Loss of USP28-mediated BRAF degradation drives resistance to RAF cancer therapies
Bibliographic record
Abstract
RAF kinase inhibitors are clinically active in patients with BRAF (V600E) mutant melanoma. However, rarely do tumors regress completely, with the majority of responses being short-lived. This is partially mediated through the loss of negative feedback loops after MAPK inhibition and reactivation of upstream signaling. Here, we demonstrate that the deubiquitinating enzyme USP28 functions through a feedback loop to destabilize RAF family members. Loss of USP28 stabilizes BRAF enhancing downstream MAPK activation and promotes resistance to RAF inhibitor therapy in culture and in vivo models. Importantly, we demonstrate that USP28 is deleted in a proportion of melanoma patients and may act as a biomarker for response to BRAF inhibitor therapy in patients. Furthermore, we identify Rigosertib as a possible therapeutic strategy for USP28-depleted tumors. Our results show that loss of USP28 enhances MAPK activity through the stabilization of RAF family members and is a key factor in BRAF inhibitor resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".