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, Arman Hossain, Katarzyna Kamieniecka, Daniel J. White, Claire L. Mitchell, Alastair Kerr, Matthew Krebs, Gerard Brady, Caroline Dive, Natalie Cook, Dominic G. Rothwell
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.