Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood
Bibliographic record
Abstract
Abstract A small population of self-renewing, hematopoietic stem cells continuously reconstitutes our immune system. As we age, these cells, or their pluripotent descendants, accumulate somatic mutations; some of these mutations provide selection advantages and increase in frequency in the peripheral blood cell population. This process of positive selection, deemed age-related clonal hematopoiesis (ARCH), is associated with increased risk for cardiac disease and blood malignancies, like acute myeloid leukemia (AML). However, it remains unclear why some people with ARCH do not progress to AML, even when their blood cells harbor well-known AML driver mutations. Here, we examine whether negative selection can play a role in determining AML progression by modelling the complex interplay of positive and negative selective processes. Using a novel approach combining deep learning and population genetic models, we detect pervasive negative selection in targeted sequence data from the blood of 92 pre-AML individuals and 385 healthy controls. We find that the relative proportion of passenger to driver mutations is critical in determining if the selective advantage conferred to a cell by a known driver mutation is able to overwhelm negative selection acting on passenger mutations and allow clones harbouring disease-predisposing mutations to rise to dominance. We find that a subset of non-driver genes is enriched for mildly damaging mutations in healthy individuals fitting purifying models of evolution suggesting that mutations in these genes might confer a protective role against disease-predisposing clonal expansions. Through exploring non drivercentric models of evolution, we show how different classes of evolution act to shape hematopoietic dynamics and subsequent health outcome which may better inform disease prediction and unveil novel therapeutic targets. We anticipate that our results and modelling techniques can be broadly applied to identify both driver mutations and those mildly damaging passenger mutations, as well as help understand the early evolution of cancer in other cells and tissues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".