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Clonal hematopoiesis (CH),a prevalent and premalignant state in the elderly,has been detected in young individuals under selective pressures such as hematopoietic cell transplantation (HCT). However,the origin of CH and mutational processes underlying CH driver mutations in young blood systems remain unclear. Here,we used genome‐wide somatic mutation profiles to retrospectively trace the origin of DNMT3A‐mutant CH in three individuals,14–41 years after childhood HCT. Both the rate and spectrum of somatic mutations in individuals with posttransplant CH were consistent with normal age‐associated mutagenesis. Phylogenetic analysis revealed that DNMT3A‐mutant HSPCs were present in the donor before 6.8 years of age,including during fetal development,despite being undetectable with a limit of detection of variant allele frequency of 0.001 at the time of transplantation. These findings were validated by comparing the observed mutations to expected age‐dependent mutational signatures. Our results reveal that undetectable DNMT3A‐mutant clones in young donors can expand into significant CH clones within decades upon transplantation. The rapid expansion of these clones in this context indicates that specific environmental pressures,rather than solely mutation acquisition,drive the development of CH. View Publication

Background: Lung cancer is a leading cause of cancer-related mortality worldwide,with heterogeneity and acquired resistance posing major challenges to treatment. Advances in next-generation sequencing (NGS) have enabled comprehensive genomic profiling,yet there remains a need for robust patient-derived models to study tumor biology and inform precision medicine. This study aims to establish and characterize patient-derived lung cancer organoids (LCOs) using whole-genome sequencing (WGS) to explore their genomic landscape and therapeutic potential. Methods: We established a panel of LCOs from resected tumors and malignant pleural effusions (MPEs) of 14 non-small cell lung cancer (NSCLC) patients. Organoids were authenticated and subjected to WGS to profile somatic single nucleotide variants (SNVs),insertions/deletions (InDels),copy number variations (CNVs),structural variants (SVs),and microsatellite instability (MSI). Bioinformatic analyses were performed to annotate mutations,assess tumor mutation burden (TMB),and explore mutational signatures. Furthermore,deep learning-based drug response prediction and in vitro drug sensitivity assays were conducted to evaluate therapeutic potentials in the established LCOs. Results: In the established LCOs,WGS revealed recurrent mutations in TP53,TTN,MUC16,and FLG,with approximately 80% of somatic variants located in non-coding regions,highlighting the potential role of regulatory elements in lung cancer pathogenesis. Early and locally advanced-stage tumor-derived LCOs exhibited higher TMB and MSI compared to those from advanced-stage disease,suggesting greater clonal diversity prior to therapeutic intervention. Drug screening demonstrated the feasibility of using genomic data for drug prediction,but requires more advanced models to fully utilize the WGS data. Conclusions: Our comprehensive genomic characterization of patient-derived LCOs provides valuable insights into the mutational landscape and evolutionary dynamics of lung cancer. These well-annotated organoid models serve as a powerful resource for investigating tumor biology and developing genomically informed therapeutic strategies. View Publication