Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
UNLABELLED: We generated cell lines resistant to BRAF inhibitors and show that the EGF receptor (EGFR)-SRC family kinase (SFK)-STAT3 signaling pathway was upregulated in these cells. In addition to driving proliferation of resistant cells, this pathway also stimulated invasion and metastasis. EGFR inhibitors cooperated with BRAF inhibitors to block the growth of the resistant cells in vitro and in vivo, and monotherapy with the broad specificity tyrosine kinase inhibitor dasatinib blocked growth and metastasis in vivo. We analyzed tumors from patients with intrinsic or acquired resistance to vemurafenib and observed increased EGFR and SFK activity. Furthermore, dasatinib blocked the growth and metastasis of one of the resistant tumors in immunocompromised mice. Our data show that BRAF inhibitor-mediated activation of EGFR-SFK-STAT3 signaling can mediate resistance in patients with BRAF-mutant melanoma. We describe 2 treatments that seem to overcome this resistance and could deliver therapeutic efficacy in patients with drug-resistant BRAF-mutant melanoma. SIGNIFICANCE: Therapies that target the driver oncogenes in cancer can achieve remarkable responses if patients are stratified for treatment. However, as with conventional therapies, patients often develop acquired resistance to targeted therapies, and a proportion of patients are intrinsically resistant and fail to respond despite the presence of an appropriate driver oncogene mutation. We found that the EGFR/SFK pathway mediated resistance to vemurafenib in BRAF -mutant melanoma and that BRAF and EGFR or SFK inhibition blocked proliferation and invasion of these resistant tumors, providing potentially effective therapeutic options for these patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it