Development, Characterization, and Immunotherapeutic Use of Peptide Mimics of the Thomsen-Friedenreich Carbohydrate Antigen
Bibliographic record
Abstract
The tumor-associated carbohydrate Thomsen-Friedenreich antigen (TF-Ag; Galbeta1-3GalNAcalpha-O-Ser/Thr) is overexpressed on the cell surface of several types of tumor cells, contributing to cancer cell adhesion and metastasis to sites containing TF-Ag-binding lectins. A highly specific immunoglobulin G(3) monoclonal antibody (Ab) developed to TF-Ag (JAA-F11) impedes TF-Ag binding to vascular endothelium, blocking a primary metastatic step and providing a survival advantage. In addition, in patients, even low levels of antibodies to TF-Ag seem to improve prognosis; thus, it is expected that vaccines generating antibodies toward TF-Ag would be clinically valuable. Unfortunately, vaccinations with protein conjugates of carbohydrate tumor-associated Ags have induced clinically inadequate immune responses. However, immunization using peptides that mimic carbohydrate Ags such as Lewis has resulted in both Ab and T-cell responses. Here, we tested the hypothesis that vaccinations with unique TF-Ag peptide mimics may generate immune responses to TF-Ag epitopes on tumor cells, useful for active immunotherapy against relevant cancers. Peptide mimics of TF-Ag were selected by phage display biopanning using JAA-F11 and rabbit anti-TF-Ag Ab and were analyzed in vitro to confirm TF-Ag peptide mimicry. In vitro, TF-Ag peptide mimics bound to TF-Ag-specific peanut agglutinin and blocked TF-Ag-mediated rolling and stable adhesion of cancer cells to vascular endothelium. In vivo, the immunization with TF-Ag-mimicking multiple antigenic peptides induced TF-Ag-reactive Ab production. We propose that this novel active immunotherapy approach could decrease tumor burden in cancer patients by specifically targeting TF-Ag-positive cancer cells and blocking metastasis.
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 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) | 0.000 | 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 teacher head, 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".