Neratinib Efficacy and Circulating Tumor DNA Detection of <i>HER2</i> Mutations in <i>HER2</i> Nonamplified Metastatic Breast Cancer
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Purpose: Based on promising preclinical data, we conducted a single-arm phase II trial to assess the clinical benefit rate (CBR) of neratinib, defined as complete/partial response (CR/PR) or stable disease (SD) ≥24 weeks, in HER2mut nonamplified metastatic breast cancer (MBC). Secondary endpoints included progression-free survival (PFS), toxicity, and circulating tumor DNA (ctDNA) HER2mut detection. Experimental Design: Tumor tissue positive for HER2mut was required for eligibility. Neratinib was administered 240 mg daily with prophylactic loperamide. ctDNA sequencing was performed retrospectively for 54 patients (14 positive and 40 negative for tumor HER2mut). Results: Nine of 381 tumors (2.4%) sequenced centrally harbored HER2mut (lobular 7.8% vs. ductal 1.6%; P = 0.026). Thirteen additional HER2mut cases were identified locally. Twenty-one of these 22 HER2mut cases were estrogen receptor positive. Sixteen patients [median age 58 (31–74) years and three (2–10) prior metastatic regimens] received neratinib. The CBR was 31% [90% confidence interval (CI), 13%–55%], including one CR, one PR, and three SD ≥24 weeks. Median PFS was 16 (90% CI, 8–31) weeks. Diarrhea (grade 2, 44%; grade 3, 25%) was the most common adverse event. Baseline ctDNA sequencing identified the same HER2mut in 11 of 14 tumor-positive cases (sensitivity, 79%; 90% CI, 53%–94%) and correctly assigned 32 of 32 informative negative cases (specificity, 100%; 90% CI, 91%–100%). In addition, ctDNA HER2mut variant allele frequency decreased in nine of 11 paired samples at week 4, followed by an increase upon progression. Conclusions: Neratinib is active in HER2mut, nonamplified MBC. ctDNA sequencing offers a noninvasive strategy to identify patients with HER2mut cancers for clinical trial participation. Clin Cancer Res; 23(19); 5687–95. ©2017 AACR.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it