Abstract A014: Understanding the transcriptional heterogeneity of pediatric low-grade gliomas and its implication for tumor pathophysiology
Bibliographic record
Abstract
Abstract Pediatric low-grade gliomas (pLGGs) are the most frequent brain tumors in children and comprise a heterogeneous group of tumors with different locations, histologic subtypes, ages at presentation, and clinical behavior. Tumors frequently respond to treatment with chemotherapy or radiation therapy, but they can regrow after a period of quiescence, requiring further therapy. Thus, a deeper understanding of the molecular processes involved in these tumors is required to develop therapeutic strategies that are effective against their disease mechanisms. To better understand the cellular behaviors of this heterogenous group of tumors, we have employed single-cell and single-nuclei RNA sequencing technologies to analyze a large-scale dataset (>250,000 cells) of pLGGs. Analysis of this data identified a heterogenous population of cell types and cell states, detecting mature and progenitor-like astrocytes and oligodendrocytes, as well as cells exhibiting senescence or cycling programs. Moreover, we identify a significant immune infiltrate, comprised primarily of microglia. In addition to heterogeneity within pLGG tumors, heterogeneity between LGG subtypes represents another layer that stratifies pLGG biology. We performed a compositional analysis of the cell types present in these tumors and compared transcription signatures and gene expression programs across shared cellular populations of histologically and genetically distinct pLGGs. Finally, we optimized our integration and batch correction analyses by using external 293T cells as spike in controls during our single-cell and single-nuclei data generation steps to determine the most suitable method for batch-effect removal. Our analysis of human pLGGs at the single-cell and single-nuclei resolution provides critical insight into the heterogenous biological activities that constitute these tumors. Citation Format: Michelle Boisvert, Ashwyn Perera, Alexandra L. Condurat, John Jeang, Jessica W. Tsai, Dana Novikov, Kevin Zhou, Madison Chacon, Jeromy DiGiacomo, Rushil Kumbhani, Dayle Wang, Michael D. Taylor, Jordan R. Hansford, Louise Ludlow, Nada Jabado, Keith L. Ligon, Rameen Beroukhim, Pratiti Bandopadhayay, David T.W. Jones. Understanding the transcriptional heterogeneity of pediatric low-grade gliomas and its implication for tumor pathophysiology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr A014.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".