Supporting data for " Decoding clonal heterogeneity through single-cell targeted DNA sequencing in acute myeloid leukemia"

Leukemia is a complex hematological malignancy which has diverse subtypes with distinct molecular and cytogenetic features, notably acute myeloid leukemia (AML). This study investigates the clonal heterogeneity and evolutionary dynamics of AML, with an emphasis on chromosomal abnormalities such as trisomy. We developed and applied novel machine learning algorithms and computational framework to accurately detect large-scale copy number variations and construct precise clonal evolution trees by integrating mutation and chromosomal abnormalities data in single cell DNA sequencing. Utilizing bulk and single-cell DNA sequencing, combined with these innovative methods, we analyze mutation patterns, copy number variations, and their interactions at both bulk and single cell levels. Our results reveal significant associations between mutations particularly in genes like ASXL1 and trisomy, suggesting a role for genetic mutations in promoting chromosomal instability. Phylogenetic reconstructions and clonal evolution analyses demonstrate that specific mutational combinations often precede trisomy events, supporting their contributory role in chromosomal abnormalities. Furthermore, chromosomal instability simulations incorporating mutation driven missegregation rates highlight how genetic alterations can facilitate aneuploidy and disease progression. These findings deepen our understanding of AML heterogeneity, underscore the impact of molecular abnormalities on chromosomal stability, and provide a new quantitative framework for tumor evolution, with important implications for targeted therapeutic strategies.