39. CHARACTERIZING NOVEL INHIBITORS OF BRAIN METASTASIS-INITIATING CELLS
Bibliographic record
Abstract
Abstract BACKGROUND The incidence of brain metastases (BM) is tenfold higher than primary brain tumors. BM commonly originate from primary lung, breast, and melanoma tumors with a 90% mortality rate within one year of diagnosis. Current standard of care for BM includes surgical resection with concurrent chemoradiation, but does not extend median survival past 16 months, posing a large unmet need to identify novel therapies against BM. METHODS From a large in-house biobank of patient-derived BM cell lines, the Singh Lab has generated murine orthotopic patient-derived xenograft (PDX) models of lung, breast, and melanoma BM that recapitulate the stages of BM progression as seen in human patients. Using these three PDX models, we identified a population of “pre-metastatic” brain metastasis-initiating cells (BMICs) that are newly arrived in the brain but have yet to form detectable tumors. Pre-metastatic BMICs are not detectable in human patients but are important therapeutic targets with the potential to prevent BM in at-risk patients. RESULTS RNA sequencing of pre-metastatic BMICs from all three PDX primary tumor models with subsequent Connectivity Map analysis identified novel compounds that have the potential of killing all three types of BMICs. In particular, we identified two compounds that have selective killing of BMICs in vitro from all three primary tumor cohorts while sparing non-cancerous cells. We further characterized their ability to inhibit the self-renewal and proliferative properties of BMICs. Ongoing in vivo work will investigate the compounds’ preclinical utilities in preventing BM. CONCLUSION Identification of novel small molecules that target BMICs could prevent the formation of BM completely 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 | 0.000 | 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) | 0.004 | 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".