Pilot Study on the Utility of Circulating HER2/Neu Levels in the Serum of Breast Cancer Patients
Bibliographic record
Abstract
BACKGROUND/AIM: Accurate and timely assessment of the human epidermal growth factor receptor 2 (HER2/neu) overexpression is pivotal for the identification of breast cancer (BC) patients that could benefit from HER2-targeted therapy. Currently approved tissue-based HER2 assays (tHER2) are limited to testing HER2 status on tumor samples obtained at a few points in time during the course of the disease. Herein, we assessed serum HER2 (sHER2) status longitudinally in 81 serial samples prospectively collected from 43 consenting patients pre- and post-therapy to revisit the idea of serum testing in the follow-up of BC patients. PATIENTS AND METHODS: The cohort included 11 patients with early BC (EBC), 17 with locally advanced BC (LABC), and 15 with metastatic BC (MBC). sHER2 concentrations were measured using a quantitative ELISA-based technique, using 15 ng/ml as the cut-off for positivity. RESULTS: At baseline, sHER2 was negative in all EBC patients while positive in 1 LABC and 5 MBC patients. Sixteen BC patients (10 LABC, 1 EBC, and 5 MBC) were tHER2 positive. sHER2 and tHER2 results were discordant in 14 patients. Among the 16 tHER2 positive patients, 9 LABC, 1 EBC and 2 MBC patients were sHER2 negative. Conversely, 2 MBC patients were sHER2 positive, despite being tHER2 negative. A rise or drop of sHER2 by >20% correlated with disease progression or pathological response to therapy, respectively. CONCLUSION: The study demonstrated the technical validity and feasibility of the sHER2 assay. Findings suggest that post initial tissue diagnosis (tHER2), sHER2 assay may supplement subsequent tissue tests to monitor disease status and response to therapy. Further studies to assess the role of HER2 targeted therapies in sHER-positive/tHER2-negative cases upon disease progression are warranted.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".