3′-Deoxy-3′-[18F]Fluorothymidine Positron Emission Tomography Is a Sensitive Method for Imaging the Response of BRAF-Dependent Tumors to MEK Inhibition
Bibliographic record
Abstract
Activating mutations of BRAF occur in approximately 7% of all human tumors and in the majority of melanomas. These tumors are very sensitive to pharmacologic inhibition of mitogen-activated protein kinase/extracellular signal-regulated kinase kinase (MEK), which causes loss of D-cyclin expression, hypophosphorylation of Rb, and G(1) arrest. Growth arrest is followed by differentiation or senescence and, in a subset of BRAF mutant tumors, by apoptosis. The former effects result in so-called "stable disease" and, in patients with cancer, can be difficult to distinguish from indolent tumor growth. The profound G(1) arrest induced by MEK inhibition in BRAF mutant tumors is associated with a marked decline in thymidine uptake and is therefore potentially detectable in vivo by noninvasive 3'-deoxy-3'-[(18)F]fluorothymidine ([(18)F]FLT) positron emission tomography (PET) imaging. In SKMEL-28 tumor xenografts, MEK inhibition completely inhibited tumor growth and induced differentiation with only modest tumor regression. MEK inhibition also resulted in a rapid decline in the [(18)F]FLT signal in V600E BRAF mutant SKMEL-28 xenografts but not in BRAF wild-type BT-474 xenografts. The data suggest that [(18)F]FLT PET can effectively image induction of G(1) arrest by MEK inhibitors in mutant BRAF tumors and may be a useful noninvasive method for assessing the early biological response to this class of drugs.
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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 | 0.001 |
| 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 | 0.001 | 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".