Data from Adaptive Resistance to Dual BRAF/MEK Inhibition in BRAF-Driven Tumors through Autocrine FGFR Pathway Activation
Bibliographic record
Abstract
<div>AbstractPurpose:<p>Combined MAPK pathway inhibition using dual BRAF and MEK inhibitors has prolonged the duration of clinical response in patients with BRAF<sup>V600E</sup>-driven tumors compared with either agent alone. However, resistance frequently arises.</p>Experimental Design:<p>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.</p>Results:<p>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.</p>Conclusions:<p>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.</p></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".