The AhR‐SRC axis as a therapeutic vulnerability in BRAFi‐resistant melanoma
Bibliographic record
Abstract
The nongenetic mechanisms required to control tumor phenotypic plasticity and shape drug‐resistance remain unclear. We show here that the Aryl hydrocarbon Receptor (AhR) transcription factor directly regulates the gene expression program associated with the acquisition of resistance to BRAF inhibitor (BRAFi) in melanoma. In addition, we show in melanoma cells that canonical activation of AhR mediates the activation of the SRC pathway and promotes the acquisition of an invasive and aggressive resistant phenotype to front‐line BRAFi treatment in melanoma. This nongenetic reprogramming identifies a clinically compatible approach to reverse BRAFi resistance in melanoma. Using a preclinical BRAFi‐resistant PDX melanoma model, we demonstrate that SRC inhibition with dasatinib significantly re‐sensitizes melanoma cells to BRAFi. Together we identify the AhR/SRC axis as a new therapeutic vulnerability to trigger resistance and warrant the introduction of SRC inhibitors during the course of the treatment in combination with front‐line therapeutics to delay BRAFi resistance. This study reports that the AhR/SRC axis constitutes a therapeutic vulnerability in BRAFi‐resistant melanoma. This study reports that the AhR/SRC axis constitutes a therapeutic vulnerability in BRAFi‐resistant 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".