Abstract B01: Alternative splicing as a therapeutic vulnerability in pediatric rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma, and children who present with metastatic disease at the time of diagnosis have a poor prognosis and survival rate. The molecular events distinguishing these tumors are poorly understood. Comprehensive genetic analysis of pediatric RMS samples has revealed that these tumors are “quiet” at the genomic level and have a relatively low mutation rate. Thus, we have turned our attention to the RMS spliceome, the alternatively or aberrantly spliced transcripts, to identify new therapeutic targets. Indeed, recent advances in high-throughput sequencing technologies have uncovered a surprising number of alternatively spliced variants associated with tumorigenesis, implicating regulated splicing in the tumor phenotype. We hypothesize that differentially spliced isoforms provide a novel opportunity to modulate splicing as a therapeutic intervention in these cancers. We have identified two major splicing networks that are altered in response to tumor specific physiologic changes in RMS. We have identified RNA binding protein and their specific cis-regulatory sequence binding sites that control the splicing changes. In collaboration with Ionis Pharmaceuticals, we have designed splice-switching oligonucleotides that target the specific sequences in the pre-mRNA transcript to occlude protein binding and engineer splicing away from the tumorigenic isoforms. The splice switching oligonucleotides (SSOs) have been successful in targeting the desired transcripts to change the splice isoform, repressing RMS cell growth, and preventing migration, invasion, and angiogenesis. We are currently testing the SSOs in RMS xenograft models as well as in genetically engineered mice to assess their efficacy in vivo. Our long-term goal is to understand the role of alternative splicing in cancer and to target pre-mRNA splicing pathway as a therapeutic intervention point. From these studies, we expect to better understand the cellular events that activate alternative splicing in the pathway to tumorigenesis and how to target transcripts for therapeutic benefit in the future. Furthermore, our findings implement a new splice-modulating paradigm for cancer treatment and pioneer a strategy that may be applied throughout the spliceome to treat cancer and provide a new avenue of rational therapy design. Citation Format: Safiya Khurshid, Matias Montes, Ryan Roberts, Frank Rigo, Dawn S. Chandler. Alternative splicing as a therapeutic vulnerability in pediatric 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 B01.
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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".