Microenvironment-Modulated Metastatic CD133+/CXCR4+/EpCAM− Lung Cancer–Initiating Cells Sustain Tumor Dissemination and Correlate with Poor Prognosis
Bibliographic record
Abstract
Metastasis is the main reason for lung cancer-related mortality, but little is known about specific determinants of successful dissemination from primary tumors and metastasis initiation. Here, we show that CD133(+)/CXCR4(+) cancer-initiating cells (CIC) directly isolated from patient-derived xenografts (PDX) of non-small cell lung cancer are endowed with superior ability to seed and initiate metastasis at distant organs. We additionally report that CXCR4 inhibition successfully prevents the increase of cisplatin-resistant CD133(+)/CXCR4(+) cells in residual tumors and their metastatization. Immunophenotypic analysis of lung tumor cells intravenously injected or spontaneously disseminated to murine lungs demonstrated the survival advantage and increased colonization ability of a specific subset of CD133(+)/CXCR4(+) with reduced expression of epithelial cell adhesion molecule (EpCAM(-)), which also shows the greatest in vitro invasive potential. We next prove that recovered disseminated cells from lungs of PDX-bearing mice enriched for CD133(+)/CXCR4(+)/EpCAM(-) CICs are highly tumorigenic and metastatic. Importantly, microenvironment stimuli eliciting epithelial-to-mesenchymal transition, including signals from cancer-associated fibroblasts, are able to increase the dissemination potential of lung cancer cells through the generation of the CD133(+)/CXCR4(+)/EpCAM(-) subset. These findings also have correlates in patient samples where disseminating CICs are enriched in metastatic lymph nodes (20-fold, P = 0.006) and their detection in primary tumors is correlated with poor clinical outcome (disease-free survival: P = 0.03; overall survival: P = 0.05). Overall, these results highlight the importance of specific cellular subsets in the metastatic process, the need for in-depth characterization of disseminating tumor cells, and the potential of therapeutic strategies targeting both primary tumor and tumor-microenvironment interactions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".