Early Modulation of Circulating MicroRNAs Levels in HER2-Positive Breast Cancer Patients Treated with Trastuzumab-Based Neoadjuvant Therapy
Bibliographic record
Abstract
Circulating microRNA (ct-miRNAs) are able to identify patients with differential response to HER2-targeted therapy. However, their dynamics are largely unknown. We assessed 752 miRNAs from 52 NeoALTTO patients with plasma pairs prior and two weeks after trastuzumab. Increased levels of ct-miR-148a-3p and ct-miR-374a-5p were significantly associated with pathological complete response (pCR) (p = 0.008 and 0.048, respectively). At a threshold ≥ the upper limit of the 95%CI of the mean difference, pCR resulted 45% (95%CI 24%–68%), and 44% (95%CI 22%–69%) for ct-miR-148a-3p and ct-miR-374a-5p, respectively. Notably, ct-miR-148a-3p retained its predictive value (OR 3.42, 95%CI 1.23–9.46, p = 0.018) in bivariate analysis along with estrogen receptor status. Combined information from ct-miR-148a-3p and ct-miR140-5p, which we previously reported to identify trastuzumab-responsive patients, resulted in greater predictive capability over each other, with pCR of 54% (95%CI 25%–81%) and 0% (95%CI 0%–31%) in ct-miR-148a/ct-miR-140-5p high/present and low/absent, respectively. GO and KEGG analyses showed common enriched terms between the targets of these ct-miRNAs, including cell metabolism regulation, AMPK and MAPK signaling, and HCC progression. In conclusion, early modulated ct-miR-148-3p may inform on the functional processes underlying treatment response, integrate the information from already available predictive biomarkers, and identify patients likely to respond to single agent trastuzumab-based neoadjuvant therapy.
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 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 | 0.000 |
| 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 | 0.000 | 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".