The MUC1 oncoprotein as a functional target: Immunotoxin binding to α/β junction mediates cell killing
Bibliographic record
Abstract
MUC1, a heavily glycosylated mucin, has generated considerable interest as a target for tumor killing because of its overexpression in malignancies. Full-length MUC1 (MUC1/TM) is proteolytically cleaved after synthesis generating alpha and beta subunits, which specifically bind in a noncovalent interaction. Although the beta chain remains on the cell surface, the alpha chain binds in an on-and-off interaction. Most anti-MUC1 antibodies (Abs) described to date recognize epitopes within the highly immunogenic alpha-chain tandem repeat. Because the alpha-chain is shed, such Abs are sequestered and fail to reach MUC1-expressing cells. Immunizing with cDNA encoding MUC1/TM and the spliced MUC1/X isoform from which the tandem repeat has been deleted yielded antibodies to the MUC1 alpha/beta junction. Pseudomonas toxin PE38 linked to polyclonal anti-MUC1 alpha/beta junction Abs both bound and killed MUC1-positive malignant cells. Monoclonal DMC209 binds the MUC1 alpha/beta junction in both MUC1/X and MUC1/TM. When injected into SCID mice xenotransplanted with human breast cancer MDA-MB-231, monoclonal DMC209 showed significant in vivo tumor-suppressive activity. The MUC1/X alpha/beta junction presents a biologically-significant target in MUC1-expressing malignancies because (i) antibodies directed against cell-bound alpha/beta junction epitopes reach the intended cellular target, (ii) antibodies to junction epitope are internalized into cells, (iii) anti alpha/beta junction antibodies can effectively kill high MUC1-expressing cancer cells as antibody-toxin conjugates and (iv) antibodies targeting the MUC1 cell-bound alpha/beta junction results in tumor suppression in vivo. Our results indicate that cell-bound MUC1 alpha/beta junction, unlike shed alpha chain, represents a highly effective moiety for targeting and killing MUC1-expressing malignancies.
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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".