Targeting the Cx26/NANOG/Focal Adhesion Kinase Complex via Cell-Penetrating Peptides in Triple-Negative Breast Cancers
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) represents the most lethal and treatment-resistant breast cancer subtype with limited treatment options. We previously identified a protein complex unique to TNBC cancer stem cells composed of the gap junction protein connexin 26 (Cx26), the pluripotency transcription factor NANOG, and focal adhesion kinase (FAK). We sought to determine whether a peptide mimetic of Cx26 designed to target the complex attenuated tumor growth in pre-clinical models. Histological assessment was employed to verify expression of complex members. We designed peptides based on Cx26 juxtamembrane domains and performed binding experiments with NANOG and FAK using surface plasmon resonance. Peptides with high affinity were engineered with a cell-penetrating sequence and assessed in functional assays including cell proliferation, self-renewal, and in vivo tumor growth, and downstream signaling changes were measured. Binding studies revealed that the Cx26 C-terminal tail and intracellular loop bound to NANOG and FAK with submicromolar-to-micromolar affinity and that a 5-amino acid sequence in the C-terminal tail of Cx26 (RYCSG) was sufficient for binding. The Cx26 C-terminal tail was tagged with an antennapedia cell-penetrating peptide sequence and intracellular localization was confirmed. The cell-penetrating Cx26 peptide (aCx26-pep) disrupted self-renewal as assessed by tumorsphere formation assay while reducing nuclear FAK and NANOG and inhibiting NANOG target gene expression in TNBC cells but not luminal mammary epithelial cells. In vivo, aCx26-pep reduced tumor growth and proliferation and induced cell death. We provide proof-of-concept that a Cx26 peptide-based strategy inhibits growth and alters NANOG activity in TNBC.
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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) | 0.000 | 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 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".