A Lack of DNA Mismatch Repair on an Athymic Murine Background Predisposes to Hematologic Malignancy
Bibliographic record
Abstract
Inheritance of a germline mutation in one of the DNA mismatch repair genes predisposes human individuals to hereditary nonpolyposis colorectal cancer, characterized by development of tumors predominantly in the colon, endometrium, and gastrointestinal tract. Mice heterozygous for a mismatch repair-null mutation generally do not have an increased risk of neoplasia. However, mice constitutively lacking mismatch repair are prone to tumor development from an early age, particularly thymic lymphomas. Mismatch repair-deficient mice crossed to Apc(+/-) mice develop an increased spontaneous intestinal tumor incidence, demonstrating that the tumor spectrum can be genetically influenced. Here, we bred Msh2- and Msh6-deficient mice to athymic nude mice, hypothesizing that a broader tumor spectrum may be observed if mice are able to survive longer without succumbing to thymic lymphomas. However, Msh2(-/-);Foxn1(nu/nu) and Msh6(-/-);Foxn1(nu/nu) mice developed primarily early-onset lymphoblastic lymphomas. Using B-cell-specific markers, we found these tumors to be predominately B-cell in origin. The development of hematologic malignancy in the mouse, even in the absence of a thymus, parallels the development of B- and T-cell lymphoma and leukemia in the few rare mismatch repair-null human patients that have been identified. The persistent development of hematologic malignancy both in the mouse and in human patients deficient in mismatch repair leads us to implicate mismatch repair as an important repair mechanism in normal B- and T-cell development. Thus, mismatch repair-deficient mice may prove to be a good model to study human hematologic malignancy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".