<i>Idh1</i> mutations contribute to the development of T-cell malignancies in genetically engineered mice
Bibliographic record
Abstract
Gain-of-function mutations in isocitrate dehydrogenase 1 (IDH1) are key drivers of hematopoietic malignancies. Although these mutations are most commonly associated with myeloid diseases, they also occur in malignancies of the T-cell lineage. To investigate their role in these diseases and provide tractable disease models for further investigation, we analyzed the T-cell compartment in a conditional knock-in (KI) mouse model of mutant Idh1. We observed the development of a spontaneous T-cell acute lymphoblastic leukemia (T-ALL) in these animals. The disease was transplantable and maintained expression of mutant IDH1. Whole-exome sequencing revealed the presence of a spontaneous activating mutation in Notch1, one of the most common mutations in human T-ALL, suggesting Idh1 mutations may have the capacity to cooperate with Notch1 to drive T-ALL. To further investigate the Idh1 mutation as an oncogenic driver in the T-cell lineage, we crossed Idh1-KI mice with conditional Trp53 null mice, a well-characterized model of T-cell malignancy, and found that T-cell lymphomagenesis was accelerated in mice bearing both mutations. Because both IDH1 and p53 are known to affect cellular metabolism, we compared the requirements for glucose and glutamine in cells derived from these tumors and found that cells bearing the Idh1 mutation have an increased dependence on both glucose and glutamine. These data suggest that mutant IDH1 contributes to malignancy in the T-cell lineage and may alter the metabolic profile of malignant T cells.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".