Exploration of tumour-infiltrating lymphocytes as a predictive biomarker for adjuvant endocrine therapy in early breast cancer
Bibliographic record
Abstract
PURPOSE: Tumour-infiltrating lymphocytes (TILs) have been shown to be prognostic for disease-free survival and predictive for the benefit of chemotherapy in patients with early breast cancer, but have not been studied for endocrine therapy. EXPERIMENTAL DESIGN: The number of CD8-positive TILs was assessed in a subcohort of 236 patients in the Intergroup Exemestane Study. AQ After 2-3 years of adjuvant tamoxifen, AQpatients were randomized between the schemes of continuation for 5 years on tamoxifen and switching to exemestane. The numbers of CD8-positive TILs were analysed for correlations with disease-free survival (DFS) and overall survival (OS). A similar analysis was performed on 2596 patients in the TEAM trial who were randomized between the sequential scheme and the exemestane monotherapy. RESULTS: In the first cohort, patients with low (below median) numbers of CD8-positive TILs had a univariate hazard ratio (HR) for DFS of 0.27 (95% CI 0.13-0.55) in favour of treatment with exemestane, whereas this benefit was not observed in patients with high numbers of CD8-positive TILs (HR 1.34, 95% CI 0.71-2.50, HR for interaction 5.02, p = 0.001). In the second cohort, patients with low numbers of CD8-positive TILs showed a benefit of exemestane treatment on recurrence-free survival (RFS HR 0.67, 95% CI 0.45-0.99), and not with above-median numbers of CD8-positive TILs (HR 0.86, 95% CI 0.59-1.26, HR for interaction 1.29, p = 0.36). CONCLUSIONS: This study is the first to propose the number of CD8-positive TILs as potential predictive markers for endocrine therapy, with the low presence of CD8-positive TILs associated to benefit for exemestane-inclusive therapy. However, treatment-by-marker interactions were only significant in one cohort, indicating the need for further validation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".