O-mannosylation and protein maturation check-points represent therapeutic opportunities in BRAF fusion protein oncogenesis
Bibliographic record
Abstract
Fusions between protein-coding genes are common oncogenic drivers across cancers, typically pairing a proto-oncogene with partner that does not independently drive cancer. In all therapeutically actionable fusions, the proto-oncogene is the drug target, the contributions to oncogenicity of the fusion partner have largely been ignored. We studied the role of BRAF fusion partners and found that they are necessary for transformation. In the setting of KIAA1549::BRAF, the most common fusion protein across brain tumors, we found that KIAA1549 is necessary for the oncogenicity of KIAA1549::BRAF and engenders a striking and specific dependency on the protein O-mannosyltransferase complex (POMT1/2). Specifically, we show that genetic silencing or pharmacologic inhibition of the protein O-mannosyltransferase complex (POMT1/2) reverses fusion-induced transformation, thereby representing a novel and MAPK independent therapeutic target. Furthermore, POMT1/2 is required to glycosylate and enable maturation of the K::B fusion protein. These findings represent a proof-of-concept for targeting the partners in oncogenic fusions as a potential cancer therapeutic strategy.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".