Immunotherapy Implication of Signature-Guided Biomarker Discovery for Trastuzumab-Resistant HER2-Positive Breast Cancer
Bibliographic record
Abstract
Abstract Despite the great improvement of patient outcomes by trastuzumab, a monoclonal antibody targeted on HER2-positive breast cancer, approximately 23% of patients with early-stage disease treated with adjuvant trastuzumab either fail to respond or experience recurrence within 10 years, highlighting the importance of identifying which HER2-positive patients would benefit from trastuzumab upfront. Efforts to identify biomarkers predictive of response to trastuzumab in initial breast tumor core biopsies have been complicated by the clinical and biological heterogeneity of HER2-positive tumors. Therefore, we identified a trastuzumab-resistant (TrR) signature that accurately predicts response to trastuzumab quantitively and qualitatively in vitro and in vivo, via repurposing transcriptome profiles in an engineered cell line model. We additionally demonstrated that our TrR signature was associated with tumor progression and capable of stratifying patient prognosis. Our study further illustrated the possible mechanism of this resistance as being less inherited cytotoxic T cell infiltration and failure to secrete Interferon-γ upon trastuzumab treatment in TrR tumors. These findings highlight the potential clinical application of TrR signature in treatment management and identifying possible immunotherapy interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".