Abstract 2228: A multi-omic perspective of how selection shapes blood cancer risk phenotypes in aging populations
Bibliographic record
Abstract
Abstract The genetic architecture of blood is highly complex with germline polymorphisms, somatic point mutations, and larger chromosomal alterations playing a role in shaping the fitness of the immune cells. Many advances have been made in understanding how the age associated acquisition of point mutations and somatic structural variants (SSVs) in blood, termed Age-Related Clonal Hematopoiesis (ARCH), predispose individuals to hematological cancer or cardiovascular disease. Yet, ARCH is commonly observed in healthy individuals and our ability to predict who is at risk of progressing to disease remains limited. A previous study which integrated deep learning and population genetics methods to evaluate the complex interplay of selection on point mutations in deeply sequenced blood samples highlighted the role that negative selection plays in prevention progression to hematological cancer. Here, we interrogate the mutational and selective pressures within blood to better understand how the full spectrum of somatic changes within an individual impact clonal fitness and disease outcomes. We evaluate how selection shapes the prevalence of large somatic structural variation in blood sampled from 15,910 individuals across over 20 different genetic ancestries, including the Thousand Genomes Study and the Canadian Partnership for Tomorrow’s Health study. Using dense genotyping arrays, we capture SSVs among 20 populations and find that ARCH attributed to somatic structural variation is three times as high as previously reported with up to 14% of individuals harbouring at least one large SSV in their blood. We estimate the rate at which SSVs accrue in blood cells and find that selection impacts the size and frequency of SSVs within individual blood populations. To determine the functional impact of clonal mutations on molecular phenotypes, we investigate the relationship between structural variation and the transcriptome. We show that gains, losses and copy number neutral variants impact gene expression distinctly, with stabilizing selection shaping the penetrance of copy number alterations in gene expression. Our work shows how different classes of selection shape clonal dynamics in blood thus enabling us to better understand why certain individuals are at a high risk of malignancy. Citation Format: Kimberly Skead, David Soave, Marie-Julie Fave, Vanessa Bruat, Quaid Morris, Philip Awadalla. A multi-omic perspective of how selection shapes blood cancer risk phenotypes in aging populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2228.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".