Immunotherapy and the Tumor Microenvironment in Brain Metastases from Non-Small Cell Lung Cancer: Challenges and Future Directions
Bibliographic record
Abstract
Brain metastases (BMs) are a relatively common and severe complication in advanced non-small cell lung cancer (NSCLC), significantly affecting patient prognosis. Metastatic tumor cells can alter the brain tumor microenvironment (TME) to promote an immunosuppressive state, characterized by reduced infiltration of tumor-infiltrating lymphocytes (TILs), diminished expression of programmed death-ligand 1 (PD-L1), and changes in other proinflammatory factors and immune cell populations. Microglia, the resident macrophages of the brain, play a pivotal role in modulating the central nervous system (CNS) microenvironment through interactions with metastatic cancer cells, astrocytes, and infiltrating T cells. The M2 phenotype of microglia contributes to immunosuppression in BM via the activation of signaling pathways such as STAT3 and PI3K-AKT-mTOR. Recent advances have enhanced our understanding of the immune landscape of BMs in NSCLC, particularly regarding immune evasion within the CNS. Current immunotherapeutic strategies, including immune checkpoint inhibitors, have shown promise for NSCLC patients with BM, demonstrating intracranial activity and manageable safety profiles. Future research is warranted to further explore the molecular and immune mechanisms underlying BM, aiming to develop more effective treatments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".