JAK/STAT PATHWAY COOPERATES WITH CIC FUSIONS TO DRIVE CIC-REARRANGED SARCOMAS
Bibliographic record
Abstract
Abstract CIC-rearranged sarcoma is a rare disease driven by a specific fusion protein involving the CIC gene. 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 induce tumorigenesis remains unknown. Hypothesis: Our preliminary data shows that CIC- DUX4/NUTM1 fusions activate JAK (cytokine receptors) and its downstream effector STAT1/3(transcription factors). 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. METHODS AND RESULTS: JAK1 is a potential target for CIC-fusions- We show high levels of JAK1/STAT1/3 activation in CIC-fusion patient-derived sarcoma cell lines (NCC-SCC-89/C) as compared to other sarcoma cell lines without the fusion. JAK1 inhibition using Solicitinub and Ruxolitinib reduced phosphorylation of STAT1/3 as well as protein and mRNA expression of transcriptional oncogenic factors ETV1/4/5 and cell proliferation. JAK1/STAT1/3 inhibition in patient-derived CIC-DUX4 xenograft models- To evaluate the pre-clinical effect of targeting the JAK1/STAT1/3 pathway, the CIC-DUX4 cell line was grafted into NSG mice. Ruxolitinib treatment significantly reduced tumor volume, STAT1 activation, and oncogenic transcriptional factors ETV1/4/5. CONCLUSION: We show that the JAK1/STAT1/3 pathway plays a critical role in cooperating with CIC-fusions to drive CIC-rearranged sarcomas providing insight into potential therapeutic avenues for these aggressive tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".