BIOM-03. INVASIVE HISTOPATHOLOGY DRIVES POOR OUTCOMES IN SURGICALLY RESECTED BRAIN METASTASES
Bibliographic record
Abstract
Abstract BACKGROUND Surgery as a single modality for the treatment of brain metastases (BrM) results in local recurrence (LR) in 60% of patients. These failure rates are reduced by half with post-operative radiotherapy. The non-invasive nature of BrM has led to the assumption that local recurrence is caused by spillage of cancer cells into the surgical cavity at the time of surgery. We present evidence suggesting that invasion of metastatic cancer cells into the adjacent brain is present in the majority of BrM and is associated with LR, leptomeningeal metastasis (LM), and overall survival (OS). METHODS We assessed the histopathological growth pattern (HGP) of 164 surgically resected BrM. HGP was correlated with LR, LM and OS. Single-cell transcriptomics (scRNAseq) was performed on 15,615 cells from metastasis center (MC) and surrounding brain (SB) adjacent to the tumor. N=30 orthotopic patient-derived xenograft models (OPDX) were established from BrM. RESULTS 56/164 (34%) BrM specimens showed a minimally invasive (MI) HGP between the tumor and adjacent brain while 108/164 (66%) showed significant invasion of tumor lobules or single-cells into the brain (HI-HGP). HI-HGP was associated with LR, LM and shortened OS in BrM patients. scRNAseq identified abundant cancer cells in SB that overexpressed pathways and genes involved in cell survival and stress adaptation compared to matched cancer cells in MC. Validation of these targets with immunohistochemistry in patient and OPDX tissues revealed cold-inducible RNA binding protein (CIRBP) overexpression in HI-HGP patient and OPDX BrM. Modulation of CIRBP expression in OPDX and cell line models of HI-HGP BrM delayed BrM progression and extended OS. CONCLUSION HI-HGP is a poor prognostic indicator in patients with surgically resected BrM, establishing HGP as an important prognostic factor that should be considered by clinicians treating BrM patients. We identify CIRBP as a functional mediator of this process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".