HGG-36. Elucidating the role of long non-coding RNAs in pediatric high grade gliomas
Bibliographic record
Abstract
Abstract BACKGROUND: Genomic and transcriptomic studies have elucidated new insights into the landscape of diffuse intrinsic pontine glioma (DIPG). However, the role of long non-coding RNAs (lncRNAs) has not been explored at depth in these tumors, and there have not been studies focused on how lncRNAs interact with the K27M histone mutation. In a recent analysis of nearly 200 DIPGs and pediatric high-grade gliomas (pHGG), we previously detected a novel, recurring structural variant in the lncRNA CCDC26. This rearrangement occurs in nearly 10% of all DIPGs, and we have furthermore identified alterations in more than 100 lncRNAs in DIPG. METHODS: To identify lncRNAs required for proliferation of patient-derived DIPG cancer cells, we designed two custom genome-scale lncRNA libraries. We generated a genome-scale lncRNA CRISPR-Cas9 knockout pooled library, consisting of 45,766 single guide RNAs (sgRNAs). Additionally, we generated a genome-wide CRISPR interference pooled library consisting of 45,608 sgRNAs, targeting lncRNA transcription start sites (TSS). RESULTS: We utilized in vitro histone-mutant pHGG models as well as edited clones of these models with the K27M mutant corrected in order to compare lncRNA dependencies in these two contexts. We have successfully performed genome-scale CRISPR-Cas9 knockout and CRISPR interference screens targeting lncRNAs in these cell lines, revealing lncRNA dependencies. Candidate dependencies in our CRISPR-Cas9 knockout screen include LOC100507412, LOC105379524, and LINC02193. CONCLUSION: Genome-wide lncRNA CRISPR knock-out and CRISPR interference screens are a novel approach for the unbiased identification of lncRNAs that are required for pediatric high-grade glioma proliferation. Further validation of specific lncRNAs is required, and these lncRNA dependencies represent potential novel therapeutic targets.
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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".