Growth Factor Independence-1 Is Expressed in Primary Human Neuroendocrine Lung Carcinomas and Mediates the Differentiation of Murine Pulmonary Neuroendocrine Cells
Bibliographic record
Abstract
Human small cell lung cancers might be derived from pulmonary cells with a neuroendocrine phenotype. They are driven to proliferate by autocrine and paracrine neuropeptide growth factor stimulation. The molecular basis of the neuroendocrine phenotype of lung carcinomas is relatively unknown. The Achaete-Scute Homologue-1 (ASH1) transcription factor is critically required for the formation of pulmonary neuroendocrine cells and is a marker for human small cell lung cancers. The Drosophila orthologues of ASH1 (Achaete and Scute) and the growth factor independence-1 (GFI1) oncoprotein (Senseless) genetically interact to inhibit Notch signaling and specify fly sensory organ development. Here, we show that GFI1, as with ASH1, is expressed in neuroendocrine lung cancer cell lines and that GFI1 in lung cancer cell lines functions as a DNA-binding transcriptional repressor protein. Forced expression of GFI1 potentiates tumor formation of small-cell lung carcinoma cells. In primary human lung cancer specimens, GFI1 expression strongly correlates with expression of ASH1, the neuroendocrine growth factor gastrin-releasing peptide, and neuroendocrine markers synaptophysin and chromogranin A (P < 0.0000001). GFI1 colocalizes with chromogranin A and calcitonin-gene-related peptide in embryonic and adult murine pulmonary neuroendocrine cells. In addition, mice with a mutation in GFI1 display abnormal development of pulmonary neuroendocrine cells, indicating that GFI1 is important for neuroendocrine differentiation.
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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) | 0.000 | 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".