Observational analysis identifies TGF-β1 SNPs as protective against radiation toxicities in head and neck cancer, suggesting personalized treatment strategies.
Background: Head and neck cancer (HNC), predominantly head and neck squamous cell carcinoma (HNSCC), poses a significant health burden globally. Intensity-modulated radiation therapy (IMRT) has improved outcomes in HNC; however, radiation-induced toxicities, particularly oral mucositis and dermatitis, remain critical concerns. Genetic variations, such as single-nucleotide polymorphisms (SNPs) in the TGF-β1 gene (-509C>T, rs1800469), may modulate these toxicities and impact treatment response. Materials and Methods: This observational analytical study included 50 patients with locally advanced HNC undergoing IMRT at a tertiary care center in North India. Genotyping of TGF-β1 (-509C>T, rs1800469) was conducted using PCR-RFLP. Acute radiation toxicities were assessed weekly during IMRT and graded using CTCAE v4.0. Radiotherapy response was evaluated three months posttreatment using RECIST v1.1. Statistical analyses, including Chi-square test, multivariate regression, explored associations between genotypes, toxicities, and treatment outcomes. Results: The cohort comprised 50 patients (94% male, mean age: 53.3 ± 12.35 years). TGF-β1 (-509C>T, rs1800469) genotypes were distributed as CC (48%), TC (30%), and TT (22%), conforming to the Hardy-Weinberg equilibrium. Grade 1 oral mucositis and dermatitis occurred in 48% and 56%, respectively, with higher grades in 52% and 44%. The CC genotype was significantly associated with reduced risk of severe oral mucositis ( P = 0.046; OR = 0.31), while the TC genotype conferred protection against severe dermatitis ( P = 0.035; OR = 0.26). Conclusion: The CC and TC genotypes of TGF-β1 (-509C>T, rs1800469) exhibit protective roles against acute radiation toxicities in HNC patients receiving IMRT. These findings underscore the potential of TGF-β1 SNPs as biomarkers for toxicity risk stratification, enabling personalized therapeutic approaches.
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Krishnan et al. (2026) studied this question.
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