Biomarker Data from the Phase III KATHERINE Study of Adjuvant T-DM1 versus Trastuzumab for Residual Invasive Disease after Neoadjuvant Therapy for HER2-Positive Breast Cancer
Bibliographic record
Abstract
PURPOSE: In KATHERINE, adjuvant T-DM1 reduced risk of disease recurrence or death by 50% compared with trastuzumab in patients with residual invasive breast cancer after neoadjuvant therapy (NAT) comprised of HER2-targeted therapy and chemotherapy. This analysis aimed to identify biomarkers of response and differences in biomarker expression before and after NAT. EXPERIMENTAL DESIGN: Exploratory analyses investigated the relationship between invasive disease-free survival (IDFS) and HER2 protein expression/gene amplification, PIK3CA hotspot mutations, and gene expression of HER2, PD-L1, CD8, predefined immune signatures, and Prediction Analysis of Microarray 50 intrinsic molecular subtypes, classified by Absolute Intrinsic Molecular Subtyping. HER2 expression on paired pre- and post-NAT samples was examined. RESULTS: T-DM1 appeared to improve IDFS versus trastuzumab across most biomarker subgroups, except the HER2 focal expression subgroup. High versus low HER2 gene expression in residual disease was associated with worse outcomes with trastuzumab [HR, 2.02; 95% confidence interval (CI), 1.32-3.11], but IDFS with T-DM1 was independent of HER2 expression level (HR, 1.01; 95% CI, 0.56-1.83). Low PD-L1 gene expression in residual disease was associated with worse outcomes with trastuzumab (HR, 0.66; 95% CI, 0.44-1.00), but not T-DM1 (HR, 1.05; 95% CI, 0.59-1.87). PIK3CA mutations were not prognostic. Increased variability in HER2 expression was observed in post-NAT versus paired pre-NAT samples. CONCLUSIONS: T-DM1 appears to overcome HER2 resistance. T-DM1 benefit does not appear dependent on immune activation, but these results do not rule out an influence of the tumor immune microenvironment on the degree of response.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".