medRxivGenetic & Genomic MedicinePreprint
Mutation-specific impairment of TET2 and DNMT3A enzymatic activity predicts clonal hematopoiesis disease risk
Yash PershadiD, Kun ZhaoiD, Joseph C. Van AmburgiD, Robert W. CortyiD, Alyssa ParkeriD, Alexander James SilveriD, Yara. F. AlmadaniiD, Ashwin KishtagariiD, Emily C. HodgesiD, Michael Robert SavonaiD, Jonathan Brett HeimlichiD, Alexander G. BickiD
Abstract
Clonal hematopoiesis of indeterminate potential (CHIP) driven by somatic mutations in TET2 and DNMT3A is present in >10% of adults over 60 and confers substantial risk for hematologic malignancy and cardiovascular disease, yet the majority of patients with CHIP do not progress to disease. Analyzing 1,020,538 individuals across three biobanks (UK Biobank, All of Us, BioVU), we show that a discrete subset of enzymatically disruptive mutations -- TET2 loss-of-function variants and the DNMT3A R882 hotspot -- account for the majority of clinical risk in these genes and exhibit the strongest clonal fitness advantage. Because DNMT3A and TET2 encode enzymes that modulate DNA methylation, we reasoned that peripheral blood methylation patterns should reflect the functional impact of individual mutations, enabling a direct readout of enzymatic dysfunction in CHIP patients. We developed and validated methylation-based activity scores for TET2 and DNMT3A as patient specific biomarkers that quantify enzymatic activity. These scores capture functional heterogeneity across mutation subtypes, predict disease risk comparably to clinical risk scores such as the Clonal Hematopoiesis Risk Score and the AHA PREVENT cardiovascular risk model. Integrating the activity score with the clinical models substantially improves prediction of incident cytopenia, myeloid neoplasm, and major adverse cardiovascular events. These findings establish that TET2 and DNMT3A CHIP pathogenicity is proportional to the degree of enzymatic disruption conferred by specific variants, and nominate methylation-based activity scores as a functional biomarker for individualized CHIP risk stratification and monitoring therapeutic response.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- research focus: variant effect prediction
- research focus: benchmarking
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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