Integrated Proteomic and Epigenomic Analysis Reveals IGF2 as a Vulnerability in PRC2-Deficient Malignant Peripheral Nerve Sheath Tumors
Bibliographic record
Abstract
Abstract Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive sarcomas with limited therapeutic options. Loss of the Polycomb repressive complex 2 (PRC2), via inactivating mutations in SUZ12 or EED, occurs frequently in MPNSTs and is associated with poor prognosis. However, the downstream chromatin and signaling consequences of these mutations remain incompletely understood. Here, we show that PRC2 deficiency in MPNST cells induces coordinated chromatin remodeling, characterized by loss of repressive H3K27me3 and gain of activating marks, including H3K27ac and H3K36me2. Integrative epigenomic, transcriptomic, and proteomic profiling revealed that this chromatin reprogramming activates a fetal-like growth signature centered on insulin-like growth factor 2 (IGF2) and its post-transcriptional regulators, Insulin-like Growth Factor 2 mRNA-Binding Protein (IGF2BP1-3). Functional studies demonstrate that PRC2-deficient cells are selectively dependent on IGF2 for proliferation, and that restoration of SUZ12 suppresses IGF2 expression and reduces growth. Analysis of human MPNST tumors confirms upregulation of the IGF2-IGF2BP axis in PRC2-deficient tumors, highlighting its clinical relevance. Together, these findings link PRC2 loss to activation of fetal growth factor-driven oncogenic signaling and identify IGF2 and its regulatory network as potential vulnerabilities in this aggressive tumor subtype.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".