TMOD-13GENETICALLY ENGINEERED MOUSE MODELS OF CHOROID PLEXUS TUMORS
Bibliographic record
Abstract
Choroid plexus tumors are the most common brain tumors in children under one year of age, accounting for up to 20% of brain tumors in that age group. These tumors exhibit a range of malignancies, from relatively benign choroid plexus papillomas (CPPs) to extremely aggressive choroid plexus carcinomas (CPCs), which despite intensive therapy, have a dismal prognosis. More effective treatments for these tumors depend on animal models that can be used to study tumor biology. We have created animal models of choroid plexus tumors by crossing mice carrying Cre-inducible alleles of Myc (LSL-MycT58A) and Cre-deletable alleles of p53 (p53flox) to animals that express Cre recombinase in cerebellar neural progenitor cells (Atoh1-Cre). The progeny of these crosses develop choroid plexus tumors with high penetrance: activation of Myc alone results in CPPs, and concomitant deletion of p53 accelerates tumor growth and malignancy, culminating in formation of CPCs. Histological and gene expression analyses indicate that our mouse CPPs and CPCs share common features with their human counterparts, such as large chromosomal imbalances, altered expression of cell cycle regulators. To utilize these mouse models for preclinical studies, we performed a high throughput screening with more than 7800 biologically active compounds. We have identified several compounds that effectively inhibit mouse and human choroid plexus tumor growth with minimal toxicity to postmitotic neurons. These studies provide important insight into the mechanisms of choroid plexus tumorigenesis, and yield valuable animal models that can be used to identify novel therapies for patients with aggressive choroid plexus tumors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".