Concise Review: Age-Related Clonal Hematopoiesis: Stem Cells Tempting the Devil
Bibliographic record
Abstract
The recent characterization of clonal hematopoiesis in a large segment of the aging population has raised tremendous interest and concern alike. Mutations have been documented in genes associated with hematological cancers and in non-driver candidates. These mutations are present at low frequency in the majority of individuals after middle-age, and principally affect the epigenetic modifiers DNMT3A and TET2. In 10%-40% of cases, the clone will progress to meet the diagnostic criteria for Clonal Hematopoiesis of Indeterminate Potential, which is associated with an increased risk of hematological cancer and cardiovascular mortality. Blood cell parameters appear unmodified in these individuals, but a minority of them will develop a hematologic malignancy. At this time, the factors put forward as potentially influencing the risk of cancer development are clone size, specific gene, specific mutation, and the number of mutations. Specific stress on hematopoiesis also gives rise to clonal expansion. Genotoxic exposure (such as chemotherapy), or immune attack (as in aplastic anemia) selects/provides a fitness advantage to clones with a context-specific signature. Clonal hematopoiesis offers a new opportunity to understand the biology and adaptation mechanisms of aging hematopoiesis and provides insight into the mechanisms underlying malignant transformation. Furthermore, it might shed light on common denominators of age-associated medical conditions and help devise global strategies that will impact the prevention of hematologic cancers and promote healthy aging. Stem Cells 2018;36:1287-1294.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".