Germline genetic variation impacts clonal hematopoiesis landscape and progression to malignancy
Bibliographic record
Abstract
With age, clonal expansions occur pervasively across normal tissues yet only in rare instances lead to cancer, despite being driven by well-established cancer drivers. Characterization of the factors that influence clonal progression is needed to inform interventional approaches. Germline genetic variation influences cancer risk and shapes tumor mutational profile, but its influence on the mutational landscape of normal tissues is not well known. Here we studied the impact of germline genetic variation on clonal hematopoiesis (CH) in 731,835 individuals. We identified 22 new CH-predisposition genes, most of which predispose to CH driven by specific mutational events. CH-predisposition genes contribute to unique somatic landscapes, reflecting the influence of germline genetic backdrop on gene-specific CH fitness. Correspondingly, somatic–germline interactions influence the risk of CH progression to hematologic malignancies. These results demonstrate that germline genetic variation influences somatic evolution in the blood, findings that likely extend to other tissues. The relationship between pathogenic germline variation, clonal hematopoiesis (CH) and risk of hematologic malignancy is explored in 731,835 individuals across 6 cohorts. Carriers of variants in certain genes show distinct patterns of CH and increased risk of CH progression to malignancy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".