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Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors
B J Bongers1, M Gorostiola González1,2, X Wang1
1Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
Abstract:
G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A "DRY" motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.
Insights
This study identifies G Protein-coupled receptors (GPCRs) with mutations linked to cancer, highlighting conserved motifs and discovering novel cancer targets like P2RY10 for further research.
Area of Science:
- Biochemistry
- Genomics
- Oncology
Background:
- G Protein-coupled receptors (GPCRs) are crucial drug targets and frequently mutated in cancer.
- Understanding GPCR mutations in cancer is vital for therapeutic development.
Purpose of the Study:
- To identify GPCRs with cancer-associated mutations by comparing patient data with natural human genetic variation.
- To rank GPCRs based on mutation frequency and conservation for potential cancer relevance.
Main Methods:
- Utilized mutation data from the Genomic Data Commons and natural variation data from the 1000 Genomes Project.
- Analyzed mutation patterns across GPCR classes and conserved motifs using Two-Entropy Analysis.
- Employed Pareto Front Ranking to prioritize GPCRs based on cancer mutation frequency and conservation.
Main Results:
- Observed mutation enrichment in cancer patients within conserved GPCR motifs, like the Class A "DRY" motif.
- Confirmed correlation between residue conservation and cancer mutation frequency.
- Identified known cancer targets (LPA, mGlu receptors) and novel candidates (e.g., P2RY10).
Conclusions:
- Presents a ranked list of GPCRs with significant cancer-associated mutations.
- Highlights specific GPCR motifs and residues critical in cancer development.
- Provides a foundation for experimental validation of novel GPCR cancer targets.
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