Abstract IA29: Developmental and oncogenic programs in pediatric brain tumors dissected by single-cell RNA-sequencing
Bibliographic record
Abstract
Abstract During development, the multiple cell types of human brain arise from pluripotent stem cells via progressively more committed progenitor cells. Emerging data suggest that pediatric brain tumors arise from progenitors that have become developmentally stalled. In preliminary studies, our lab used single-cell RNA-seq to display the expression profiles of individual cells within pediatric midline and hemispheric high-grade gliomas. Our data show that these tumors are composed of (i) replicating cells that resemble glial and neural progenitors and (ii) maturing cells resembling astrocytes, oligodendrocytes, or mesenchymal cells. In this presentation I will address how single-cell RNA-sequencing has revolutionized our understanding of the developmental hierarchy and oncogenic programs in pediatric brain tumors. Our recent findings pointing towards potential cells of origin of midline and hemispheric pediatric high-grade gliomas as well as plasticity of different cancer cell states will be discussed. Citation Format: Mariella G. Filbin. Developmental and oncogenic programs in pediatric brain tumors dissected by single-cell RNA-sequencing [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 IA29.
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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 | 0.001 | 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) | 0.002 | 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".