Dasatinib Stimulates Its Own Mechanism of Resistance by Activating a CRTC3/MITF/Bcl-2 Pathway in Melanoma with Mutant or Amplified c-Kit
Bibliographic record
Abstract
Amplification or activating mutations of c-Kit are a frequent oncogenic alteration, which occurs commonly in acral and mucosal melanoma. Among c-Kit inhibitors, dasatinib is the most active due to its ability to bind both active and inactive conformations of the receptor. However, its use as a single agent in melanoma showed limited clinical benefit. We first found that sensitivity to dasatinib is restricted to melanoma cell lines harboring c-Kit alteration but, unexpectedly, we observed lower effect at higher concentrations that can readily be found in patient blood. We then investigated relevant pathway alterations and found complete inhibition of MAPK and PI3K/AKT pathways but an increase in MITF and its downstream target Bcl-2 through CRTC3 pathway, which turn on the CREB regulated transcription of MITF. More importantly, dasatinib upregulates MITF and Bcl-2 through SIK2 inhibition revealed by CRTC3 reduced phosphorylation, CREB transcription activation of MITF, MITF transcription activation of Bcl-2 as well as pigmentation. Furthermore, overexpression of MITF renders melanoma cells resistant to all dasatinib concentrations. Selective Bcl-2 inhibition by ABT-199 or Bcl-2 knockout restores the sensitivity of melanoma cells to dasatinib, validating the involvement of MITF and Bcl-2 axis in the resistance of melanoma to dasatinib. In conclusion, we showed for the first time that dasatinib in melanoma stimulates its proper mechanism of resistance, independently of MAPK and PI3K/AKT pathways reactivation commonly associated to secondary c-Kit mutations, but through CRTC3/MITF/Bcl-2 pathway activation at clinically relevant doses which may explain the weak clinical benefit of dasatinib in patients with melanoma. IMPLICATIONS: Dasatinib stimulates its proper mechanism of resistance through CRTC3/MITF/Bcl-2 pathway, which may explain its modest clinical efficiency in patients with melanoma.
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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) | 0.002 | 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".