: Percentage of mutations that lead to the acquisition of a cysteine in KRAS, FGFR3, TP53, IDH1, GNAS, FBXW7, CTNNB1 and DNMT3A in cancer based on the COSMIC database. Bars: Number of tumors with a certain acquired cysteine for the 15 most frequently acquired cysteines in cancer in blue. Other acquired cysteines in the selected proteins are shown in grey.

Figure 2. Amino acid abundance

The abundance of each amino acid in the proteome depends on several factors including codon degeneracy. Relative to its abundance in the human proteome (2,31%), cysteine is found most often (6,06%; a 2,62-fold increase) as a result of a missense mutation in cancer in the COSMIC database.

Figure 3. structure and targetability of acquired cysteines

Local Protein structure for the top fifteen acquired cysteines in cancer. Structural data was available for KRAS G12C and TP53 Y220C. The other structures are based on the structure of the wild type protein or in case of IDH1 on the R132H mutant. The acquired cysteine was modeled in these structures using the mutagenesis tool in the PyMol software package by selecting a rotamer with the lowest hypothetical steric hindrance. The targetability scores given are based on the interpretation of FTMap analysis and includes the number of, distance to and strength (S) of ligand-binding hot spots in the vicinity of the acquired cysteine [19]. The hotspot parameters can be found in supplementary table 2.

Figure 4. Different strategies for finding compounds to target acquired cysteines

A. The first strategy is based on modification of an available non-covalent binder to include a cysteine-reactive moiety that cross-links it to an acquired cysteine in or near the binding pocket of the non-covalent binder. B. The second strategy uses a screen with a library of thiophiles as its basis for drug discovery. Thiophiles may include disulfides and irreversible and reversible electrophiles like acrylates and cyano-acrylamides respectively conjugated to different R groups that can provide selectivity for the target.