O76: SERUM JAM-A AS A PREDICTOR OF TREATMENT RESISTANCE IN BREAST CANCER PATIENTS
Bibliographic record
Abstract
Abstract Introduction Junctional Adhesion Molecule-A (JAM-A) has important physiological functions in epithelial and endothelial barriers, but its overexpression has also been linked with tumour progression and poor prognosis in various malignancies. Since JAM-A can be enzymatically cleaved (cJAM-A) and has been detected in the bloodstream, we hypothesized that cJAM-A shed from tumours overexpressing JAM-A may represent a possible predictor of treatment resistance in breast cancer. Method An assay was optimised to detect cJAM-A in serum/plasma. Samples were obtained from HER2-positive breast cancer patients (n=20) in Beaumont Hospital. Independently, serial samples were obtained from a Canadian cohort of locally advanced breast cancer (LABC) patients (n=53). Result Serum cJAM-A levels in therapy-resistant patients was significantly higher than those in treatment-sensitive patients (p<0.05) in an Irish cohort of HER2 positive patients. In a diverse international cohort of LABC patients, the development of metastatic disease was associated with higher levels of cJAM-A (p<0.05) as well as shorter time to progression (p<0.05). Conclusion Our data suggest that cJAM-A merits further investigation as a novel biomarker enabling prospective identification of patients at greatest risk of developing therapeutic resistance. Take-home message Our data suggest that cJAM-A merits further investigation as a novel biomarker enabling prospective identification of patients at greatest risk of developing therapeutic resistance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".