Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in patients with HER2-positive early breast cancer (EBC)
Bibliographic record
Abstract
BACKGROUND: In human epidermal growth factor receptor 2 (HER2)-positive early breast cancer (EBC), we investigated tumor and immune changes during neoadjuvant treatment and their impact on residual disease (RD) biology and prognostic implications across four neoadjuvant studies of trastuzumab with or without lapatinib, and with or without chemotherapy: CALGB 40601, PAMELA, NeoALTTO, and NSABP B-41. PATIENTS AND METHODS: We compared tumor and immune gene expression changes during neoadjuvant treatment and their association with event-free survival (EFS) by uni- and multivariable Cox regression models in different cohorts and timepoints: 452 RD samples at baseline including 169 with a paired RD, and biomarker changes during neoadjuvant therapy, evaluating model performance via the c-index. RESULTS: Analysis of 169 paired tumor samples revealed a shift in intrinsic subtype proportions from HER2-enriched at baseline (50.3%) to normal-like (49.1%) followed by luminal A (18.9%) in RD. This luminal phenotypic change was supported by decreased correlation to the HER2-enriched centroid, ERBB2, and HER2 amplicon genes and increased correlation to the luminal A centroid (Wilcoxon test P < 0.001). Additionally, RD showed relative immune activation marked by significant increases in B-cell, CD8 T-cell, and natural killer cell signatures (Wilcoxon test P < 0.05). In multivariable Cox models, intrinsic subtypes at baseline provided more prognostic information, while immune gene expression signatures provided more prognostic information in RD. Notably, the best multivariable EFS model (c-index = 0.77) integrated the immunoglobulin G signature from RD samples (adjusted hazard ratio 0.45, 95% confidence interval 0.30-0.67, adjusted P = 0.002). CONCLUSIONS: In patients with HER2-positive EBC and RD, tumor biomarkers provide more prognostic information at baseline. In contrast, immune biomarkers perform better for EFS prognosis in RD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".