Genetic biomarkers associated with response to palliative radiotherapy in patients with painful bone metastases.

BACKGROUND

Palliative radiotherapy (RT) is effective in patients with painful bone metastases. Genetic factors may identify subgroup of patients who responded to RT. To identify DNA biomarkers associated with response to palliative RT.

METHODS

Patients who received a single 8 Gy dose of RT for painful bone metastases were categorised into responders (n=36), non-responders (NR) (n=71). Saliva samples were sequenced to identify single-nucleotide variants (SNVs) in genes with known disease-causing variants from inflammation, radiation response, and DNA damage pathways. In univariate analysis, Cochran-Armitage trend tests were used to identify SNVs that associated with pain response (P<0.005), and the Penalized LASSO method with minimum Bayesian Information Criterion was used to identify multi-SNVs that jointly predict pain response to RT. The corresponding estimated effect of the multi-SNVs were used to drive the prognostic score for each patient. Based on it, patients were divided into 3 equal size risk groups.

RESULTS

Forty-one significant variants were identified in univariate analysis. Multivariable analysis selected 14 variants to generate prognostic scores, adjusting for gender and primary cancer site. Eighty-nine percent of patients in the high prognostic group responded to palliative radiation therapy (P=0.0001). Estimated effect sizes of the variants ranged from 0.108-2.551. The most statistically significant variant was a deletion at position 111992032 in the ataxin gene ATXN2 (P=0.0001). Five variants were non-synonymous, including AOAH rs7986 (P=0.0017), ZAN rs539445 (P=0.00078) and rs542137 (P=0.00078), RAG1 rs3740955 (P=0.0014), and GBGT1 rs75765336 (P=0.0026).

CONCLUSIONS

SNVs involved in mechanisms including DNA repair, inflammation, cellular adhesion, and cell signalling have significant associations with radiation response. SNVs with predictive power may stratify patient populations according to likelihood of responding to treatment, therefore enabling more efficient identification of beneficial strategies for pain management and improved resource utilisation.