Abstract PR005: PRC2 inactivating mutations amplify cell death in response to DNMT1-targeted therapy through enhanced viral mimicry in malignant peripheral nerve sheath tumor
Bibliographic record
Abstract
Abstract Polycomb Repressive Complex 2 (PRC2) establishes and maintains di- and tri-methylation at histone 3 at lysine 27 (H3K27me2/3) in the genome and plays oncogenic and tumor suppressor roles in context-dependent cancer pathogenesis. While there is clinical success of therapeutically targeting PRC2 core component, EZH2, in PRC2-dependent cancers (e.g., follicular lymphoma, epithelioid sarcoma), it remains an unmet therapeutic bottleneck in PRC2-inactivated cancer. Biallelic inactivating mutations in PRC2 core components are a hallmark feature of high-grade malignant peripheral nerve sheath tumor (MPNST), an aggressive subtype of sarcoma with poor prognosis and no effective targeted therapeutics. Using a custom RNAi-based drop out screen, we observed that PRC2-inactivation is synthetic lethal with DNA methyltransferase 1 (DNMT1) downregulation; we further observed that small molecule DNMT inhibitors (DNMTis) resulted in enhanced cytotoxicity and anti-tumor responses in PRC2-loss cancer context in vitro and in vivo. Mechanistically, DNMTi-mediated de-repression of retrotransposons (e.g., endogenous retroviral elements) and gene targets is partly restricted by PRC2, which potentially contributes to limited therapeutic activity in PRC2-wild-type (wt) cancer context. In contrast, DNMTi treatment synergizes with PRC2 inactivation and cooperatively amplifies the expression of retrotransposons, and subsequent viral mimicry response that promotes robust cell death in part through PKR-dependent double stranded-RNA (dsRNA) sensing. Collectively, our observations posit DNA methylation as a safeguard against anti-tumorigenic cell fate decisions in the context of PRC2-inactivation to promote cancer pathogenesis, identify DNMT1-targeted therapy as novel therapeutic strategy for PRC2-inactivated MPNST, and merit further preclinical and clinical investigation of this strategy in other PRC2-inactivated cancers. Citation Format: Ping Chi, Amish J. Patel, Sarah Warda, Jesper L.V. Maag, Rohan Misra, Miguel A. Miranda-Román, Mohini R. Pachai, Cindy J. Lee, Dan Li, Naitao Wang, Gabriella Bayshtok, Eve Fishinevich, Yinuo Meng, Elissa W.P. Wong, Juan Yan, Emily Giff, Jonathan Fletcher, Joseph M. Scandura, Richard Koche, Jacob L. Glass, Cristina R. Antonescu, Deyou Zheng, Yu Chen. PRC2 inactivating mutations amplify cell death in response to DNMT1-targeted therapy through enhanced viral mimicry in malignant peripheral nerve sheath tumor [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR005.
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) | 0.003 | 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".