Characterisation of the Phosphatidylinositol 3-Kinase Pathway in Non-Small Cell Lung Cancer Cells Isolated from Pleural Effusions
Bibliographic record
Abstract
OBJECTIVES: We hypothesised that it was possible to quantify phosphorylation of important nodes in the phosphatidylinositol 3-kinase (PI3K) pathway in cancer cells isolated from pleural effusions of patients with non-small cell lung cancer (NSCLC) and study their correlation to somatic mutations and clinical outcomes. MATERIALS AND METHODS: Cells were immunomagnetically separated from samples of pleural effusion in patients with NSCLC. p-AKT, p-S6K and p-GSK3β levels were quantified by ELISA; targeted next-generation sequencing was used to characterise mutations in 26 genes. RESULTS: It was possible to quantify phosphoproteins in cells isolated from 38/43 pleural effusions. There was a significant correlation between p-AKT and p-S6K levels [r = 0.85 (95% confidence interval 0.73-0.92), p < 0.0001], but not p-AKT and p-GSK3β levels [r = 0.19 (95% confidence interval -0.16 to 0.5), p = 0.3]. A wide range of mutations was described and p-S6K was higher in samples that harboured at least one mutation compared to those that did not (p = 0.03). On multivariate analysis, p-S6K levels were significantly associated with poor survival (p < 0.01). CONCLUSION: Our study has shown a correlation between p-AKT levels and p-S6K, but not GSK3β, suggesting differences in regulation of the distal PI3K pathway by AKT. Higher p-S6K levels were associated with adverse survival, making it a critically important target in NSCLC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".