Data from Adaptive Resistance to Dual BRAF/MEK Inhibition in BRAF-Driven Tumors through Autocrine FGFR Pathway Activation
Bibliographic record
Abstract
AbstractPurpose: Combined MAPK pathway inhibition using dual BRAF and MEK inhibitors has prolonged the duration of clinical response in patients with BRAFV600E-driven tumors compared with either agent alone. However, resistance frequently arises. Experimental Design: We generated cell lines resistant to dual BRAF/MEK inhibition and utilized a pharmacologic synthetic lethal approach to identify a novel, adaptive resistance mechanism mediated through the fibroblast growth factor receptor (FGFR) pathway. Results: In response to drug treatment, transcriptional upregulation of FGF1 results in autocrine activation of FGFR, which potentiates extracellular signal-regulated kinases (ERK) activation. FGFR inhibition overcomes resistance to dual BRAF/MEK inhibitors in both cell lines and patient-derived xenograft (PDX) models. Abrogation of this bypass mechanism in the first-line setting enhances tumor killing and prevents the emergence of drug-resistant cells. Moreover, clinical data implicate serum FGF1 levels in disease prognosis. Conclusions: Taken together, these results describe a new, adaptive resistance mechanism that is more commonly observed in the context of dual BRAF/MEK blockade as opposed to single-agent treatment and reveal the potential clinical utility of FGFR-targeting agents in combination with BRAF and MEK inhibitors as a promising strategy to forestall resistance in a subset of BRAF-driven cancers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".