TMOD-11. HUMAN STEM CELL BASED MODEL OF MEDULLOBLASTOMA
Bibliographic record
Abstract
Medulloblastoma (MB) is the most common malignant pediatric brain tumor and consists of four subgroups: WNT, SHH, Group 3 and Group 4. Although drivers of Group 3 and 4 remain unknown, recurrent subchromosomal gains and losses frequently occurs. Current models to study MB include human cell lines, patient derived xenografts, and genetically engineered mouse models (GEMM). However, transcriptional profiles of human cell lines cluster separately from patient samples, and patient tumors are rare and difficult to culture. While GEMM reproducibly generate tumors resembling MB, modeling human patient-relevant chromosomal aberrations in mice would be difficult as mouse and human chromosomes do not align. To address these issues, we developed a human stem cell (hSC)-based model of MB. For proof of principle, we first generated iPSC from patients with Gorlin syndrome which is characterized by germline mutation in the SHH antagonist PTCH1 and predisposition to MB. The iPSC were differentiated to neuroepithelial stem (NES) cells (cell of origin of MB) and implanted orthotopically in mice. Tumors developed at long latency and histology resembled MB. Importantly, our model provides new functional insight of genes mutated in patient samples. For instance, CRISPR/Cas9 knockout of GSE1 (co-mutated with PTCH1 in SHH MB) in Gorlin NES cells accelerated tumor growth in vivo. Furthermore, MYCN, whose amplification occurs in SHH and Group 4, is sufficient to transform normal human NES cells. While GEMM of MYCN-driven MB align to Group 3, our hSC-based model more appropriately clustered to SHH. Using CRISPR/Cas9, we generated Group 4-relevant chromosomal deletions in human NES cells to evaluate its role in MB tumorigenesis. Thus, our hSC-based model of MB represents human patient tumors better than GEMM and can routinely evaluate candidate drivers of MB, including subchromosomal abnormalities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".