Most spontaneous tumors in a mouse model of Li-Fraumeni syndrome do not have a mutator phenotype
Bibliographic record
Abstract
Mutations are the substrate of cancer. Yet, little is known about the degree and nature of mutations in tumors because measurement of mutation load in tumors and normal tissues was generally not possible until the advent of transgenic mouse mutation detection systems. Herein, we present the first analysis of mutation frequency and pattern in thymic tumors from a mouse model of Li-Fraumeni syndrome (p53+/- murine model) using the Big Blue assay with sequencing of all mutants. We also make the first characterization of mutation frequency and pattern in p53-deficient extra-thymic cancers. The data more than triple the literature on all non-mismatch repair deficient tumors for which mutations are identified by sequence analysis, allowing mutation frequency and pattern to be determined. Most tumors had a normal mutation frequency and a normal mutation pattern. Five tumors showed modest increases in mutation frequency (2.3-fold or less). Alterations in mutation patterns were uncommon, tumor-specific and not necessarily associated with increases in mutation frequency. Given the data from two spontaneous tumors (normal mutation frequency with an abnormal pattern in a p53-/- mouse and low mutation frequency in a p53+/+ control mouse), we hypothesize that tumors sometimes can carry a low mutation load. The study was not without certain caveats: mutation load could not be compared between tumor and normal tissue from the same animal; sample sizes for extra-thymic tumor types were small, and only point mutations and deletions, insertions and indels up to 2 kb were detected. However, the data clearly show key differences in tumors from p53+/- mice compared with mismatch repair deficient tumors; a lack of dramatic increase in mutation frequency and absence of a signature of mutation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".