Breast Cancer Targeting Peptide Binds Keratin 1: A New Molecular Marker for Targeted Drug Delivery to Breast Cancer
Why this work is in the frame
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Bibliographic record
Abstract
The biomarkers or receptors expressed on cancer cells and the targeting ligands with high binding affinity for biomarkers play a key role in early detection and treatment of breast cancer. The breast cancer targeting peptide p160 (12-mer) and its enzymatically stable analogue 18-4 (10-mer) showed marked potential for breast cancer drug delivery using cell studies and animal models. Herein, we used affinity purification, liquid chromatography–tandem mass spectrometry, and proteomics to identify keratin 1 (KRT1) as the target receptor highly expressed on breast cancer cells for p160 peptide(s). Western blot and immunocytochemistry in MCF-7 breast cancer cells confirmed the identity of KRT1. We demonstrate that the p160 or 18-4 binding to MCF-7 breast cancer cells is dependent on the expression of KRT1, and we confirm peptide-KRT1 binding specificity using SPR experiments ( K d ∼ 1.1 μM and 0.98 μM for p160 and 18-4, respectively). Furthermore, we assessed the ability of peptide 18-4 to improve the cellular uptake and anticancer activity of a pro-apoptotic antimicrobial peptide, microcin J25 (MccJ25), in breast cancer cells. A covalent conjugate of peptide 18-4 with MccJ25 showed preferential cytotoxicity toward breast cancer cells with minimal cytotoxicity against normal HUVEC cells. The conjugate inhibited the growth of MDA-MB-435 MDR multidrug-resistant cells with an IC 50 comparable to that of nonresistant cells. Conjugation improved selective cellular uptake of MccJ25, and the conjugate triggered cancer cell death by apoptosis. Our findings establish KRT1 as a new marker for breast cancer targeting. Additionally, it pinpoints the potential use of antimicrobial lasso peptides as a novel class of anticancer therapeutics.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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 it