Age-Associated TET2 Mutations: Common Drivers of Myeloid Dysfunction, Cancer and Cardiovascular Disease
Bibliographic record
Abstract
Acquired, inactivating mutations in Tet methylcytosine dioxygenase 2 (TET2) are detected in peripheral blood cells of a remarkable 5%–10% of adults greater than 65 years of age. They impart a hematopoietic stem cell advantage and resultant clonal hematopoiesis of indeterminate potential (CHIP) with skewed myelomonocytic differentiation. CHIP is associated with an overall increased risk of transformation to a hematological malignancy, especially myeloproliferative and myelodysplastic neoplasms (MPN, MDS) and acute myeloid leukemia (AML), of approximately 0.5% to 1% per year. However, it is becoming increasingly possible to identify individuals at greatest risk, based on CHIP mutational characteristics. CHIP, and particularly TET2-mutant CHIP, is also a novel, significant risk factor for cardiovascular diseases, related in part to hyper-inflammatory, progeny macrophages carrying TET2 mutations. Therefore, somatic TET2 mutations contribute to myeloid expansion and innate immune dysregulation with age and contribute to prevalent diseases in the developed world—cancer and cardiovascular disease. Herein, we describe the impact of detecting TET2 mutations in the clinical setting. We also present the rationale and promise for targeting TET2-mutant and other CHIP clones, and their inflammatory environment, as potential means of lessening risk of myeloid cancer development and dampening CHIP-comorbid inflammatory diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".