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Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics
Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics
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Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics
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Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics
Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics

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Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics
Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics
Journal Article

Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics

2020
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Overview
Clonal diversity is a consequence of cancer cell evolution driven by Darwinian selection. Precise characterization of clonal architecture is essential to understand the evolutionary history of tumor development and its association with treatment resistance. Here, using a single-cell DNA sequencing, we report the clonal architecture and mutational histories of 123 acute myeloid leukemia (AML) patients. The single-cell data reveals cell-level mutation co-occurrence and enables reconstruction of mutational histories characterized by linear and branching patterns of clonal evolution, with the latter including convergent evolution. Through xenotransplantion, we show leukemia initiating capabilities of individual subclones evolving in parallel. Also, by simultaneous single-cell DNA and cell surface protein analysis, we illustrate both genetic and phenotypic evolution in AML. Lastly, single-cell analysis of longitudinal samples reveals underlying evolutionary process of therapeutic resistance. Together, these data unravel clonal diversity and evolution patterns of AML, and highlight their clinical relevance in the era of precision medicine. Understanding the evolutionary trajectory of cancer samples may enable understanding resistance to treatment. Here, the authors used single cell sequencing of a cohort of acute myeloid leukemia tumours and identify features of linear and branching evolution in tumours.