CSIG-07. JAK1/STAT1/3 COOPERATES WITH THE CIC-FUSIONS TO DRIVE CIC-REARRANGED SARCOMAS
Bibliographic record
Abstract
Abstract CIC-rearranged sarcoma (CRS) is an rare disease driven by a specific fusion protein involving the CIC gene. Occurrence in the brain is 3% in all CRS patients. The native CIC protein is a transcriptional repressor of the (RTK)/Ras/ERK signaling pathway, which is one of the most tumorigenic pathways in cancer. The most common rearrangement is with the double homeobox 4 (DUX4) transcription factor (CIC-DUX4), and others, such as CIC-NUTM1 fusions, have been identified in a subset of pediatric primitive neuroectodermal tumors. However, the molecular mechanisms by which CIC-fusions drive CRS remain unknown. Preliminary data shows that CIC-DUX4/NUTM1 fusions activate JAK and its downstream effector STAT1/3. We hypothesize that the JAK/STAT1/3 signal transduction pathway cooperates with CIC-fusions to induce the expression of oncogenic transcription factors ETV1/4/5 and drive these sarcomas. We show high levels of JAK1/STAT1/3 activation in patient-derived CRS cell lines (NCC-SCC-89/C) as compared to other sarcoma cell lines without the fusion. JAK1 inhibition using Solicitinib and Ruxolitinib reduced phosphorylation of STAT1/3 as well as protein and mRNA expression of ETV1/4/5, promoter activity of ETV5, cell proliferation and tumorgenicity. Interestingly DUX4 is involved in histone acetylation and STAT1 has been shown to activate p300/CBP complex which lead us to evaluating the role of STAT1 in the gene activation CRS. We elucidate mechanistically that the fusion proteins increase binding of STAT1/3 at the promoters of ETV 1/4/5. Importantly, JAK1 inhibition effectively reduces histone acetylation induced by CIC-fusions at the promoters of ETV1/4/5. To evaluate the pre-clinical effect of targeting the JAK1/STAT1/3 pathway, A CRS cell line (NCC-SCC-89/C) were grafted into NSG mice. Ruxolitinib treatment significantly reduced tumor volume, STAT activation, and ETV1/4/5. In conclusion, we show that the JAK1/STAT1/3 pathway plays a critical role in cooperating with CIC-fusions to drive CRS, providing insight into potential therapeutic avenues for these aggressive tumors.
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) | 0.002 | 0.001 |
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".