Single-Cell DNA Sequencing to Uncover Clonal Diversity in Acute Myeloid Leukemia
Leukemia is a complex hematological malignancy with diverse subtypes and a wide range of molecular and cytogenetic features, notably acute myeloid leukemia (AML). This study investigates the clonal heterogeneity and evolutionary dynamics of AML, with a focus on chromosomal abnormalities such as trisomy. We developed and applied novel machine learning algorithms and computational frameworks to detect large-scale copy number variations and reconstruct clonal evolution by integrating mutation and chromosomal data from single cell DNA sequencing. Utilizing both bulk and single-cell DNA sequencing data, and leveraging these computational methods, we analyze mutation patterns, copy number alterations, and their interplay at different levels. Our results reveal complex associations between genetic mutations and chromosomal aberrations, providing new insights into the molecular landscape of AML. Further phylogenetic and clonal evolution analyses illustrate the diverse evolutionary trajectories present in AML samples. Simulations of chromosomal instability further suggest that genetic alterations may be implicated in the development of aneuploidy and disease progression. Collectively, these findings contribute to a deeper understanding of AML heterogeneity and tumor evolution, offering a quantitative framework for future studies and potential therapeutic strategies.
Leukemia is a complex hematological malignancy with diverse subtypes and a wide range of molecular and cytogenetic features, notably acute myeloid leukemia (AML). This study investigates the clonal heterogeneity and evolutionary dynamics of AML, with a focus on chromosomal abnormalities such as trisomy. We developed and applied novel machine learning algorithms and computational frameworks to detect large-scale copy number variations and reconstruct clonal evolution by integrating mutation and chromosomal data from single cell DNA sequencing. Utilizing both bulk and single-cell DNA sequencing data, and leveraging these computational methods, we analyze mutation patterns, copy number alterations, and their interplay at different levels. Our results reveal complex associations between genetic mutations and chromosomal aberrations, providing new insights into the molecular landscape of AML. Further phylogenetic and clonal evolution analyses illustrate the diverse evolutionary trajectories present in AML samples. Simulations of chromosomal instability further suggest that genetic alterations may be implicated in the development of aneuploidy and disease progression. Collectively, these findings contribute to a deeper understanding of AML heterogeneity and tumor evolution, offering a quantitative framework for future studies and potential therapeutic strategies.