Abstract B14: Pinpointing the origins of pediatric brain tumors using single-cell transcriptomic analysis
Bibliographic record
Abstract
Abstract Childhood tumors of the central nervous system are deadly diseases, and for many tumor types outcome has not improved over the last three decades. Current evidence supports a model in which genetic alterations drive changes in neurodevelopmental gene expression programs, leading to oncogenesis postnatally. We hypothesize that there are specific cellular states during normal brain development that are vulnerable targets for oncogenic mutations. However, comprehensive developmental data for many brain regions relevant to pediatric brain tumors (PBT) are lacking. Here, we leverage single-cell RNA-sequencing to generate a reference transcriptome atlas of the developing brain in two regions where PBT commonly arise, the forebrain and the pons. We profile >65,000 cells from healthy embryonic and neonatal mouse and human specimens, and characterize cellular diversity, dynamics related to differentiation, and transcription factor regulatory activity. We use a novel strategy to project bulk RNA-seq profiles for a cohort of 200 PBT onto these cell types. We show that this projection stratifies tumors by type, and for several PBT subtypes, we identify their closest transcriptional match among the cell populations of the normal developing brain. WNT-subtype medulloblastoma matches the lower rhombic-lip derived mossy fiber neuron lineage. In embryonal tumors with multilayered rosettes, we identify prenatal neurogenic radial glial cells as candidate cells of origin, while Group 2a/b atypical teratoid/rhabdoid tumors originate outside the neuro-ectoderm. Finally, single-cell profiling of patient tumor samples shows that these tumors mimic the transcriptional programs of their corresponding normal lineages and contain a mixture of cells with varying degrees of differentiation. We identify impaired differentiation of specific neural progenitors as a common oncogenic mechanism underlying PBT, and further demonstrate that this differentiation blockade may be reversible. Our findings thus provide a rational framework to guide future modeling and therapeutics. More broadly, we assemble a high-resolution developmental dataset, a valuable resource for the study of neuroscience and many brain pathologies. Citation Format: Selin Jessa, Alexis Blanchet-Cohen, Brian Krug, Maria C. Vladoiu, Marie Coutelier, Damien Faury, Brice Poreau, Nicolas De Jay, W. Todd Farmer, Yixing Hu, Steven Hébert, Jean Monlong, Keith K. Murai, Melissa McConechy, Guillaume Bourque, Jiannis Ragoussis, Livia Garzia, Michael D. Taylor, Nada Jabado, Claudia L. Kleinman. Pinpointing the origins of pediatric brain tumors using single-cell transcriptomic analysis [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 B14.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".