CMET-47. PRECLINICAL VALIDATION OF NOVEL THERAPEUTICS TARGETING A 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, therapeutic strategies remain limited. Advancements in in vivo modelling of metastasis presents a useful platform to aid in the advance and screening of novel targeting therapeutics, though currently few models exist that properly recapitulate the clinical progression of brain metastasis. Utilizing primary patient samples of BMs, we have characterized a subpopulation of CSC-like cells, termed brain metastasis-initiating cells (BMICs), which are responsible for initiating BMs. Through injections of BMICs isolated from lung BMs into NOD-SCID mice, we have generated a novel patient-derived xeno-transplantation (PDXT) model of BM that allows for interrogation of each phase of the metastatic process from lung to brain. We then expanded our model to incorporate BMICs derived from breast and melanoma BMs. BMICs were harvested from primary sites and corresponding BMs, and RNA submitted for sequencing to identify metastatic and tissue-specific gene signatures. BMICs were found to possess a unique genomic profile as compared to BMICs isolated from full primary tumors and complete macro-metastases, exhibiting dysregulated expression in over 13,000 genes, including those involved in stem cell, epithelial-to-mesenchymal transition, and quiescence properties. In silico analysis was used to generate a list of therapeutics targeting this unique BMIC population. In vitro and in vivo screening has identified a subset of compounds with no previously known efficacy in cancer treatment that inhibits BMIC growth and metastasis. Ultimately, we aim to transform a uniformly fatal systemic disease into a locally controlled and eminently more treatable one.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".