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[https://doi.org/10.1038/s41467-024-47195-7](https://doi.org/10.1038/s41467-024-47195-7)  \nA cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary  \nReceived: 12 October 2023  \n\n| Accepted: 18 March 2024 |\n| --- |\n| |\n| Check for updates |\n\nAlicia-Marie Conway 1,2,5, Simon P. Pearce 3,5, Alexandra Clipson 1,5, Steven M. Hill 3, Francesca Chemi1, Dan Slane-Tan1, Saba Ferdous3, A. S. Md Mukarram Hossain 3, Katarzyna Kamieniecka3, Daniel J. White1, Claire Mitchell4, Alastair Kerr 3, Matthew G. Krebs2, Gerard Brady1, Caroline Dive 1,3 , Natalie Cook 2  & Dominic G. Rothwell 1   \nCancers 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.  \nThe diagnostic and therapeutic challenges in Cancers of Unknown Primary (CUP) are an increasingly important unmet clinical need, recently exemplified by several large studies and updated ESMO CUP guidelines1–4. Improved management pathways attempt to aid diagnosis of otherwise ‘diagnostically challenging’ primary tumours often misclassified as CUP3. These pathways also seek to simplify and expand classification of ‘favourable’ CUP subsets defined as tumours clinically aligned to known tumour types3. Patients within the ‘favourable’ subset (~20%)can access the aligned tumour site-directed therapies and achieve better clinical outcomes5. However, for these patients arriving at a primary tumour diagnosis usually involves a lengthy diagnostic journey, comprising multiple investigations and pathology reviews, from a very limited quantity of tissue obtained via an invasive tumour biopsy.  \nFor patients within the ‘unfavourable’ CUP subset, treatment is limited to ‘one-size-fits-all’ chemotherapy despite a wealth of clinical, pathological and molecular heterogeneity. The role of biomarkerdriven precision oncology and advent of immunotherapies is rapidly changing standard-of-care (SOC) treatment and improving overall survival across numerous tumour types. However, most of these approaches remain out-of-reach for patients with ‘unfavourable’ CUP as currently only a handful of treatments are approved irrespective of tumour type (tumour agnostic) . Most targeted therapies demonstrate tumour-type dependent efficacy, exemplified by the activity of targeted inhibitors in B-RAF mutant melanoma versus inactivity in colorectal cancers6. Additionally, immunotherapy is increasingly indicated by biomarker presence validated by tumour type.  \n1Nucleic Acid Biomarker Team, Cancer Research UK National Biomarker Centre, The University of Manchester, Manchester, UK. 2Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester and The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. 3Bioinformatics and Biostatistics Team, Cancer Research UK National Biomarker Centre, The University of Manchester, Manchester, UK. 4The Christie NHS Foundation Trust, Manchester, UK. 5These authors contributed equally: Alicia","cbCaink10sxBtnB2","https://ap.wps.com/l/cbCaink10sxBtnB2","pdf",2127621,12,"English","# Introduction\n## Challenges in CUP diagnosis and treatment\n## Precision oncology and biomarker-driven approaches\n# CUPiD Method and Performance\n## cfDNA methylation-based machine learning classifier\n## Cohort evaluation and accuracy metrics","[{\"question\":\"What problem does this study address in cancers of unknown primary (CUP)?\",\"answer\":\"CUP remains difficult to diagnose and treat because tumors are biologically heterogeneous and often do not respond well to standard chemotherapy.\"},{\"question\":\"How does the proposed CUPiD classifier make tissue-of-origin (TOO) predictions?\",\"answer\":\"CUPiD uses circulating cell-free DNA (cfDNA) methylation patterns and machine learning to predict TOO across many tumor classes.\"},{\"question\":\"What performance results are reported for CUPiD?\",\"answer\":\"On 143 cfDNA samples, CUPiD achieved 84.6% overall sensitivity and 96.8% TOO accuracy, with additional cohort results showing 78.0% predictions in CUP cases and 88.5% clinical consistency when follow-up information was available.\"}]","A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary | PDF",1789988996]