Development of Human Recombinant Antibodies Against ROR1 Tumor Antigen
Bibliographic record
Abstract
Background: Receptor tyrosine kinase-like orphan receptor 1 (ROR1) is an oncofetal antigen expressed on many types of cancer cells, but not normal adult cells.ROR1 antigen contributes to cancer development and progression by several signaling pathways.ROR1 expression has been associated with tumor growth, survival, and metastasis.In this study specific human recombinant antibodies were selected against ROR1 antigen for their use in cancer immunotherapy.Methods: Phage display technology was used to produce phage antibody from a human scFv library.Phage concentration was determined to confirm the phage rescue process.Panning procedure was performed to isolate specific scFv clones against ROR1 epitope.Phage ELISA was done to evaluate the reactivity of the selected scFvs.Results: Two specific human scFvs with frequencies of 20% and 25% were selected against ROR1 peptide.The antibodies showed specific reaction to the corresponding epitopes in phage ELISA.Conclusions: Cancer targeted therapy using human specific antibodies is a new strategy, which is used in cancer therapy.The selected specific scFvs that target ROR1 epitope are human antibodies that originated from a human library and have the potential to be used in clinic in cancer immunotherapy of ROR1 positive tumors without induction of human anti mouse antibody (HAMA) response.
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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 | 0.000 | 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 | 0.001 |
| 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".