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

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, Claire Mitchell, Alastair Kerr, Matthew G. Krebs, Gerard Brady, Caroline Dive (), Natalie Cook () and Dominic G. Rothwell ()
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Alicia-Marie Conway: The University of Manchester
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
Natalie Cook: The University of Manchester and The Christie NHS Foundation Trust, Manchester Academic Health Science Centre
Dominic G. Rothwell: The University of Manchester

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-47195-7

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