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Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution

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  • S. Kasar

    (Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School
    Brigham and Women’s Hospital, Harvard Medical School)

  • J. Kim

    (Cancer Program, Broad Institute of Harvard and MIT)

  • R. Improgo

    (Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School
    Brigham and Women’s Hospital, Harvard Medical School)

  • G. Tiao

    (Cancer Program, Broad Institute of Harvard and MIT)

  • P. Polak

    (Cancer Program, Broad Institute of Harvard and MIT)

  • N. Haradhvala

    (Cancer Program, Broad Institute of Harvard and MIT)

  • M. S. Lawrence

    (Cancer Program, Broad Institute of Harvard and MIT)

  • A. Kiezun

    (Cancer Program, Broad Institute of Harvard and MIT)

  • S. M. Fernandes

    (Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School)

  • S. Bahl

    (Cancer Program, Broad Institute of Harvard and MIT)

  • C. Sougnez

    (Cancer Program, Broad Institute of Harvard and MIT)

  • S. Gabriel

    (Cancer Program, Broad Institute of Harvard and MIT)

  • E. S. Lander

    (Cancer Program, Broad Institute of Harvard and MIT)

  • H. T. Kim

    (Dana-Farber Cancer Institute)

  • G. Getz

    (Cancer Program, Broad Institute of Harvard and MIT
    Massachusetts General Hospital
    Harvard Medical School)

  • J. R. Brown

    (Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School
    Brigham and Women’s Hospital, Harvard Medical School)

Abstract

Patients with chromosome 13q deletion or normal cytogenetics represent the majority of chronic lymphocytic leukaemia (CLL) cases, yet have relatively few driver mutations. To better understand their genomic landscape, here we perform whole-genome sequencing on a cohort of patients enriched with these cytogenetic characteristics. Mutations in known CLL drivers are seen in only 33% of this cohort, and associated with normal cytogenetics and unmutated IGHV. The most commonly mutated gene in our cohort, IGLL5, shows a mutational pattern suggestive of activation-induced cytidine deaminase (AID) activity. Unsupervised analysis of mutational signatures demonstrates the activities of canonical AID (c-AID), leading to clustered mutations near active transcriptional start sites; non-canonical AID (nc-AID), leading to genome-wide non-clustered mutations, and an ageing signature responsible for most mutations. Using mutation clonality to infer time of onset, we find that while ageing and c-AID activities are ongoing, nc-AID-associated mutations likely occur earlier in tumour evolution.

Suggested Citation

  • S. Kasar & J. Kim & R. Improgo & G. Tiao & P. Polak & N. Haradhvala & M. S. Lawrence & A. Kiezun & S. M. Fernandes & S. Bahl & C. Sougnez & S. Gabriel & E. S. Lander & H. T. Kim & G. Getz & J. R. Brow, 2015. "Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution," Nature Communications, Nature, vol. 6(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9866
    DOI: 10.1038/ncomms9866
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    Cited by:

    1. Ryan N. Ptashkin & Mark D. Ewalt & Gowtham Jayakumaran & Iwona Kiecka & Anita S. Bowman & JinJuan Yao & Jacklyn Casanova & Yun-Te David Lin & Kseniya Petrova-Drus & Abhinita S. Mohanty & Ruben Bacares, 2023. "Enhanced clinical assessment of hematologic malignancies through routine paired tumor and normal sequencing," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Romanos Sklavenitis-Pistofidis & Yoshinobu Konishi & Daniel Heilpern-Mallory & Ting Wu & Nicholas Tsakmaklis & Michelle P. Aranha & Zachary R. Hunter & Alaa K. Ali & Junko Tsuji & Nicholas J. Haradhva, 2025. "Single-cell RNA sequencing defines distinct disease subtypes and reveals hypo-responsiveness to interferon in asymptomatic Waldenstrom’s Macroglobulinemia," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    3. Luan Nguyen & Arne Hoeck & Edwin Cuppen, 2022. "Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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