Identification and characterization of an antibody to the CD9 tetraspanin: Therapeutic implications for cancer treatment.
Bibliographic record
Abstract
A75 Introduction: CD9 is a 24 kDa member of the vast transmembrane 4 (TM4) family that is expressed on both hematopoietic and non-hematopoietic cells. Based on cDNA sequence analysis, the TM4SF members are predicted to be single polypeptide chains with four highly hydrophobic putative transmembrane (TM) regions and two extracellular (EC) loops with both the amino and carboxy termini localized intracellular. Tetraspanins have been implicated in a large variety of physiological processes such as immune cell activation, cell migration, cell-cell fusion and various aspects of cellular differentiation. Using the ARIUS FunctionFirst™ Platform, AR40A746.2.3 a monoclonal antibody targeting CD9, has been generated. In vitro and in vivo assays revealed that this antibody induces cytotoxicity in cancer cell lines and tumor growth inhibition in both prophylactic and established xenograft models of human cancers. Method: Cytotoxicity of AR40A746.2.3 was tested on a variety of cancer cell lines in vitro. To investigate the potential anti-tumor effect of this antibody in vivo, AR40A746.2.3 was administered in sub-cutaneous prophylactic and established xenograft models of human pancreatic and breast cancer. Immunohistochemical analyses (IHC) were performed to characterize the distribution of the AR40A746.2.3 epitope in human normal and cancer tissues and to select a relevant pre-clinical toxicology model. Binding of AR40A746.2.3 to CD9 were assessed by Western blot, flow cytometry and ELISA. Results: AR40A746.2.3 demonstrated cytotoxicity in several cancer cell lines including pancreatic BXPC-3 cells and breast MDA-MB-231 cells. Efficacy studies of AR40A756.2.3 treatment in xenograft tumor models demonstrated a significant tumor growth inhibition in the prophylactic BXPC-3 model (99.56%, p
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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 | 0.001 | 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".