BMET-31DEVELOPMENT AND APPLICATION OF A NOVEL MODEL OF HUMAN LUNG-TO-BRAIN METASTASIS TO IDENTIFY UNIQUE METASTATIC GENE SIGNATURES
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 (CSCs). Previous work conducted in our lab identified a population of CSCs within lung-derived brain metastases. We hypothesize that a subgroup of CSCs, termed brain metastasis-initiating cells (BMICs), are responsible for the initiation of BM and are identifiable by an exclusive subset of genes that regulate self-renewal and metastasis. Despite the prevalence and lethality of BM, there is no clinically relevant model that fully reflects metastasis in patients. We recently generated a novel human-mouse xenotransplantation model of BM that allows for interrogation of each phase of the metastatic process from lung to brain, through injection of human patient-derived GFP-expressing BMICs into immunocompromised mice via three routes: 1) intracranial (IC), 2) intrathoracic (IT) and 3) intravascular/intracardiac (IV). GFP+ cells were harvested from the lungs and/or brains from each injection route, and RNA was submitted for microarray analysis to identify a unique metastatic and tissue-specific gene signature. In order to identify genes involved in self-renewal and tumor initiation, we also performed RNA interference screens in vitro and in vivo on BMIC lines against 150 genes implicated in BM formation. Further in vitro validation of select hit genes identified SPOCK1 and TWIST2 as essential regulators of cell proliferation, sphere formation, and migration. Future work will validate the role of these genes in our established in vivo metastasis models. We also aim to examine their expression in primary lung FFPE samples to determine if they are predictive biomarkers of lung-to-brain metastasis in prospective cohorts of newly diagnosed lung cancer patients, and to determine their potential as therapeutic targets.
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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 | 0.001 | 0.002 |
| 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".