Biomarker data from KATHERINE: A phase III study of adjuvant trastuzumab emtansine (T-DM1) versus trastuzumab (H) in patients with residual invasive disease after neoadjuvant therapy for HER2-positive breast cancer.
Bibliographic record
Abstract
502 Background: The phase 3 KATHERINE study (NCT01772472) compared adjuvant T-DM1 versus H in patients with residual invasive breast cancer after neoadjuvant chemotherapy plus HER2-targeted therapy. Here we report exploratory analyses of the relationship between invasive disease-free survival (IDFS) and biomarkers potentially related to response. Methods: Formalin fixed paraffin-embedded tissue samples were collected before neoadjuvant treatment and/or at surgery. Surgical samples were used for analyses, except when only pre-treatment samples were available (~20% of cases). DNA was derived to identify PIK3CA hotspot mutations and gene expression (RNA) analysis was used to detect HER2, PD-L1, CD8 and predefined immune signatures including 3-gene, 5-gene, Teffector, chemokine signaling, and checkpoint inhibitor signatures. RNA analysis was adjusted for tumor content and expression levels were dichotomized at the median into low (≤) and high (>) groups. The effect of treatment and biomarkers on IDFS was assessed. Results: PIK3CA mutation (mut) status was available from 1363 (91.7%) patients. T-DM1 IDFS benefit was independent of PIK3CA mut status (mut: HR 0.54; 95%CI 0.23–0.90; non-mut: HR 0.48; 95%CI 0.35–0.65) and no impact of PIK3CA mut was observed within either treatment arm. Gene expression data were available from 1059 (71.3%) patients. Similar gene expression levels were observed between treatment arms, but, unlike the surgical samples (n = 815), the pre-treatment samples (n = 244) were not representative of the ITT population. Thus, subsequent analyses were based on surgical samples (H n = 398; T-DM1 n = 417). Consistent treatment benefit with T-DM1 vs H was observed across the single-gene and immune gene-signature subgroups as in the ITT population. High vs low HER2 expression was associated with worse outcome (HR 2.02; 95% CI 1.32–3.11) within the H arm, but not within the T-DM1 arm (HR 1.01; 95% CI 0.56–1.83). High vs low PD-L1 expression was associated with better outcome within the H arm (HR 0.66; 95% CI 0.44–1.00) but not within the T-DM1 arm (HR 1.05; 95% CI 0.59–1.87). Similar trends were observed in the checkpoint inhibitor subgroups. Conclusions: These exploratory analyses provide the first data on the relationship between biomarker expression in residual disease after HER2-targeted therapy and outcomes. PIK3CA mut status did not influence outcomes with H or T-DM1. T-DM1 benefit appeared to be independent of all biomarkers assessed. Clinical trial information: NCT01772472 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".