Lung Cancer Driven by BRAFG469V Mutation Is Targetable by EGFR Kinase Inhibitors
Bibliographic record
Abstract
Introduction Mutations in BRAF occur in 2% to 4% of patients with lung adenocarcinoma. Combination dabrafenib and trametinib, or single-agent vemurafenib is approved only for patients with cancers driven by the V600E BRAF mutation. Targeted therapy is not currently available for patients harboring non-V600 BRAF mutations. Methods A lung adenocarcinoma patient-derived xenograft model (PHLC12) with wild-type and nonamplified EGFR was tested for response to EGFR tyrosine kinase inhibitors (TKIs). A cell line derived from this model (X12CL) was also used to evaluate drug sensitivity and to identify potential drivers by small interfering RNA knockdown. Kinase assays were used to test direct targeting of the candidate driver by the EGFR TKIs. Structural modeling including, molecular dynamics simulations, and binding assays were conducted to explore the mechanism of off-target inhibition by EGFR TKIs on the model 12 driver. Results Both patient-derived xenograft PHLC12 and the X12CL cell line were sensitive to multiple EGFR TKIs. The BRAF G469V mutation was found to be the only known oncogenic mutation in this model. Small interfering RNA knockdown of BRAF, but not the EGFR, killed X12CL, confirming BRAF G469V as the oncogenic driver. Kinase activity of the BRAF protein isolated from X12CL was inhibited by treatment with the EGFR TKIs gefitinib and osimertinib, and expression of BRAF G469V in non–EGFR–expressing NR6 cells promoted growth in low serum condition, which was also sensitive to EGFR TKIs. Structural modeling, molecular dynamic simulations, and in vitro binding assays support BRAF G469V being a direct target of the TKIs. Conclusions Clinically approved EGFR TKIs can be repurposed to treat patients with non-small cell lung cancer harboring the BRAF G469V mutation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".