Abstract B35: Liaison between <i>SNAI2</i> and <i>MYOD</i> enhances oncogenesis and suppresses differentiation in fusion-negative rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma (RMS) is a pediatric malignancy of the muscle, and patients with high-risk fusion-negative RMS (FN-RMS), the major subtype of this disease, are associated with RAS pathway activating mutations and have a poor survival rate of <30%. RMS cells express the myogenic master transcription factors MYOD and MYOG and yet are unable to differentiate. Here, we report an oncogenic role for SNAI2, which is highly expressed in FN-RMS, that blocks differentiation and promotes tumor growth through inhibition of a MYOD, MYOG, and MEF2 program. HiC analyses of the tridimensional-structure around the SNAI2 locus in IMR-90 cells find that it is regulated by a 1.2 mb enhancer regulatory region. In RMS tumors, MYOD can engage multiple SNAI2 enhancers and directly induce SNAI2 expression, while SNAI2 knockdown in RMS RD and JR1 results in increased expression of MYOD, indicating that SNAI2 in turn can repress MYOD expression. Employing two validated shRNAs to knock down SNAI2 in FN-RMS RD, JR1, and RD18 cells, we find that SNAI2 plays an oncogenic role by blocking myogenic differentiation and promoting growth both in vitro and in vivo in murine xenograft experiments. SNAI2 knockdown potentiates vincristine treatments and expands differentiation and greatly reduces tumor growth in vivo. In order to understand molecularly how SNAI2 blocks differentiation and promotes growth, we optimized ChIP-seq experiments in FN-RMS RD, JR1, and SMS-CTR cells to define genome-wide chromatin binding of SNAI2 and MYOD, with or without SNAI2 knockdown. Our ChIP-sequencing experiments confirmed that SNAI2 binds EBoxes, including the SNAI2 motif and motifs that MYOD and MYOG engage. Importantly, combining ChIPseq and RNAseq analyses, we discovered that SNAI2 preferentially binds EBox elements associated with enhancer elements and regulates gene expression by dampening enhancer function. SNAI2 competes with MYOD at a subset of myogenic enhancers associated with terminal differentiation, thus blocking differentiation in FN-RMS cells while potentially enabling progrowth effects of MYOD. SNAI2 also downregulates the expression of a MYOG, MEF2A/C/D, and CDKN1A differentiation program that is suppressed in FN-RMS cells, which upon reactivation along with MYOD drives robust differentiation and inhibits tumor xenograft growth in mice. Finally, we establish that SNAI2 function is downstream of the RAS program involved in blocking differentiation. In summary, SNAI2, through inhibition of a MYOD, MYOG, MEF2, and CDNK1A program, blocks tumor differentiation and promotes growth in FN-RMS. Citation Format: Silvia Pomella, Prethish Sreenivas, Berkley E. Gryder, Long Wang, Matteo Cassandri, Kunal Baxi, Nicole R. Hensch, Elena Carcarino, Young Song, Marielle Yohe, Bruno Amadio, Ignazio Caruana, Cristiano De Stefanis, Rita De Vito, Franco Locatelli, Yidong Chen, Eleanor Y. Chen, Peter Houghton, Javed Khan, Rossella Rota, Myron S. Ignatius. Liaison between SNAI2 and MYOD enhances oncogenesis and suppresses differentiation in fusion-negative rhabdomyosarcoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B35.
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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 | 0.001 |
| 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".