A Cancer Stem Cell Model for Studying Brain Metastases From Primary Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Brain metastases are most common in adults with lung cancer, predicting uniformly poor patient outcome, with a median survival of only months. Despite their frequency and severity, very little is known about tumorigenesis in brain metastases. METHODS: We applied previously developed primary solid tumor-initiating cell models to the study of brain metastases from the lung to evaluate the presence of a cancer stem cell population. Patient-derived brain metastases (n = 20) and the NCI-H1915 cell line were cultured as stem-enriching tumorspheres. We used in vitro limiting-dilution and sphere-forming assays, as well as intracranial human-mouse xenograft models. To determine genes overexpressed in brain metastasis tumorspheres, we performed comparative transcriptome analysis. All statistical analyses were two-sided. RESULTS: Patient-derived brain metastasis tumorspheres had a mean sphere-forming capacity of 33 spheres/2000 cells (SD = 33.40) and median stem-cell frequency of 1/60 (range = 0-1/141), comparable to that of primary brain tumorspheres (P = .53 and P = .20, respectively). Brain metastases also expressed CD15 and CD133, markers suggestive of a stemlike population. Through intracranial xenotransplantation, brain metastasis tumorspheres were found to recapitulate the original patient tumor heterogeneity. We also identified several genes overexpressed in brain metastasis tumorspheres as statistically significant predictors of poor survival in primary lung cancer. CONCLUSIONS: For the first time, we demonstrate the presence of a stemlike population in brain metastases from the lung. We also show that NCI-H1915 tumorspheres could be useful in studying self-renewal and tumor initiation in brain metastases. Our candidate genes may be essential to metastatic stem cell populations, where pathway interference may be able to transform a uniformly fatal disease into a more localized and 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 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) | 0.000 | 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 | 0.000 |
| 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".