A Tumor-Selective Monoclonal Antibody from Immunization with a Tumor-Associated Mucin Glycopeptide
Bibliographic record
Abstract
Abstract We have previously studied the generation of immune responses after vaccination with tumor-associated carbohydrate antigen (TACA)-containing glycopeptides from the tandem repeat (TR) sequence of MUC4, an aberrantly expressed mucin in pancreatic adenocarcinomas. A specific lead antigen from that study containing the Thomsen-Friedenreich TACA disaccharide facilitated the pursuit of a monoclonal antibody to this synthetic hapten. Initial evaluation of polyclonal antiserum resulting from immunization with a KLH conjugate of this glycopeptide into rabbits showed high titer antibodies by ELISA assays, and selective immunoreactivity with MUC4+ cells by western blot and flow cytometry techniques. Glycan microarray analysis showed an intriguing binding pattern where the antiserum showed near complete specificity for MUC4 TR glycopeptides and peptides, relative to all components on the array. Tissue staining also showed distinct tumor specificity to pancreatic tumor tissue in relation to normal pancreatic tissue, with a preference for more aggressive tumor foci. Based on this data, we produced a monoclonal antibody whose binding and reactivity profile was similar to that of the polyclonal serum, with the added benefit of being more specific for the N-terminal glycosylated peptide domain. This epitope represents a novel immunogen to potentially develop diagnostic antibodies or immunotherapies against various MUC4-positive cancers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".