Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia

  1. Catherine J. Wu1,2,4,9
  1. 1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA;
  2. 2Harvard Medical School, Boston, Massachusetts 02115, USA;
  3. 3Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA;
  4. 4Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;
  5. 5Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee DD1 4HN, United Kingdom;
  6. 6Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA;
  7. 7Suzhou Precision Medicine Scientific Ltd, Suzhou, China, 215006;
  8. 8Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA;
  9. 9Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA;
  10. 10Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA;
  11. 11Fluidigm Corporation, South San Francisco, California 94080, USA

  1. ↵12 These authors contributed equally to this work.

  • Corresponding authors: cwu{at}partners.org, cheng-zhong_zhang{at}dfci.harvard.edu

    • Abstract

    Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype–phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy.

    Footnotes

    • Received October 18, 2016.
    • Accepted May 22, 2017.

    This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

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    1. Published in Advance July 5, 2017, doi: 10.1101/gr.217331.116 Genome Res. 27: 1300-1311 © 2017 Wang et al.; Published by Cold Spring Harbor Laboratory Press

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