Invasive growth associated with cold-inducible RNA-binding protein expression drives recurrence of surgically resected brain metastases
Bibliographic record
Abstract
BACKGROUND: Sixty percent of surgically resected brain metastases (BrM) recur within 1 year. These recurrences have long been thought to result from the dispersion of cancer cells during surgery. We tested the alternative hypothesis that invasion of cancer cells into the adjacent brain plays a significant role in local recurrence and shortened overall survival. METHODS: We determined the invasion pattern of 164 surgically resected BrM and correlated with local recurrence and overall survival. We performed single-cell RNA sequencing (scRNAseq) of >15,000 cells from BrM and adjacent brain tissue. Validation of targets was performed with a novel cohort of BrM patient-derived xenografts (PDX) and patient tissues. RESULTS: We demonstrate that invasion of metastatic cancer cells into the adjacent brain is associated with local recurrence and shortened overall survival. scRNAseq of paired tumor and adjacent brain samples confirmed the existence of invasive cancer cells in the tumor-adjacent brain. Analysis of these cells identified cold-inducible RNA-binding protein (CIRBP) overexpression in invasive cancer cells compared to cancer cells located within the metastases. Applying PDX models that recapitulate the invasion pattern observed in patients, we show that CIRBP is overexpressed in highly invasive BrM and is required for efficient invasive growth in the brain. CONCLUSIONS: These data demonstrate peritumoral invasion as a driver of treatment failure in BrM that is functionally mediated by CIRBP. These findings improve our understanding of the biology underlying postoperative treatment failure and lay the groundwork for rational clinical trial development based upon invasion pattern in surgically resected BrM.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".