BSCI-19. Therapeutic intervention of lung-, breast-, and melanoma-brain metastasis
Bibliographic record
Abstract
Abstract Background The incidence of brain metastases (BM) is tenfold higher than that of primary brain tumours. BM predominantly originate from primary lung, breast, and melanoma tumours with a 90% mortality rate within one year of diagnosis, posing a large unmet clinical need to identify novel therapies against BM. Methods Using a large in-house biobank of patient-derived BM cell lines, the Singh Lab has generated murine orthotopic patient-derived xenograft models of BM and captured a “premetastatic” population of BM cells that have just seeded the brains of mice before forming clinically detectable tumours: a cell population that is impossible to detect in human patients but represents a therapeutic window wherein metastasizing cells can be targeted and eradicated before establishing clinically detectable tumours. Results RNA sequencing of pre-metastatic BM cells from all three primary tumour models with subsequent Connectivity Map analysis identified a lead compound that exhibits selective anti-BM activity in vitro. Preliminary in vivo work has shown that this lead compound reduces the tumor burden of treated mice compared to vehicle control while providing a significant survival advantage. Ongoing mechanistic investigations aim to delineate the protein target of this compound in the context of the observed selective anti-BM phenotype. Conclusion Therapeutic targeting of premetastatic BM cells could prevent the formation of BM and dramatically improve the prognosis of at-risk cancer patients.
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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 | 0.001 | 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) | 0.008 | 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".