LAUSR

Simple Summary Pain is experienced by ~55% of patients who undergo anti-cancer treatment. However, many genetic factors that are involved in the efficacy of opioid analgesics in the treatment of cancer pain remain unidentified. The aim of our genome-wide association study (GWAS) was to comprehensively explore genetic variations that are associated with opioid analgesic requirements in the treatment of cancer pain. We identified several single-nucleotide polymorphisms (SNPs) that are associated with opioid analgesic requirements, two of the most potent of which were the rs1283671 and rs1283720 SNPs in the ANGPT1 gene region, and SNPs in the SLC2A14 gene were also associated with the phenotype. These results indicate that these SNPs in the ANGPT1 and SLC2A14 genes could serve as markers that predict the efficacy of opioid analgesics in the treatment of cancer pain. Abstract Considerable individual differences have been widely observed in the sensitivity to opioids. We conducted a genome-wide association study (GWAS) in patients with cancer pain to identify potential candidate single-nucleotide polymorphisms (SNPs) that contribute to individual differences in opioid analgesic requirements in pain treatment by utilizing whole-genome genotyping arrays with more than 650,000 markers. The subjects in the GWAS were 428 patients who provided written informed consent and underwent treatment for pain with opioid analgesics in a palliative care unit at Higashi-Sapporo Hospital. The GWAS showed two intronic SNPs, rs1283671 and rs1283720, in the ANGPT1 gene that encodes a secreted glycoprotein that belongs to the angiopoietin family. These two SNPs were strongly associated with average daily opioid requirements for the treatment of pain in both the additive and recessive models (p < 5.0000 × 10−8). Several other SNPs were also significantly associated with the phenotype. In the gene-based analysis, the association was significant for the SLC2A14 gene in the additive model. These results indicate that these SNPs could serve as markers that predict the efficacy of opioid analgesics in cancer pain treatment. Our findings may provide valuable information for achieving satisfactory pain control and open new avenues for personalized pain treatment.