Evaluation of cell-free DNA approaches for multi-cancer early detectionMore about Open Access at the Crick
Authors listArash Jamshidi Minetta C Liu Eric A Klein Oliver Venn Earl Hubbell John F Beausang Samuel Gross Collin Melton Alexander P Fields Qinwen Liu Nan Zhang Eric T Fung Kathryn N Kurtzman Hamed Amini Craig Betts Daniel Civello Peter Freese Robert Calef Konstantin Davydov Saniya Fayzullina Chenlu Hou Roger Jiang Byoungsok Jung Susan Tang Vasiliki Demas Joshua Newman Onur Sakarya Eric Scott Archana Shenoy Seyedmehdi Shojaee Kristan K Steffen Virgil Nicula Tom C Chien Siddhartha Bagaria Nathan Hunkapiller Mohini Desai Zhao Dong Donald A Richards Timothy J Yeatman Allen L Cohn David D Thiel Donald A Berry Mohan K Tummala Kristi McIntyre Mikkael A Sekeres Alan Bryce Alexander M Aravanis Michael V Seiden Charles Swanton
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In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.
Journal Cancer Cell
Issue number 12