TMOD-23. DEVELOPMENT AND APPLICATION OF A NOVEL MODEL OF HUMAN LUNG-TO- BRAIN METASTASIS TO IDENTIFY GENETIC REGULATORS OF BRAIN METASTASIS INITIATING CELLS
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). 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 BMICs into immunocompromised mice. Comprehensive interrogation of human BM using RNA interference screens with subsequent validation in our model identified SPOCK1 and TWIST2 as novel regulators of brain metastasis-initiating cell (BMIC) self-renewal and migration to the brain, respectively. A prospective cohort of primary lung cancer specimens showed SPOCK1 and TWIST2 over-expressed only in patients who ultimately developed BM. Protein-protein interaction network mapping identified novel BMIC regulatory genes with significant prognostic value in lung cancer patients. Specifically, the most significant connector between SPOCK1 and TWIST2, INHBA, a TGF-β ligand found mutated in lung adenocarcinoma, showed reduced expression in BMICs with knockdown of SPOCK1, defining a novel pathway between INHBA, SPOCK1 and TWIST2 and implicating the involvement of the TGF-β signaling pathway in BM development. Our development of a novel preclinical model of BM, through which we have identified several novel BMIC regulators, present potential therapeutic targets that could aid in blockage of the metastatic process, and transform a uniformly fatal systemic disease into a locally controlled and eminently more treatable one.
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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 | 0.000 |
| 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".