Genome-wide enriched pathway analysis of acute post-radiotherapy pain in breast cancer patients: a prospective cohort study.
Hum Genomics. 2019 Jun 13;13(1):28
Authors: Lee E, Takita C, Wright JL, Slifer SH, Martin ER, Urbanic JJ, Langefeld CD, Lesser GJ, Shaw EG, Hu JJ
BACKGROUND: Adjuvant radiotherapy (RT) can increase the risk of developing pain; however, the molecular mechanisms of RT-related pain remain unclear. The current study aimed to identify susceptibility loci and enriched pathways for clinically relevant acute post-RT pain, defined as having moderate to severe pain (pain score ≥ 4) at the completion of RT.
METHODS: We conducted a genome-wide association study (GWAS) with 1,344,832 single-nucleotide polymorphisms (SNPs), a gene-based analysis using PLINK set-based tests of 19,621 genes, and a functional enrichment analysis of a gene list of 875 genes with p < 0.05 using NIH DAVID functional annotation module with KEGG pathways and GO terms (n = 380) among 1112 breast cancer patients.
RESULTS: About 29% of patients reported acute post-RT pain. None of SNPs nor genes reached genome-wide significant level. Four SNPs showed suggestive associations with post-RT pain; rs16970540 in RFFL or near the LIG3 gene (p = 1.7 × 10-6), rs4584690, and rs7335912 in ABCC4/MPR4 gene (p = 5.5 × 10-6 and p = 7.8 × 10-6, respectively), and rs73633565 in EGFL6 gene (p = 8.1 × 10-6). Gene-based analysis suggested the potential involvement of neurotransmitters, olfactory receptors, and cytochrome P450 in post-RT pain, whereas functional analysis showed glucuronidation (FDR-adjusted p value = 9.46 × 10-7) and olfactory receptor activities (FDR-adjusted p value = 0.032) as the most significantly enriched biological features.
CONCLUSIONS: This is the first GWAS suggesting that post-RT pain is a complex polygenic trait influenced by many biological processes and functions such as glucuronidation and olfactory receptor activities. If validated in larger populations, the results can provide biological targets for pain management to improve cancer patients’ quality of life. Additionally, these genes can be further tested as predictive biomarkers for personalized pain management.
PMID: 31196165 [PubMed – in process]