A patient tumor-derived orthotopic xenograft mouse model replicating the group 3 supratentorial primitive neuroectodermal tumor in children
Bibliographic record
Abstract
BACKGROUND: Supratentorial primitive neuroectodermal tumor (sPNET) is a malignant brain tumor with poor prognosis. New model systems that replicate sPNET's molecular subtype(s) and maintain cancer stem cell (CSC) pool are needed. METHODS: A fresh surgical specimen of a pediatric sPNET was directly injected into the right cerebrum of Rag2/SCID mice. The xenograft tumors were serially sub-transplanted in mouse brains, characterized histopathologically, and subclassified into molecular subtype through qRT-PCR and immunohistochemical analysis. CSCs were identified through flow cytometric profiling of putative CSC markers (CD133, CD15, CD24, CD44, and CD117), functional examination of neurosphere forming efficiency in vitro, and tumor formation capacity in vivo. To establish a neurosphere line, neurospheres were propagated in serum-free medium. RESULTS: Formation of intracerebral xenograft tumors was confirmed in 4 of the 5 mice injected with the patient tumor. These xenograft tumors were sub-transplanted in vivo 5 times. They replicated the histopathological features of the original patient tumor and expressed the molecular markers (TWIST1 and FOXJ1) of group 3 sPNET. CD133(+) and CD15(+) cells were found to have strong neurosphere-forming efficiency in vitro and potent tumor-forming capacity (with as few as 100 cells) in vivo. A neurosphere line BXD-2664PNET-NS was established that preserved stem cell features and expressed group 3 markers. CONCLUSION: We have established a group 3 sPNET xenograft mouse model (IC-2664PNET) with matching neurosphere line (BXD-2664PNET-NS) and identified CD133(+) and CD15(+) cells as the major CSC subpopulations. This novel model system should facilitate biological studies and preclinical drug screenings for childhood sPNET.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".