Predictive biomarkers of radiotherapy- related dermatitis, xerostomia, mucositis and dysphagia in head and neck cancer: A systematic review.

Background
Radiotherapy is essential for treating head and neck cancer but often leads to severe toxicity. Traditional predictors include anatomical location, tumor extent, and dosimetric data. Recently, biomarkers have been explored to better predict and understand toxicity. This review aims to summarize the current literature, assess data quality, and guide future research.Methods
Two reviewers independently screened EMBASE and PubMed for studies published between 2010 and 2023. Endpoints were dermatitis, mucositis, sticky saliva/xerostomia, and dysphagia. Statistical analysis was performed using R, and bias assessed via a modified QUIPS questionnaire. Pathway analysis was conducted using gProfiler. The study adhered to PRISMA and COSMOS-E guidelines and was registered in the PROSPERO database (#CRD42023361245).Results
Of 2,550 abstracts, 69 publications met the inclusion criteria. These studies involved a median of 81 patients, primarily male (75 %), with common primary tumors in the nasopharynx (32 %) and oropharynx (27 %). Most patients (84 %) had advanced disease (stage III/IV). The most frequently studied biomarkers were DNA-based single-nucleotide polymorphisms (SNPs, 59 %), salivary proteins (13 %), and bacteria (10 %). Ten statistically-significant biomarkers (all SNPs) in low-bias publications were identified, particularly in DNA repair and cell detoxification pathways. Data quality was often poor and few validation studies were present in the dataset.Conclusion
This review provides an overview of the research landscape, highlights research gaps and provides recommendations for future research directions. We identified several potential biomarkers, particularly in DNA repair pathways, that align with current understanding of radiation-induced cell damage. However, the overall data quality was poor, with key clinical variables often missing. Overall, rigorous standardization of reporting, validation studies and multi-center collaborations to increase study power and sample sizes are necessary to build high-level evidence for clinical application.