Titration of signalling output: insights into clinical combinations of MEK and AKT inhibitors
Bibliographic record
Abstract
BACKGROUND: We aimed to understand the relative contributions of inhibiting MEK and AKT on cell growth to guide combinations of these agents. MATERIALS AND METHODS: A panel of 20 cell lines was exposed to either the MEK inhibitor, PD0325901, or AKT inhibitor, AKT 1/2 inhibitor. p-ERK and p-S6 ELISAs were used to define degrees of MEK and AKT inhibition, respectively. Growth inhibition to different degrees of MEK and AKT inhibition, either singly or in combination using 96-h sulphorhodamine assays was then studied. RESULTS: A significantly greater growth inhibition was seen in BRAF(M) and PIK3CA(M) cells upon maximal MEK (P = 0.004) and AKT inhibition (P = 0.038), respectively. KRAS(M) and BRAF/PIK3CA/KRAS(WT) cells were not significantly more likely to be sensitive to MEK or AKT inhibition. Significant incremental growth inhibition of the combination of MEK + AKT over either MEK or AKT inhibition alone was seen when MEK + AKT was inhibited maximally and not when sub-maximal inhibition of both MEK + AKT was used (11/20 cell lines versus 1/20 cell lines; P = 0.0012). CONCLUSIONS: KRAS(M) cells are likely to benefit from combinations of MEK and AKT inhibitors. Sub-maximally inhibiting both MEK and AKT within a combination, in a majority of instances, does not significantly increase growth inhibition compared with maximally inhibiting MEK or AKT alone and alternative phase I trial designs are needed to clinically evaluate such combinations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".