Genetic biomarkers associated with response to palliative radiotherapy in patients with painful bone metastases
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".