Blood Journal
Leading the way in experimental and clinical research in hematology

Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns

  1. Anna Schuh1,
  2. Jennifer Becq2,
  3. Sean Humphray2,
  4. Adrian Alexa2,
  5. Adam Burns1,
  6. Ruth Clifford1,
  7. Stephan M. Feller3,
  8. Russell Grocock2,
  9. Shirley Henderson1,
  10. Irina Khrebtukova4,
  11. Zoya Kingsbury2,
  12. Shujun Luo4,
  13. David McBride2,
  14. Lisa Murray2,
  15. Toshi Menju3,5,
  16. Adele Timbs1,
  17. Mark Ross2,
  18. Jenny Taylor1, and
  19. David Bentley2
  1. 1Oxford National Institute of Health Research (NIHR) Biomedical Research Centre, University of Oxford, Oxford, United Kingdom;
  2. 2Illumina Cambridge Ltd, Saffron Walden, United Kingdom;
  3. 3Biologic Systems Architecture Group, Department of Oncology, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom;
  4. 4Illumina Inc, Hayward, CA; and
  5. 5Department of Thoracic Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan

Abstract

Chronic lymphocytic leukemia is characterized by relapse after treatment and chemotherapy resistance. Similarly, in other malignancies leukemia cells accumulate mutations during growth, forming heterogeneous cell populations that are subject to Darwinian selection and may respond differentially to treatment. There is therefore a clinical need to monitor changes in the subclonal composition of cancers during disease progression. Here, we use whole-genome sequencing to track subclonal heterogeneity in 3 chronic lymphocytic leukemia patients subjected to repeated cycles of therapy. We reveal different somatic mutation profiles in each patient and use these to establish probable hierarchical patterns of subclonal evolution, to identify subclones that decline or expand over time, and to detect founder mutations. We show that clonal evolution patterns are heterogeneous in individual patients. We conclude that genome sequencing is a powerful and sensitive approach to monitor disease progression repeatedly at the molecular level. If applied to future clinical trials, this approach might eventually influence treatment strategies as a tool to individualize and direct cancer treatment.

  • Submitted May 30, 2012.
  • Accepted August 5, 2012.
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