Development and Characterization of a Bispecific Single-Chain Antibody Directed Against T Cells and Ovarian Carcinoma
Bibliographic record
Abstract
Bispecific antibodies with specificity for tumor antigen and CD3 have been shown to redirect the cytotoxicity of T cells against relevant tumor. Our objective was to generate single-chain bispecific antibodies (bsSCA) that could retarget mouse cytotoxic T lymphocytes (CTL) to destroy human ovarian carcinoma in a xenogeneic setting. A bsSCA, 2C11 x B43.13, was constructed by genetic engineering and expressed in mammalian cells. Molecular characteristics, binding properties, and ability to retarget CTL were studied. Western blot analysis showed that the product is a 65-kDa protein. Purification of antibodies could be done by single-step affinity chromatography using protein L-agarose with an unoptimized yield of 200 microg/L. BsSCA 2C11 x B43.13 was capable of binding to mouse CD3 and human CA125 as detected by FACS analysis of EL4 and OVCAR Nu3H2 cells, respectively. It could also bridge activated splenic T cells and human ovarian carcinoma as demonstrated by a bridge FACS assay. Redirected mouse CTL could mediate human target cell lysis in a 20-h 51Cr release assay despite that they are xenogeneic. Prolonged incubation of redirected CTL and tumor targets resulted in a dramatic reduction in tumor cell number. CD28 co-stimulation enhanced redirected CTL function in both types of assays. BsSCA 2C11 x B43.13 thus can be used as a preclinical immunotherapeutic model for human ovarian cancer in a xenogeneic setting.
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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".