1. Evaluation of cell-free DNA approaches for multi-cancer early detection
- Author
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Arash 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, and Charles Swanton
- Subjects
Chemical Biology & High Throughput ,Signalling & Oncogenes ,Human Biology & Physiology ,Cancer Research ,Ecology,Evolution & Ethology ,Oncology ,Genome Integrity & Repair ,Tumour Biology ,Genetics & Genomics ,Computational & Systems Biology - Abstract
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.
- Published
- 2022