Differential disruption of cell cycle pathways in small cell and non-small cell lung cancer
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related mortality in the world, with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) comprising the two major cell types. Although these cell types can be distinguished readily at the histological level, knowledge of their underlying molecular differences is very limited. In this study, we compared 14 SCLC cell lines against 27 NSCLC cell lines using an integrated array comparative genomic hybridisation and gene expression profiling approach to identify subtype-specific disruptions. Using stringent criteria, we have identified 159 of the genes that are responsible for the different biology of these cell types. Sorting of these genes by their biological functions revealed the differential disruption of key components involved in cell cycle pathways. Our novel comparative combined genome and transcriptome analysis not only identified differentially altered genes, but also revealed that certain shared pathways are preferentially disrupted at different steps in these cell types. Small cell lung cancer exhibited increased expression of MRP5, activation of Wnt pathway inhibitors, and upregulation of p38 MAPK activating genes, while NSCLC showed downregulation of CDKN2A, and upregulation of MAPK9 and EGFR. This information suggests that cell cycle upregulation in SCLC and NSCLC occurs through drastically different mechanisms, highlighting the need for differential molecular target selection in the treatment of these cancers.
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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 | 0.000 |
| 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".