Sorafenib Inhibits the Imatinib-Resistant <i>KIT</i> <i>T670I</i> Gatekeeper Mutation in Gastrointestinal Stromal Tumor
Bibliographic record
Abstract
PURPOSE: Resistance is commonly acquired in patients with metastatic gastrointestinal stromal tumor who are treated with imatinib mesylate, often due to the development of secondary mutations in the KIT kinase domain. We sought to investigate the efficacy of second-line tyrosine kinase inhibitors, such as sorafenib, dasatinib, and nilotinib, against the commonly observed imatinib-resistant KIT mutations (KIT(V654A), KIT(T670I), KIT(D820Y), and KIT(N822K)) expressed in the Ba/F3 cellular system. EXPERIMENTAL DESIGN: In vitro drug screening of stable Ba/F3 KIT mutants recapitulating the genotype of imatinib-resistant patients harboring primary and secondary KIT mutations was investigated. Comparison was made to imatinib-sensitive Ba/F3 KIT mutant cells as well as Ba/F3 cells expressing only secondary KIT mutations. The efficacy of drug treatment was evaluated by proliferation and apoptosis assays, in addition to biochemical inhibition of KIT activation. RESULTS: Sorafenib was potent against all imatinib-resistant Ba/F3 KIT double mutants tested, including the gatekeeper secondary mutation KIT(WK557-8del/T670I), which was resistant to other kinase inhibitors. Although all three drugs tested decreased cell proliferation and inhibited KIT activation against exon 13 (KIT(V560del/V654A)) and exon 17 (KIT(V559D/D820Y)) double mutants, nilotinib did so at lower concentrations. CONCLUSIONS: Our results emphasize the need for tailored salvage therapy in imatinib-refractory gastrointestinal stromal tumors according to individual molecular mechanisms of resistance. The Ba/F3 KIT(WK557-8del/T670I) cells were sensitive only to sorafenib inhibition, whereas nilotinib was more potent on imatinib-resistant KIT(V560del/V654A) and KIT(V559D/D820Y) mutant cells than dasatinib and sorafenib.
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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) | 0.001 | 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".