BSCI-10. Invasive growth of brain metastases is driven by cancer cell-pSTAT3+ reactive astrocyte crosstalk
Bibliographic record
Abstract
Abstract Background Brain metastases (BrM) with a highly invasive (HI) histological growth pattern are associated with poor prognosis compared to minimally invasive (MI) masses. Compared to MI lesions, HI BrM form greater contacts with cells in the peritumoral brain, particularly reactive astrocytes (RAs). RAs expressing phosphorylated STAT3 (pSTAT3+RAs) have been shown to promote BrM colonization. Here, we investigate the role of pSTAT3+RAs in promoting invasive growth of HI BrM. Methods We performed immunohistochemistry to identify pSTAT3+RAs in HI and MI human and patient-derived xenograft BrM. We assessed how pharmacological STAT3 inhibition or RA-specific STAT3 genetic ablation affected HI and MI BrM growth in vivo. scRNA-seq data generated from HI BrM astrocytes were integrated with published RA secretome data to identify STAT3 targets expressed by RAs that may drive invasion. Cancer cell invasion was modeled in vitro using a brain slice-tumor co-culture assay. Results HI BrM display increased pSTAT3-positivity within RAs when compared to MI lesions. Pharmacological STAT3 inhibition with Legasil (Silibinin) or genetic ablation decreased in vivo growth of HI, but not MI, BrM. Brain slice cultures treated with STAT3-activating cytokines induced cancer cell invasion, a response that was ablated following STAT3 inhibition. Chi3L1 was identified as a STAT3 target expressed by RAs. Cancer cells treated with recombinant Chi3L1 showed greater invasion into brain slice cultures compared to untreated cells. Conclusions pSTAT3+RAs are over-represented in HI BrM, rendering HI BrM preferentially sensitive to STAT3 inhibition. pSTAT3+RAs functionally contribute to BrM invasion within the brain, in part through Chi3L1-mediated activity. This work identifies STAT3 and Chi3L1 as clinically relevant therapeutic targets in management of HI BrM.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".