Neither Tumor-Infiltrating Lymphocytes nor Cytotoxic T Cells Predict Enhanced Benefit from Chemotherapy in the DBCG77B Phase III Clinical Trial
Bibliographic record
Abstract
Recent studies have shown that immune infiltrates in the tumor microenvironment play a role in response to therapy, with some suggesting that patients with immunogenic tumors may receive increased benefit from chemotherapies. We evaluated this hypothesis in early breast cancer by testing the interaction between immune biomarkers and chemotherapy using materials from DBCG77B, a phase III clinical trial where high-risk premenopausal women were randomized to receive chemotherapy or no chemotherapy. Tissue microarrays were evaluated for tumor-infiltrating lymphocytes (TILs) assessed morphologically on hematoxylin and eosin-stained slides, and by immunohistochemistry for CD8, FOXP3, LAG-3, PD-1 and PD-L1. Following REMARK reporting guidelines, data analyses were performed according to a prespecified statistical plan, using 10-year invasive disease-free survival as the endpoint. Differences in survival probabilities between biomarker groups were evaluated by Kaplan–Meier and Cox proportional hazard ratio analyses and prediction for treatment benefit by an interaction test. Our results showed that stromal TILs were associated with an improved prognosis (HR = 0.93; p-value = 0.03), consistent with previous studies. However, none of the immune biomarkers predicted benefit from chemotherapy in the full study set nor within major breast cancer subtypes. Our study indicates that primary tumors with higher immune infiltration do not derive extra benefit from cyclophosphamide-based cytotoxic chemotherapy.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".