Abstract A032: Molecular insights into KIAA1549::BRAF fusion proteins: Implications for targeted therapy in pediatric low-grade gliomas
Bibliographic record
Abstract
Abstract Pilocytic astrocytomas (PAs) are low-grade gliomas (LGGs) and predominantly affect children under 14 years of age. PAs are driven by aberrant activation of the RAS/RAF/MEK/ERK axis, primarily by BRAF alterations. As a key activator of this pathway, the Ser/Thr-kinase BRAF plays a crucial role in promoting cellular survival and proliferation and is one of the most frequently mutated kinases in cancer. While the most prevalent mutation, BRAFV600E, is commonly associated with hairy cell leukemia, melanoma, and thyroid carcinomas, the KIAA1549::BRAF fusion is the hallmark oncogene of LGGs, particularly PAs. Further, due to advancements in precision oncology diagnostics, this fusion is also increasingly identified in other tumor types. Various breakpoint variants of the KIAA1549::BRAF fusion gene were reported, with the most common involving KIAA1549 exons 1-16 and BRAF exons 9-18. The oncogenic potential of KIAA1549::BRAF is attributed to the loss of exons encoding the autoinhibitory domains of BRAF, although there is increasing evidence that the fusion partners of RAF oncoproteins can influence their functionality and signalling. Despite the high frequency of KIAA1549::BRAF fusions, the function, topology, and localization of KIAA1549, an enigmatic putative transmembrane protein, remains unclear. In contrast to the cytoplasmic localisation of wild-type BRAF, point mutants and insertion or deletion mutants, KIAA1549::BRAF fusions retain the single transmembrane domain of the KIAA1549 portion, localizing it to the plasma membrane. This localization requires trafficking through the ER/Golgi system during maturation. Additionally, using TAILS mass spectrometry and genomic approaches, we demonstrate that KIAA1549 and KIAA1549::BRAF fusions are cleaved at a specific cleavage motif in the extracellular portion of the protein. Mutation of the cleavage motif slows down cell growth compared to non-cleavage impaired fusions. Known protease inhibitors can target this cleavage event, offering a promising strategy for drug development. Apart from a very recently approved RAF inhibitor (RAFi), there are very little options available for directly targeting KIAA1549::BRAF fusion proteins. This is due to the fact that the RAFi developed for BRAFV600E are ineffective against BRAF fusions and probably also due to our limited understanding of the influence of KIAA1549. Consequently, our data could support the development of urgently needed targeted therapies for this BRAF fusion protein. Citation Format: Daniel Christen, Sean Misek, Anna Borgenvik, Gloria Kyrila, Alexander Zhang, Sarah Reel, Michelle Boisvert, Kelly Cai, Kevin Zhou, Elizabeth M Gonzales, Amy Goodale, Esteban Miglietta, Jacquelyn Jones, Seth Malinowski, Lobna Elsadek, Merve Ozdemir, Zachary Eisenbies, Joohee Lee, April Apfelbaum, Jenna Robinson, Antonio Maldera, Daniel Bondeson, Jason Kwon, Mounica Vallurupalli, Sangita Pal, Todd Golub, William Hahn, Eric Fischer, Jesse Boehm, Jörn Dengjel, Henrik Clausen, Nada Jabado, Till Milde, Beth Cimini, Keith Ligon, Kathrine Janeway, Michael Eck, David Root, David Jones, Timothy N Phoenix, Rameen Beroukhim, Hiren Jitendra Joshi, Tilman Brummer, Adnan Halim, Pratiti Bandopadhayay. Molecular insights into KIAA1549::BRAF fusion proteins: Implications for targeted therapy in pediatric low-grade gliomas [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr A032.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".