Inferred developmental origins of brain tumors from single-cell RNA-sequencing data
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Bibliographic record
Abstract
Abstract Background The reactivation of neurodevelopmental programs in cancer highlights parallel biological processes that occur in both normal development and brain tumors. Achieving a deeper understanding of how dysregulated developmental factors play a role in the progression of brain tumors is therefore crucial for identifying potential targets for therapeutic interventions. Single-cell RNA-sequencing (scRNA-Seq) provides an opportunity to understand how developmental programs are dysregulated and reinitiated in brain tumors at single-cell resolution. The aim of this study is to identify the developmental origins of brain tumors using scRNA-Seq data. Methods Here, we introduce COORS (Cell Of ORigin like CellS), a computational tool trained on developmental human brain single-cell datasets that annotates “developmental-like” cell states in brain tumors. COORS leverages cell type-specific multilayer perceptron models and incorporates a developmental cell type tree that reflects hierarchical relationships and models cell type probabilities. Results Applying COORS to various brain cancer datasets, including medulloblastoma (MB), glioma, and diffuse midline glioma (DMG), we identified developmental-like cells that represent putative cells of origin in these tumors. Our method provides both cell of origin classification and cell age regression, offering insights into the developmental cell types of tumor subgroups. COORS identified outer radial glia developmental cells within IDHWT glioma cells whereas oligodendrocyte precursor cells (OPCs) and neuronal-like cells in IDHMut. Interestingly, IDHMut subgroup cells that map to OPC show bimodal distributions that are both early and late weeks in development. Furthermore, COORS offers a valuable resource by providing novel markers linked to developmental states within MB, glioma, and DMG tumor subgroups. Conclusions Our work adds to our cumulative understanding of brain tumor heterogeneity and helps pave the way for tailored treatment strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it