Abstract 3647: Single cell RNA sequencing reveals mitogenic and progenitor gene programs inBRAF-rearranged pilocytic astrocytomas
Bibliographic record
Abstract
Abstract Single-cell RNA sequencing (scRNAseq) has been performed across a range of intermediate to high-grade gliomas that each harbor multiple driver mutations. Pilocytic astrocytoma (PA), the most common childhood brain tumor, is a low-grade glioma. PAs frequently harbor oncogenic KIAA1549-BRAF fusions, and exhibit low rates of other driver mutations. While PAs exhibit a favorable prognosis compared to the higher-grade gliomas, treatment morbidity and tumor recurrence can represent major challenges for some PA patients. We performed scRNAseq of both tumor and non-tumor cells in newly-diagnosed PAs that contained KIAA1549-BRAF rearrangements. To confidently distinguish tumor cells from nontumor cells, we sorted cells by glial progenitor marker A2B5 status and profiled KIAA1549-BRAF fusion status of cells using a sensitive quantitative PCR-based assay. Results were validated using RNA in situ hybridization. When compared to higher-grade gliomas, a higher proportion of the PA tumor cells exhibited a differentiated, astrocyte-like phenotype. A smaller proportion of cells exhibited a progenitor-like phenotype with evidence of proliferation. These progenitor-like tumor cells expressed a mitogen-activated protein kinase (MAPK) program that was absent from higher-grade gliomas. Similar patterns of expression of genes associated with the astrocyte-like and MAPK gene programs were also seen in formalin fixed, paraffin embedded PA tissues using RNA in situ hybridization. Immune cells, especially microglia, comprised almost half of all cells in the PAs and accounted for differences in bulk expression profiles between tumor locations and subtypes. These single cell transcriptional data reveal a transcriptional developmental hierarchy in a pediatric low grade glioma that is skewed towards more mature brain cells compared to higher-grade gliomas. The results indicate that future analyses of bulk PA tissues should attempt to account for considerable infiltration by nontumor cells. Finally, the finding that a MAPK gene program is not uniformly expressed in PA tumor cells has implications for ongoing clinical investigations of therapies directed at the MAPK pathway for the treatment of PA. Citation Format: Zachary J. Reitman, Brenton Paolella, Guillaume Bergthold, Kristine Pelton, Robert Jones, Sarah Becker, Claire E. Sinai, Haley Malkin, Ying Huang, Leslie Grimmett, Zachary T. Herbert, Yu Sun, Jessica Weatherbee, John Alberta, John Daley, Orit Rozenblatt-Rosen, Rosalind Segal, Daphne Haas-Kogan, Mariella G. Filbin, Mario L. Suva, Aviv Regev, Charles Stiles, Mark W. Kieran, Liliana Goumnerova, Keith L. Ligon, Alex K. Shalek, Pratiti Bandopadhayay, Rameen Beroukhim. Single cell RNA sequencing reveals mitogenic and progenitor gene programs inBRAF-rearranged pilocytic astrocytomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3647.
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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 | 0.001 |
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".