Immune Infiltrate in the Primary Tumor Predicts Effect of Adjuvant Radiotherapy in Breast Cancer; Results from the Randomized SweBCG91RT Trial
Bibliographic record
Abstract
Abstract Purpose: Tumor-infiltrating immune cells play a key role in tumor progression. The purpose of this study was to analyze whether the immune infiltrate predicts benefit from postoperative radiotherapy in a large randomized breast cancer radiotherapy trial. Experimental Design: In the SweBCG91RT trial, patients with stage I and II breast cancer were randomized to breast-conserving surgery (BCS) and postoperative radiotherapy or to BCS only and followed for a median time of 15.2 years. The primary tumor immune infiltrate was quantified through two independent methods: IHC and gene expression profiling. For IHC analyses, the absolute stromal area occupied by CD8+ T cells and FOXP3+ T cells, respectively, was used to define the immune infiltrate. For gene expression analyses, immune cells found to be prognostic in independent datasets were pooled into two groups consisting of antitumoral and protumoral immune cells, respectively. Results: An antitumoral immune response in the primary tumor was associated with a reduced risk of breast cancer recurrence and predicted less benefit from adjuvant radiotherapy. The interaction between radiotherapy and immune phenotype was significant for any recurrence in both the IHC and gene expression analyses (P = 0.039 and P = 0.035) and was also significant for ipsilateral breast tumor recurrence in the gene expression analyses (P = 0.025). Conclusions: Patients with an antitumoral immune infiltrate in the primary tumor have a reduced risk of any recurrence and may derive less benefit from adjuvant radiotherapy. These results may impact decisions regarding postoperative radiotherapy in early breast cancer.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".