Breast Cancer Targeting Peptide Binds Keratin 1: A New Molecular Marker for Targeted Drug Delivery to Breast Cancer
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".