1. Cancer Biology
  2. Genetics and Genomics
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A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes

  1. Mohammad KaramiNejadRanjbar
  2. Sahand Sharifzadeh
  3. Nina C Wietek
  4. Mara Artibani
  5. Salma El-Sahhar
  6. Tatjana Sauka-Spengler
  7. Christopher Yau
  8. Volker Tresp
  9. Ahmed A Ahmed  Is a corresponding author
  1. University of Oxford, United Kingdom
  2. Ludwig Maximilian University of Munich, Germany
  3. University of Birmingham, United Kingdom
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Cite this article as: eLife 2020;9:e55207 doi: 10.7554/eLife.55207

Abstract

Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor's evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive mutations when sequencing picogram quantities of DNA. Data pooling to increase the confidence in the discovered mutations, moves the discovery back in the past to a common ancestor. Here we report a robust whole genome sequencing and analysis pipeline (DigiPico/MutLX) that virtually eliminates all false positive results while retaining an excellent proportion of true positives. Using our method, we identified, for the first time, a hyper-mutation (kataegis) event in a group of ∼30 cancer cells from a recurrent ovarian carcinoma. This was unidentifiable from the bulk WGS data. Overall, we propose DigiPico/MutLX method as a powerful framework for the identification of clone-specific variants at an unprecedented accuracy.

Data availability

Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001003555 (EGAD00001005118). Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"

The following data sets were generated

Article and author information

Author details

  1. Mohammad KaramiNejadRanjbar

    NDWRH, University of Oxford, Oxford, United Kingdom
    Competing interests
    Mohammad KaramiNejadRanjbar, AA and MK hold a patent application for DigiPico sequencing method (UK Patent Application No. 1918043.9)..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7770-2065
  2. Sahand Sharifzadeh

    Computer science, Ludwig Maximilian University of Munich, Munich, Germany
    Competing interests
    No competing interests declared.
  3. Nina C Wietek

    NDWRH, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  4. Mara Artibani

    NDWRH, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  5. Salma El-Sahhar

    NDWRH, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  6. Tatjana Sauka-Spengler

    Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9289-0263
  7. Christopher Yau

    Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7615-8523
  8. Volker Tresp

    Computer science, Ludwig Maximilian University of Munich, Munich, Germany
    Competing interests
    No competing interests declared.
  9. Ahmed A Ahmed

    NDWRH, University of Oxford, Oxford, United Kingdom
    For correspondence
    ahmed.ahmed@wrh.ox.ac.uk
    Competing interests
    Ahmed A Ahmed, MK and AA have filed a patent application regarding the DigiPico method (UK Patent Application No. 1918043.9)..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6509-2581

Funding

Ovarian Cancer Action (HER000762)

  • Ahmed A Ahmed

National Institute for Health Research (IS-BRC-0211-10025)

  • Ahmed A Ahmed

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Patients #11152, #11502 and #11513 provided written consent for participation in the prospective biomarker validation study Gynaecological Oncology Targeted Therapy Study 01 (GO-Target-01) under research ethics approval number 11/SC/0014. Patient OP1036 participated in the prospective Oxford Ovarian Cancer Predict Chemotherapy Response Trial (OXO-PCR-01), under research ethics approval number 12/SC/0404. Necessary informed consents from study participants were obtained as appropriate.

Reviewing Editor

  1. C Daniela Robles-Espinoza, International Laboratory for Human Genome Research, Mexico

Publication history

  1. Received: January 16, 2020
  2. Accepted: April 7, 2020
  3. Accepted Manuscript published: April 7, 2020 (version 1)
  4. Version of Record published: May 15, 2020 (version 2)
  5. Version of Record updated: June 29, 2020 (version 3)

Copyright

© 2020, KaramiNejadRanjbar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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