MSH2-deficient murine lymphomas harbor insertion/deletion mutations in the transforming growth factor beta receptor type 2 gene and display low not high frequency microsatellite instability
Bibliographic record
Abstract
High-frequency microsatellite instability (MSI), defined as more than 20% unstable loci, is an inconsistent finding in hematologic malignancies; consequently, the significance of deficient DNA mismatch repair (MMR) to their pathogenesis has been questioned. To further investigate the relationship between MMR deficiency and genomic instability in hematologic malignancies, this study evaluated MSH2-/- murine lymphomas for insertion/deletion (ID) mutations within the transforming growth factor (TGF)-beta receptor type II (TbetaR-II) gene and MSI at 10 neutral microsatellites. The lymphomas displayed ID mutations within short mononucleotide runs of TbetaR-II at a high frequency, whereas nonmalignant tissue from corresponding animals lacked mutations. Loss of TbetaR-II transcripts and protein was seen in 6 of 7 murine lymphomas harboring acquired TbetaR-II mutations. In the analysis of paired nonmalignant and tumor DNA samples, low-frequency but not high-frequency MSI was found. Low-frequency MSI occurred in 8 of 20 lymphomas and 12 displayed microsatellite stability. MSI was even less frequent in nonmalignant tissue as only 3 of 20 samples displayed low-frequency MSI and 17 displayed stability. Evaluation of 20 single cell clones from the MSH2-/- lymphoma cell lines R25 and L15 identified high-frequency MSI in 4 and 2 clones, respectively. The remaining clones showed low-frequency MSI or stability. These findings suggest that acquired TbetaR-II mutations represent important inactivating events in tumor pathogenesis following MSH2 deficiency. Furthermore, for some hematolymphoid malignancies, the evaluation of cancer-associated genes for ID mutations may represent a more sensitive marker of MMR deficiency than evaluation of neutral microsatellites for high-frequency MSI. (Blood. 2000;95:1767-1772)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".