COMP-06. GLIOBLASTOMA DEVELOPMENT MIRRORS THE DEVELOPING BRAIN
Bibliographic record
Abstract
Intratumoral and interpatient heterogeneity are characteristics of glioblastoma and constitute important challenges in overcoming treatment resistance and developing new, more effective therapies. Using single-cell RNA sequencing, we characterized 60 933 cells from 4 developing fetal brains and 8 glioblastomas. By using fetal brain development as a road map, we show a tri-lineage (astrocytic, oligodendrocytic, and neuronal) hierarchical organization in all glioblastomas. In each patient, a population of progenitor cancer cells was found at the apex of this hierarchy. These cells were enriched in our patient-derived glioma stem cell samples, and, like progenitors in the developing brain, were the main dividing cell population within the cancer. Using expression signatures obtained from single-cell RNA-sequencing, we isolated progenitor cancer cells and compared them to other glioblastoma cell types. We showed the progenitors are the most resistant to chemotherapy and the most tumorigenic in mouse xenograft models. This newly found conserved developmental organization points to the cell of origin and suggests new therapeutic approaches.
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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) | 0.035 | 0.019 |
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".