CMET-35. PRELIMINARY SCREENING OF A NOVEL EpCAM BISPECIFIC T-CELL ENGAGER (BiTE) ANTIBODY TO TARGET A BMIC POPULATION IN HUMAN BRAIN METASTASES
Bibliographic record
Abstract
Brain Metastases (BM) are the most common type of cerebral tumor in adult, occurring at a rate 10 times greater than that of primary brain cancers. The inherent abilities of a primary tumor cell capable of initiating a BM resembles that of a cancer stem cell (CSC). Utilizing primary patient samples of BMs, we have characterized a subpopulation of CSC-like cells, termed brain metastasis-initiating cells (BMICs), which are responsible for initiating BMs. Due to the fluctuating nature of BMICs as they undergo the metastatic cycle, there currently exists a scarce few biomarkers that can faithfully identify this BMIC population. One such marker is epithelial cell adhesion molecule (EpCAM), that is utilized to identify cells of epithelial origin and has recently shown to a possible therapeutic option for the immunological treatment of BMs. Using CellectSeq, a novel methodology that combines use of phage-displayed synthetic antibody libraries and high-throughput DNA sequencing technology, a novel EpCAM-specific monoclonal antibody was generated. EpCAM-specific BiTEs were constructed to consist of two arms; one arm recognizes the tumor antigen (EpCAM) while the second is specific to CD3 antigen. The BiTEs were constructed in various conformations and dual binding specificity was confirmed using flow cytometry. Preliminary screening was performed to test the ability of BiTEs to functionally elicit EpCAM-specific cytotoxic responses in vitro. Future work will go to validate the efficacy of EpCAM-specific BiTES in targeting EpCAM positive BMICs in vivo while maintaining minimal off target cytotoxicity. Ultimately, we aim to apply the novel field of immunotherapies to block the metastatic process, transforming a uniformly fatal systemic disease into a locally controlled and eminently more treatable one.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".