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November 25, 2014Proceedings of the National Academy of Sciences307 citationsOpen Access

Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics

CGCharles GawadWKWinston KohSQStephen R. Quake

Key Points

  • To resolve tumor heterogeneity and determine the chronological order of genetic alterations driving childhood acute lymphoblastic leukemia using single-cell genomics.
  • Performed targeted single-cell sequencing of single nucleotide variants (SNVs), deletions, and immunoglobulin heavy chain (IgH) sequences across 1,479 individual tumor cells.
  • Profiled bone marrow and blood samples from six patients with acute lymphoblastic leukemia (ALL) to segregate co-occurring mutations into distinct clonal populations.
  • Identified codominant subclonal populations in the majority of patients, with distinct clones within the same patient showing developmental arrest at different stages of B-cell maturation.
  • Demonstrated that structural DNA variants are predominantly acquired before SNVs, followed by clone-specific cytosine mutagenesis bursts and late-occurring KRAS mutations that do not confer clonal dominance.

Abstract

Many cancers have substantial genomic heterogeneity within a given tumor, and to fully understand that diversity requires the ability to perform single cell analysis. We performed targeted sequencing of a panel of single nucleotide variants (SNVs), deletions, and IgH sequences in 1,479 single tumor cells from six acute lymphoblastic leukemia (ALL) patients. By accurately segregating groups of cooccurring mutations into distinct clonal populations, we identified codominant clones in the majority of patients. Evaluation of intraclonal mutation patterns identified clone-specific punctuated cytosine mutagenesis events, showed that most structural variants are acquired before SNVs, determined that KRAS mutations occur late in disease development but are not sufficient for clonal dominance, and identified clones within the same patient that are arrested at varied stages in B-cell development. Taken together, these data order the sequence of genetic events that underlie childhood ALL and provide a framework for understanding the development of the disease at single-cell resolution.

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Cite This Study

Gawad et al. (2014) studied this question.

synapsesocial.com/papers/6a0ec14625c30b2cc7f9bd48https://doi.org/10.1073/pnas.1420822111
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