Inhibition of TFF3 synergizes with c-MET inhibitors to decrease the CSC-like phenotype and metastatic burden in ER+HER2+ mammary carcinoma
Bibliographic record
Abstract
The interaction between HER2 and ERα signaling pathways contributes to resistance to anti-estrogen and HER2-targeted therapies, presenting substantial treatment challenges in ER-positive (ER+) HER2-positive (HER2+) mammary carcinoma (MC). Trefoil Factor-3 (TFF3) has been reported to mediate resistance to both anti-estrogen and anti-HER2 targeted therapies in ER+ and ER+HER2+ MC, respectively. Herein, the function and mechanism of TFF3 in ER+HER2+ MC were delineated; and novel combinatorial therapeutic strategies were identified. Elevated expression of TFF3 promoted the oncogenicity of ER+HER2+ MC cells, including enhanced cell proliferation, survival, anchorage-independent growth, 3D growth, cancer stem cell-like (CSC-like) phenotype, migration, invasion, and xenograft growth. Targeting TFF3 with an interfering RNA plasmid or a small-molecule inhibitor (AMPC) inhibited these oncogenic characteristics, highlighting the therapeutic potential of targeting TFF3 in ER+HER2+ MC. Furthermore, a high-throughput combinatorial anti-cancer compound library screening revealed that AMPC preferentially synergized with receptor tyrosine kinase c-MET inhibitors (c-METis) to reduce cell survival and the CSC-like phenotype. The combination of AMPC and c-METis also synergistically suppressed the in vivo growth of ER+HER2+ MC cell-derived xenografts and abrogated lung metastasis. Mechanistically, TFF3 was observed to activate c-MET signaling through a positive-feedback loop to enhance the CSC-like phenotype of ER+HER2+ MC. Therefore, proof of concept is provided herein that antagonizing of TFF3 is a promising therapeutic strategy in combination with c-MET inhibition for the treatment of ER+HER2+ MC.
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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".