Abstract 3667: Inactivating BRAF mutations confer dasatinib sensitivity in lung cancer
Bibliographic record
Abstract
Abstract NSCLC is a lethal disease for which personalized therapies that target specific genetic aberrations have been markedly effective in subsets of patients. An important approach for discovering effective cancer therapeutic targets is to characterize responsive tumors. We conducted a phase II trial of the tyrosine kinase inhibitor dasatinib in stage IV NSCLC. The dramatic response of one patient, who remains cancer free four years later, led us to examine the molecular characteristics of his tumor. We performed a comprehensive analysis of this NSCLC patient's tumor including mutational analysis of 40 genes, array comparative genomic hybridization, and immunohistochemistry. We discovered a novel, inactivating BRAF mutation (Y472CBRAF) in the patient's tumor; no inactivating BRAF mutations were found in the non-responding patients. Cells transfected with Y472CBRAF exhibited CRAF, MEK, and ERK activation, which were identical to signaling changes that occur with previously known inactivating BRAF mutants. Dasatinib induced senescence in NSCLC cells with endogenous inactivating BRAF mutations. Transfection of cells with inactivating BRAF mutations led to increased dasatinib sensitivity; conversely, cells transfected with an activating BRAF mutation were more resistant. Likewise, BRAF inhibition in NSCLC cells expressing wild-type BRAF enhanced dasatinib sensitivity. Dasatinib sensitivity may depend upon CRAF since dasatinib led to decreased CRAF activity and only NSCLC cells with inactivating BRAF mutations were sensitive to CRAF inhibition. We hypothesize that patient's BRAF mutation was likely responsible for his marked response to dasatinib and suggests that tumors bearing inactive BRAF mutations will be exquisitely sensitive to dasatinib. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3667. doi:1538-7445.AM2012-3667
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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) | 0.003 | 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".