In Vivo Generation of Neural Tumors from Neoplastic Pluripotent Stem Cells Models Early Human Pediatric Brain Tumor Formation
Bibliographic record
Abstract
Recent studies have identified gene signatures in malignant tumors that are associated with human embryonic stem cells, suggesting a molecular relationship between aggressive cancers and pluripotency. Here, we characterize neural precursors (NPs) derived from transformed human embryonic stem cells (N-t-hESCs) that exhibit neoplastic features of human brain tumors. NPs derived from t-hESCs have enhanced cell proliferation and an inability to mature toward the astrocytic lineage, compared with progeny derived from normal human embryonic stem cells (N-hESCs) independent of adherent or neurosphere outgrowth. Intracranial transplantation of NPs derived from N-t-hESCs and N-hESCs into NOD SCID mice revealed development of neuroectoderm tumors exclusively from the N-t-hESCs NPs and not from normal N-hESCs. These tumors infiltrated the ventricles and the cerebellum of recipient mice and displayed morphological, phenotypic, and molecular features associated with classic medulloblastoma including retention of a pluripotent signature. Importantly, N-t-hESCs did not exhibit cytogenetic changes associated with medulloblastoma, suggesting that aberrant cellular and molecular properties precede the acquisition of karyotypic changes thus underscoring the value of this model system of human medulloblastoma. Our study demonstrates that NPs from a starting population of neoplastic human pluripotent parent cells possess brain tumor-initiating cell capacity, thereby providing a model system to investigate initiation and progression of primitive human neural cancers that are difficult to assess using somatic sources.
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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 | 0.000 |
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 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".