Integrated Cell-Based Platform to Study EGFR Activation and Transactivation
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) pathway is one of the most deregulated molecular pathways in human epithelial cancers. Many approved drugs were optimized to directly target EGFR but yielded only modest clinical improvement in cancer patients due to low efficacy and drug resistance. Transactivation of EGFR by other cell surface receptors such as G-protein-coupled receptors (GPCRs) was proposed to explain this lack of efficacy. Even if direct EGFR activation and transactivation by GPCR contribute to the activation of the same signaling pathways, they are often studied as independent events resulting in partial investigation of a drug's mechanism of action. We present a novel high-throughput approach that integrates interrogation of direct activation of EGFR and its transactivation via GPCR activation. Using distinct technology platforms, three readouts were used to measure (1) direct activation of GPCR via cyclic adenosine monophosphate (cAMP) detection, (2) direct activation of EGFR through the release of intracellular Ca(2+), and (3) EGFR transactivation by GPCR using the detection of p-extracellular-signal-regulated kinases 1/2 (p-ERK1/2). In addition to being simple, quick, and homogenous, our methods were shown to be more sensitive than those in current use. These enabling tools should improve the knowledge pertaining to GPCRs and receptor tyrosine kinases trans-regulation and facilitate the design of more potent and better targeted new therapeutic strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".