1478P - Dual single-nucleotide polymorphism biomarker combination to select opioid for cancer pain management

Background

We have recently exploring biomarkers that could help physicians in selecting the appropriate opioid for individual patients with cancer pain. Recently, we identified, by screening subjects for 74 pain-related single-nucleotide polymorphisms (SNPs), a SNP of CCL11 (rs17809012) as one such biomarker that was significantly associated with the analgesic effect of morphine.

Methods

In the current study, we measured the plasma concentrations of several chemokines/cytokines in pre-treatment plasma samples obtained from a total of 138 patients enrolled in our clinical study who had been randomized to a morphine group (n=70) or oxycodone group (n=68). A genotypic analysis was also carried out for several SNPs of the IL-16, the only gene that was positively screened in the plasma analysis. We then used multiple linear regression models to assess whether both of the SNPs of CCL11 and of IL-16 could be combined to accurately predict which opioid might be the most suitable to provide pain relief in patients with cancer.

Results

We identified one cytokine, IL-16, whose plasma concentrations showed a clear bias between patients with cancer pain who responded well and responded poorly to oxycodone. We also confirmed that a SNP of the IL-16 gene (rs4778889) as being significantly associated with the analgesic effect of oxycodone. We then found that the SNPs of CCL11 (rs17809012) and IL-16 (rs4778889) could be used in combination for the opioid prediction. Morphine tended to provide superior analgesic effect over oxycodone in patients with the rs4778889 TT genotype and the rs17809012 AG/GG genotype (n=45), while a trend towards a better analgesic effect of oxycodone was observed in patients with other genotype combinations of the SNPs (n=93) (P=0.0012 for the interaction).

Conclusions

Our results suggest that the IL-16 rs4778889 and CCL11 rs17809012 SNPs could serve as a potential dual-biomarker combination for personalized analgesic therapy in patients with cancer pain.

Clinical trial identification

UMIN000015579.

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

The Health Labor Sciences Research Grant and the Japan Agency for Medical Research and Development.

Disclosure

All authors have declared no conflicts of interest.