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Published on: November 2, 2020
Structural pharmacogenomics of drug-associated SNPs in oral squamous cell carcinoma
Punya Sunil1, K C Ananth Kumar1, A I Ananthakrishnan1
1Division of Data Analytics, Bioinformatics and Structural Biology (DABS), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, India.
Introduction:
Pharmacogenomics enables the interpretation of how genetic variation influences drug response, offering a route toward precision oncology. Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy characterized by marked inter-individual variability in response to chemotherapy and radiotherapy, particularly in South Asian populations. However, the mechanism through which OSCC-associated genetic variants alter protein structure and drug interactions remains poorly understood.
Methods:
To address this, we integrated OSCC-specific variants from a Southern Indian patient cohort with pharmacogenomic annotations from ClinPGx. First, OSCC variants were mapped to drug-associated single-nucleotide proteins, yielding 22 protein-altering variants. Second, structural availability and functional relevance were used to prioritize eight variants for detailed analysis. Third, homology modeling and molecular docking were applied to evaluate how these variants influence protein conformation and drug binding.
Result:
This multistep pipeline identified variants in FLT3, ATM, MUTYH, XRCC1, XPC, and MSH3 that affect DNA repair, signaling, and drug interaction interfaces. The highly prevalent FLT3 T227M (rs1933437) variant was predicted to alter receptor dimerization and potentially modulate sunitinib binding. The MUTYH Q310H (rs3219489) variant, which is located near a zinc-binding motif in the interdomain connector, was predicted to perturb metal coordination and enzyme architecture, which indicates impaired base-excision repair.
Conclusion:
These findings demonstrate how pharmacogenomics-guided structural analysis can reveal mechanistic links between OSCC-associated variants and therapeutic response. While our results are based on in silico modeling, they provide a biologically grounded framework for prioritizing variants for experimental validation and for advancing population-specific precision oncology in OSCC.
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