T Cell Receptor-Like Recognition of Tumor In Vivo by Synthetic Antibody Fragment
Bibliographic record
Abstract
A major difficulty in treating cancer is the inability to differentiate between normal and tumor cells. The immune system differentiates tumor from normal cells by T cell receptor (TCR) binding of tumor-associated peptides bound to Major Histocompatibility Complex (pMHC) molecules. The peptides, derived from the tumor-specific proteins, are presented by MHC proteins, which then serve as cancer markers. The TCR is a difficult protein to use as a recombinant protein because of production issues and has poor affinity for pMHC; therefore, it is not a good choice for use as a tumor identifier outside of the immune system. We constructed a synthetic antibody-fragment (Fab) library in the phage-display format and isolated antibody-fragments that bind pMHC with high affinity and specificity. One Fab, fE75, recognizes our model cancer marker, the Human Epidermal growth factor Receptor 2 (HER2/neu) peptide, E75, bound to the MHC called Human Leukocyte Antigen-A2 (HLA-A2), with nanomolar affinity. The fE75 bound selectively to E75/HLA-A2 positive cancer cell lines in vitro. The fE75 Fab conjugated with (64)Cu selectively accumulated in E75/HLA-A2 positive tumors and not in E75/HLA-A2 negative tumors in an HLA-A2 transgenic mouse as probed using positron emission tomography/computed tomography (PET/CT) imaging. Considering that hundreds to thousands of different peptides bound to HLA-A2 are present on the surface of each cell, the fact that fE75 arrives at the tumor at all shows extraordinary specificity. These antibody fragments have great potential for diagnosis and targeted drug delivery in cancer.
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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) | 0.003 | 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".