Gene-Specific Analysis of Clonal Hematopoiesis Identifies the Association Between ASXL1 and Lung Cancer Risk
Zijian Zhang1, Jing Dong1, Yun Huang2
1Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas; Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas.
Introduction:
Clonal hematopoiesis of indeterminate potential (CHIP) is a recognized risk factor for hematologic malignancies, but its contribution to different types of solid cancers remains incompletely defined.
Methods:
Here, we performed a systematic, gene-specific analysis of CHIP across 19 common solid cancer types using two large population-based cohorts, the United Kingdom Biobank and All of Us, with Cox proportional hazards models and nested case-control logistic models.
Results:
We report that the relationship between CHIP and solid tumors is highly cancer-type specific, with lung cancer exhibiting the strongest association. In lung cancer, this association is largely driven by ASXL1-mutant clones. Specifically, high variant allele fraction ASXL1 was associated with an increased risk (hazard ratio = 3.2), and the association remained robust after adjustment for age, sex, body mass index, smoking status, and genetic ancestry. Notably, ASXL1 CHIP was substantially enriched among smokers, and its association with lung cancer risk was restricted to ever-smokers, highlighting a key interaction between CHIP and environmental exposure. The enrichment of ASXL1 CHIP in lung cancer was further validated in two independent cancer-only cohorts, including Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) and The Cancer Genome Atlas (TCGA).
Conclusions:
Collectively, our findings support a model in which smoking-associated expansion of ASXL1-mutant clones is associated with an increased risk of lung cancer and suggest that gene-specific CHIP metrics may enhance risk stratification and early detection strategies.
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