Glioblastoma scRNA-seq shows treatment-induced, immune-dependent increase in mesenchymal cancer cells and structural variants in distal neural stem cells
Bibliographic record
Abstract
BACKGROUND: Glioblastoma is a treatment-resistant brain cancer. Its hierarchical cellular nature and its tumor microenvironment (TME) before, during, and after treatments remain unresolved. METHODS: Here, we used single-cell RNA sequencing to analyze new and recurrent glioblastoma and the nearby subventricular zone (SVZ). RESULTS: We found 4 glioblastoma neural lineages are present in new and recurrent glioblastoma with an enrichment of the cancer mesenchymal lineage, immune cells, and reactive astrocytes in early recurrences. Cancer lineages were hierarchically organized around cycling oligodendrocytic and astrocytic progenitors that are transcriptomically similar but distinct to SVZ neural stem cells (NSCs). Furthermore, NSCs from the SVZ of patients with glioblastoma harbored glioblastoma chromosomal anomalies. Lastly, mesenchymal cancer cells and TME reactive astrocytes shared similar gene signatures which were induced by radiotherapy in a myeloid-dependent fashion in vivo. CONCLUSION: These data reveal the dynamic, immune-dependent nature of glioblastoma's response to treatments and identify distant NSCs as likely cells of origin.
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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) | 0.002 | 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".