Evaluation of the adjuvant radiation treatment-effect heterogeneity using genomic signature for locoregional relapse and long-term outcome.
Bibliographic record
Abstract
1031 Background: In a retrospective analysis of two similar randomized radiation therapy (RT) trials (i.e. British Columbia (BC) and DBCG 82b), we reported significant survival benefits for post-mastectomy RT in Luminal A. Here we examined the predictive value of additional genomic profiles in the BC trial for loco-regional recurrences (LRR) and breast cancer survival (BCSS) in node-positive, pre-menopausal breast cancer patients randomized to adjuvant chemoradiation or chemotherapy. Methods: In the BC trial, 318 patients received adjuvant cyclophosphamide, methotrexate, fluorouracil and were randomized to with or without postmastectomy RT. From 145 formalin fixed paraffin embedded tissues available, expression profiles of 66 genes were done with the Nanostring nCounter. Treatment effects on LRR and BCSS events were examined by subpopulation treatment effect pattern plots. The research-based PAM50 proliferation score, Risk of Recurrence score (ROR-T and ROR-PT), and genes related to basal-like (ie. 13-genes VEGF-signature (VEGF-s), RAD17, RAD50 and RB1) were calculated. Results: Overall, patients in the RT arm (n= 69) were significantly associated with better LRR and BCSS than the non-RT-treated arm (n = 76). No significant treatment-effect heterogeneity was detected for VEGF-s, RAD17 and RAD50 score. Patients with lower RB1 mRNA level, and higher proliferation score, had better LRR survival when they received RT (Table). The patterns of treatment efficacy on LRR and BCSS were the most significant for the varying levels of risk score (ROR-T, -PT), particularly for patients with higher scores (Table) who showed the poorest prognosis, but whom may still benefit from adjuvant RT. Conclusions: RB1, proliferation score and ROR-T predicted LRR and BCSS benefit for adjuvant RT. The clinical utility of these biomarkers as predictor requires confirmation in a second independent trial. STEPP analysis of the treatment effect of adjuvant RT at 10-years. Covariate Interaction test P LRR BCSS RB1 mRNA level KM 0.08 0.49 HR 0.03 0.41 Proliferation score KM 0.02 0.17 HR 0.06 0.24 ROR-T KM 0.01 < 0.001 HR 0.21 0.02 ROR-PT KM 0.02 0.09 HR 0.1 0.04 Abbreviations: KM, Kaplan–Meier; HR, hazard ratio.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".