1. A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary
- Author
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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
- Subjects
Science - 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.
- Published
- 2024
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