Cleavage of the extracellular domain of junctional adhesion molecule-A is associated with resistance to anti-HER2 therapies in breast cancer settings
Bibliographic record
Abstract
BACKGROUND: Junctional adhesion molecule-A (JAM-A) is an adhesion molecule whose overexpression on breast tumor tissue has been associated with aggressive cancer phenotypes, including human epidermal growth factor receptor-2 (HER2)-positive disease. Since JAM-A has been described to regulate HER2 expression in breast cancer cells, we hypothesized that JAM-dependent stabilization of HER2 could participate in resistance to HER2-targeted therapies. METHODS: Using breast cancer cell line models resistant to anti-HER2 drugs, we investigated JAM-A expression and the effect of JAM-A silencing on biochemical/functional parameters. We also tested whether altered JAM-A expression/processing underpinned differences between drug-sensitive and -resistant cells and acted as a biomarker of patients who developed resistance to HER2-targeted therapies. RESULTS: Silencing JAM-A enhanced the anti-proliferative effects of anti-HER2 treatments in trastuzumab- and lapatinib-resistant breast cancer cells and further reduced HER2 protein expression and Akt phosphorylation in drug-treated cells. Increased epidermal growth factor receptor expression observed in drug-resistant models was normalized upon JAM-A silencing. JAM-A was highly expressed in all of a small cohort of HER2-positive patients whose disease recurred following anti-HER2 therapy. High JAM-A expression also correlated with metastatic disease at the time of diagnosis in another patient cohort resistant to trastuzumab therapy. Importantly, cleavage of JAM-A was increased in drug-resistant cell lines in conjunction with increased expression of ADAM-10 and -17 metalloproteases. Pharmacological inhibition or genetic silencing studies suggested a particular role for ADAM-10 in reducing JAM-A cleavage and partially re-sensitizing drug-resistant cells to the anti-proliferative effects of HER2-targeted drugs. Functionally, recombinant cleaved JAM-A enhanced breast cancer cell invasion in vitro and both invasion and proliferation in a semi-in vivo model. Finally, cleaved JAM-A was detectable in the serum of a small cohort of HER2-positive patients and correlated significantly with resistance to HER2-targeted therapy. CONCLUSIONS: Collectively, our data suggest a novel model whereby increased expression and cleavage of JAM-A drive tumorigenic behavior and act as a biomarker and potential therapeutic target for resistance to HER2-targeted therapies.
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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 | 0.000 |
| 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) | 0.001 | 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".