Measurement of MAP kinase activation by flow cytometry using phospho‐specific antibodies to MEK and ERK: Potential for pharmacodynamic monitoring of signal transduction inhibitors
Bibliographic record
Abstract
Cancer cells frequently show abnormal signaling via the mitogen activated protein kinase (MAP kinase) pathway due to increased activity of surface receptors for growth factors, or as a result of ras mutations. The development of potent anti-cancer agents that target this pathway prompts the need for analytical methods that allow pharmacodynamic monitoring of drug effects in patients during early phase clinical trial. We describe such a method, based on the activation of T-lymphocytes in undiluted peripheral blood using phorbol myristate acetate (PMA). Following rapid hypotonic lysis and formaldehyde fixation, activation of the MAP kinase pathway can then be demonstrated using phospho-specific antibodies that recognize the activated mediators MEK or ERK, followed by surface labeling with anti-CD3 to identify T-lymphocytes. This method was used to investigate the effects of a MEK inhibitor, U0126, and a new raf kinase inhibitor BAY 37-9751 in blood samples from normal donors. Dose-dependent inhibition of pERK activation was demonstrated for both agents. Furthermore, differential effects on pMEK activation allowed the molecular targets of the two inhibitors to be distinguished. In addition to monitoring drug effects in patients during treatment with inhibitors of the MAP kinase pathway, the general methodology described in this paper has the potential for wide application to the study of signal transduction at the single cell level using flow cytometry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".