CDK4/6 and SHP2 mediate BRAF/MEK inhibitor resistance in Class 2 and 3 BRAF mutant cancers
Bibliographic record
Abstract
Abstract Class 2 and 3 non-V600E BRAF mutations are oncogenic drivers in many cancer types. Currently, there are no established targeted therapies with proven efficacy for cancers with non-V600E BRAF mutations. We developed the investigator-initiated, Phase II BEAVER clinical trial ( NCT03839342 ) to evaluate the efficacy of BRAF and MEK inhibitors in patients with non-V600E BRAF mutations. The best objective response rate was 14% (3/21). By analyzing genomic data from patient tumors, circulating tumor DNA (ctDNA), patient-derived xenograft (PDX) models generated from enrolled patients, and functional genomics of Class 2 & 3 BRAF mutant cell lines, we discovered MAPK-dependent and independent mechanisms of intrinsic and acquired resistance to BRAF/MEK inhibition. These included the acquisition of new mutations in NRAS, MAP2K1, RAF1, and RB in ctDNA at the time of disease progression. We observed an enrichment for alterations in genes that regulate cell cycle progression amongst non-responders and increased expression of genes mediating cell cycle progression in tumors Class 2 BRAF mutant cell lines with acquired resistance to BRAF/MEK inhibitors. In Class 3 BRAF mutant cancers specifically, PTPN11 (SHP2) was an essential gene. CDK4/6 and SHP2 were found to mediate intrinsic resistance to BRAF/MEK inhibition in Class 2 & 3 BRAF mutant tumors. Therapeutic strategies combining CDK4/6 or SHP2 inhibitors with BRAF/MEK inhibitors were more effective than BRAF/MEK inhibitors alone in vitro and in vivo , highlighting the need to explore therapeutic targets outside of the MAPK pathway in these hard-to-treat Class 2 & 3 BRAF mutant 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".