A Combinatorial Strategy for Targeting <i>BRAF</i> V600E-Mutant Cancers with BRAFV600E Inhibitor (PLX4720) and Tyrosine Kinase Inhibitor (Ponatinib)
Bibliographic record
Abstract
Abstract Purpose: Most aggressive thyroid cancers are commonly associated with a BRAFV600E mutation. Preclinical and clinical data in BRAFV600E cancers suggest that combined BRAF and MEK inhibitor treatment results in a response, but resistance is common. One mechanism of acquired resistance is through persistent activation of tyrosine kinase (TK) signaling by alternate pathways. We hypothesized that combination therapy with BRAF and multitargeting TK inhibitors (MTKI) might be more effective in BRAFV600E thyroid cancer than in single-agent or BRAF and MEK inhibitors. Experimental Design: The combined drug activity was analyzed to predict any synergistic effect using high-throughput screening (HTS) of active drugs. We performed follow-up in vitro and in vivo studies to validate and determine the mechanism of action of synergistic drugs. Results: The MTKI ponatinib and the BRAF inhibitor PLX4720 showed synergistic activity by HTS. This combination significantly inhibited proliferation, colony formation, invasion, and migration in BRAFV600E thyroid cancer cell lines and downregulated pERK/MEK and c-JUN signaling pathways, and increased apoptosis. PLX4720-resistant BRAFV600E cells became sensitized to the combination treatment, with decreased proliferation at lower PLX4720 concentrations. In an orthotopic thyroid cancer mouse model, combination therapy significantly reduced tumor growth (P &lt; 0.05), decreased the number of metastases (P &lt; 0.05), and increased survival (P &lt; 0.05) compared with monotherapy and vehicle control. Conclusions: Combination treatment with ponatinib and PLX4720 exhibited significant synergistic anticancer activity in preclinical models of BRAFV600E thyroid cancer, in addition to overcoming PLX4720 resistance. Our results suggest this combination should be tested in clinical trials.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".