Development and characterisation of a panel of phosphatidylinositide 3-kinase – mammalian target of rapamycin inhibitor resistant lung cancer cell lines
Bibliographic record
Abstract
The PI3K-mTOR pathway is involved in regulating all hallmarks of cancer, and is often dysregulated in NSCLC, making it an attractive therapeutic target in this setting. Acquired resistance to PI3K-mTOR inhibition is a major hurdle to overcome in the success of PI3K-mTOR targeted agents. H460, A549, and H1975 resistant cells were generated by prolonged treatment in culture with Apitolisib (GDC-0980), a dual PI3K-mTOR inhibitor over a period of several months, from age-matched parent cells. Resistance was deemed to have developed when a log fold difference in IC50 had been achieved. Resistant cell lines also exhibited resistance to another widely investigated PI3K-mTOR dual inhibitor; Dactolisib (BEZ235). Cell lines were characterised at the level of mRNA (expression array profiling expression of >150 genes), miRNA (expression array profiling of 2100 miRNAs), protein (bottoms-up label-free mass spectrometry) and phosphoprotein (expression array profiling of 84 phospho/total proteins). Key alterations were validated by qPCR and Western blot. H1975 cells were initially most sensitive to Apitolisib (GDC-0980), but developed resistance more quickly than the other cell lines, perhaps due to increased selective pressure from the impressive initial effect. In-depth molecular profiling suggested epithelial-mesenchymal transition (EMT) may play a role in resistance to PI3K-mTOR dual inhibition 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".