BSCI-22 DETERMINING THE EFFECT OF NOVEL SMALL MOLECULE DRUGS AGAINST THE MIGRATION OF BRAIN METASTASIS INITIATING CELLS (BMICS)
Bibliographic record
Abstract
Abstract BACKGROUND Brain metastases are secondary tumors that predominantly arise from the spread of lung, skin, and breast cancers. The current standard of care for brain metastases is complete surgical resection, with a median survival of four months. Therefore, there is a dire need to discover new therapies that effectively target brain metastases. To do this, we have identified anti-brain metastasis drugs that specifically target brain metastasis initiating cells (BMICs), a cancer stem cell population that is thought to escape standard therapies and has the ability to leave their primary tumor, seed the brain, and form a secondary brain tumor. Since the migration of the BMICs is essential to the development of brain metastases in patients, the main goal of this study was to determine the effect our anti-brain metastasis drugs have against the migration of lung, skin, and breast BMICs. METHODS This migration assay utilizes a bi-well silicone structure which effectively establishes a ‘wound’ healing-like migration assay. BMICs are plated in optimized equal concentrations in each silicone bi-well structure to successfully form two cellular mono-layers that are separated by a middle silicone wall. Once cells adhere to the plate the silicone structure is removed and the area between the two cell populations is imaged over time with an in vitro imaging system. RESULTS This optimized assay has been used to screen our anti-brain metastasis drugs against the migration of lung, breast, and skin BMICs. Thus far our drugs have been tested against lung and skin BMICs which resulted in a significant decrease in BMIC migration. SIGNIFICANCE Since brain metastasis arises from the migration of cancer cells to a secondary organ, it is crucial to discover the effect of anti-brain metastasis drugs on BMIC migration prior to the initiation of preclinical animal trials.
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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 | 0.001 | 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) | 0.000 | 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".