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A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary

Author

Listed:
  • Alicia-Marie Conway

    (The University of Manchester
    The University of Manchester and The Christie NHS Foundation Trust, Manchester Academic Health Science Centre)

  • Simon P. Pearce

    (The University of Manchester)

  • Alexandra Clipson

    (The University of Manchester)

  • Steven M. Hill

    (The University of Manchester)

  • Francesca Chemi

    (The University of Manchester)

  • Dan Slane-Tan

    (The University of Manchester)

  • Saba Ferdous

    (The University of Manchester)

  • A. S. Md Mukarram Hossain

    (The University of Manchester)

  • Katarzyna Kamieniecka

    (The University of Manchester)

  • Daniel J. White

    (The University of Manchester)

  • Claire Mitchell

    (The Christie NHS Foundation Trust)

  • Alastair Kerr

    (The University of Manchester)

  • Matthew G. Krebs

    (The University of Manchester and The Christie NHS Foundation Trust, Manchester Academic Health Science Centre)

  • Gerard Brady

    (The University of Manchester)

  • Caroline Dive

    (The University of Manchester
    The University of Manchester)

  • Natalie Cook

    (The University of Manchester and The Christie NHS Foundation Trust, Manchester Academic Health Science Centre)

  • Dominic G. Rothwell

    (The University of Manchester)

Abstract

Cancers of Unknown Primary (CUP) remains a diagnostic and therapeutic challenge due to biological heterogeneity and poor responses to standard chemotherapy. Predicting tissue-of-origin (TOO) molecularly could help refine this diagnosis, with tissue acquisition barriers mitigated via liquid biopsies. However, TOO liquid biopsies are unexplored in CUP cohorts. Here we describe CUPiD, a machine learning classifier for accurate TOO predictions across 29 tumour classes using circulating cell-free DNA (cfDNA) methylation patterns. We tested CUPiD on 143 cfDNA samples from patients with 13 cancer types alongside 27 non-cancer controls, with overall sensitivity of 84.6% and TOO accuracy of 96.8%. In an additional cohort of 41 patients with CUP CUPiD predictions were made in 32/41 (78.0%) cases, with 88.5% of the predictions clinically consistent with a subsequent or suspected primary tumour diagnosis, when available (23/26 patients). Combining CUPiD with cfDNA mutation data demonstrated potential diagnosis re-classification and/or treatment change in this hard-to-treat cancer group.

Suggested Citation

  • Alicia-Marie Conway & Simon P. Pearce & Alexandra Clipson & Steven M. Hill & Francesca Chemi & Dan Slane-Tan & Saba Ferdous & A. S. Md Mukarram Hossain & Katarzyna Kamieniecka & Daniel J. White & Clai, 2024. "A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47195-7
    DOI: 10.1038/s41467-024-47195-7
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    References listed on IDEAS

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