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GRAIL compared ten ways of reading free-floating DNA in the same blood samples and found that methylation worked best both for spotting cancer and for saying where it was.
In the first substudy of the Circulating Cell-free Genome Atlas (NCT02889978), ten machine-learning classifiers built on different cell-free DNA features were trained on the same samples and independently validated. At 98% specificity, whole-genome methylation, single-nucleotide variants with white-blood-cell background removal, and combined scores gave the highest cancer signal sensitivity. The circulating tumour allele fraction predicted classifier performance better than clinical stage or tumour type, and whole-genome methylation best predicted cancer signal origin, which informed the targeted methylation test.
Whether a cancer is found depends mostly on how much tumour DNA it sheds into the blood, which is why small early cancers are often missed by every approach.
Shares CCGA (Circulating Cell-free Genome Atlas), Cell-free DNA methylation tests, GRAIL, Cell-free DNA (cfDNA).
Shares CCGA (Circulating Cell-free Genome Atlas), Eric A. Klein, Cell-free DNA methylation tests, GRAIL.
Shares CCGA (Circulating Cell-free Genome Atlas), Cell-free DNA methylation tests, Cell-free DNA (cfDNA), Galleri.
Shares Cell-free DNA methylation tests, GRAIL, Galleri, Multi-cancer early detection (MCED).
Shares GRAIL, Galleri, Multi-cancer early detection (MCED).
Shares GRAIL, Galleri, Multi-cancer early detection (MCED).
Shares GRAIL, Galleri, Multi-cancer early detection (MCED).
Shares GRAIL, Galleri, Multi-cancer early detection (MCED).
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