INTERCEPTING BRAIN METASTASES THROUGH METABOLIC VULNERABILITIES
Bibliographic record
Abstract
Abstract BTFC travel award recipient Patients with brain metastases (BM) face a 90% mortality rate within one year of diagnosis because the current standard of care is mainly palliative. Our patient-derived xenograft models have successfully recapitulated all the stages of the metastatic cascade and captured a “premetastatic” population of BM cells that have just seeded the brains of mice before forming mature, clinically detectable tumors. We applied RNA sequencing of premetastatic BM cells to reveal a unique deregulated transcriptomic profile that is specific to premetastatic cells despite the tumor of origin. Subsequent Connectivity Map analysis led us to identify a tool compound that exhibits anti-BM activity in vitro, while remaining ineffective against normal brain cell controls. Follow up preclinical studies showed that treatment with this tool compound reduces the tumor burden of mice compared to placebo, while providing a significant survival advantage. Mass spectrometry-based metabolomics and CRISPR knock-out studies directly validated our tool compound’s target as a targetable therapeutic vulnerability in BM, where pharmacological and genetic perturbation of the target attenuates BM cell proliferation both in vitro and in vivo. We have now begun a large-scale medicinal chemistry campaign to develop novel, brain penetrant inhibitors of this target with drug-like pre-clinical profiles validated by our in vivo experimental models for later stage preclinical development and subsequent clinical development. This potential first-in-class anti-metastatic therapy may provide an alternative treatment strategy for at-risk patients that are otherwise limited to palliation and could open the gate to future development of other anti-metastatic therapies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".