Abstract B021: Long non-coding RNAs that are required for robust cell growth in ewing Sarcoma
Bibliographic record
Abstract
Abstract While long non-coding RNAs (lncRNAs) are upregulated in many cancers, their role in pediatric cancers is unclear. Among pediatric cancers, Ewing sarcoma is unique in that these tumors show upregulation of a large, specific number of lncRNAs. To perform an unbiased screen of lncRNA function in Ewing sarcoma, we used a pooled CRISPRi screen to identify lncRNAs that modify cell growth. We included in the pooled library lncRNAs either highly expressed in Ewing Sarcoma or directly regulated by the driving oncogene in Ewing Sarcoma, EWS-FLI. We identified 49 lncRNAs that when inhibited by CRISPRi led to decreased growth of Ewing cell lines. Among those that scored most significantly were the small nucleolar host genes SNHG1, SNHG12, and SNHG30. To validate the screening results, we individually cloned the two most depleted sgRNAs targeting for each of these three lncRNAs into a lentiviral vector. We expressed these sgRNAs in three different dCas9-KRAB+ Ewing cell lines and used internally controlled growth assays to test whether the observed phenotypes from the screens were reproducible. We confirmed that for all three lncRNAs hits significantly affected Ewing’s growth, and their depletion leads to an increase in apoptosis. To determine how lncRNAs influence the ability of Ewing sarcoma cells to growth and survive in vivo, we performed a subcutaneous xenograft CRISPRi screen using the same pooled library used in vitro. The lncRNAs with the strongest pro-growth phenotype after knock-down were PVT1 and the uncharacterized LINC02688. Ongoing studies are directed at determining the Ewing-specific role of these lncRNAs. Overall, our studies demonstrate the value of studying lncRNA functions in vitro and in vivo, provides a valuable resource of new lncRNAs targets to further research and establishes a framework for systematic discovery of functional lncRNAs in Ewing cells. Citation Format: Marcela C. Briones Martin del Campo, Alex Lee, Max Horlbeck, Truc Dinh, Marta Roman-Moreno, Leanne Sayles, Bokyung Seong, Kimberly Stegmaier, Luke Gilbert, E. Alejandro Sweet-Cordero. Long non-coding RNAs that are required for robust cell growth in ewing Sarcoma [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 B021.
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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) | 0.004 | 0.002 |
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".