Prognostic and predictive capacity of tumor infiltrating lymphocytes in the MA.20 regional node radiotherapy trial
Bibliographic record
Abstract
Abstract Prognostic and predictive value of immune infiltrates in the context of regional nodal radiation (RNI) for breast cancer has not been assessed. Stromal tumor infiltrating lymphocytes (sTILs) were assessed on scanned images of hematoxylin and eosin (H&E) stained sections and by CD8 immunohistochemistry on tissue microarrays available from the MA.20 trial. Cox proportional modelling was used, and hazard ratios (HR) with 95% confidence intervals (CI) are reported for primary and secondary endpoints. Predictive value was assessed by an interaction test. H&E sTILs (continuous parameter) were prognostic for distant-DFS (HR 0.99, 95% CI 0.98–1.00, P = 0.04). CD8+sTILs were associated with significantly improved disease-free survival (DFS) (HR 0.99, 95% CI 0.98–1.00, P = 0.02) and distant-DFS (HR 0.98, 95% CI 0.97–0.99, P = 0.0002). CD8+sTILs was predictive of benefit from RNI for distant-DFS (continuous variable: HR 0.98, 95% CI 0.96–1.00, P (interaction) = 0.04; exploratory categorical variable: CD8+ sTILs < 44, HR = 0.83; 95% CI 0.57–1.21, and CD8+ sTILs ≥ 44; HR 0.09; 95% CI 0.01–0.74, P (interaction) = 0.04). In MA.20 breast cancer patients, pre-treatment sTILs were prognostic for DFS (CD8+sTILs) and distant-DFS. CD8+sTILs also appeared to be predictive for the effectiveness of RNI on distant-DFS, suggesting that immune mechanisms may in part be responsible and merits further investigation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".