TMOD-37. CHARACTERIZATION OF A UNIQUE BMIC POPULATION IN HUMAN BRAIN METASTASES
Bibliographic record
Abstract
Brain Metastases (BM) are the most common neoplasm to affect the adult central nervous system, occurring at a rate 10 times greater than that of primary brain cancers. Despite the prevalence and severe lethality of BM, the current available in vivo models of metastasis neither fully recapitulate the clinical progression of BM nor allow for proper examination of metastatic cells. Not every cell within a primary tumour possess the ability to survive metastasis, and studies typical investigate large macro-metastases formed by cells that have already exited the metastatic cycle. We hypothesize that within primary tumours exists a subpopulation of CSC-like cells, termed brain metastasis-initiating cells (BMICs), which are responsible for initiating BMs. We have recently generated a novel patient-derived xenotransplantation (PDX) 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. We then expanded our model to incorporate BMs from breast and melanoma primaries. BMICs were harvested from primary sites and corresponding BMs, and RNA submitted for sequencing to identify a metastatic and tissue-specific gene signatures. From our novel PDX model we were able to interrogate micro-metastasis formation, where we found BMICs to possess a unique genomic profile as compared to BMICs isolated from full primary tumors and complete macro-metastases. These BMICs exhibit increased expression of stem cell, epithelial-to-mesenchymal transition, and quiescence genes. Through in silico analysis we generated a preliminary list of therapeutics targeting these unique BMICs. Future work will identify novel therapeutics that block the metastatic process by targeting these unique BMICs, and assess the clinical utility of this novel BMIC signature in predicting BMs of different origins. Ultimately we aim to 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 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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".