Details

Autor(en) / Beteiligte

• Jamshidi, Arash
• Liu, Minetta C.
• Klein, Eric A.
• Venn, Oliver
• Hubbell, Earl
• Beausang, John F.
• Gross, Samuel
• Melton, Collin
• Fields, Alexander P.
• Liu, Qinwen
• Zhang, Nan
• Fung, Eric T.
• Kurtzman, Kathryn N.
• Amini, Hamed
• Betts, Craig
• Civello, Daniel
• Freese, Peter
• Calef, Robert
• Davydov, Konstantin
• Fayzullina, Saniya
• Hou, Chenlu
• Jiang, Roger
• Jung, Byoungsok
• Tang, Susan
• Demas, Vasiliki
• Newman, Joshua
• Sakarya, Onur
• Scott, Eric
• Shenoy, Archana
• Shojaee, Seyedmehdi
• Steffen, Kristan K.
• Nicula, Virgil
• Chien, Tom C.
• Bagaria, Siddhartha
• Hunkapiller, Nathan
• Desai, Mohini
• Dong, Zhao
• Richards, Donald A.
• Yeatman, Timothy J.
• Cohn, Allen L.
• Thiel, David D.
• Berry, Donald A.
• Tummala, Mohan K.
• McIntyre, Kristi
• Sekeres, Mikkael A.
• Bryce, Alan
• Aravanis, Alexander M.
• Seiden, Michael V.
• Swanton, Charles

Titel

Evaluation of cell-free DNA approaches for multi-cancer early detection

Ist Teil von

• Cancer cell, 2022-12, Vol.40 (12), p.1537-1549.e12

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. [Display omitted] •Clinical LOD is a useful benchmark to assess cfDNA-based test performance•cTAF accounts for cfDNA cancer signal variation across cancer types and stages•cfDNA methylation was the most promising genomic feature for cancer signal detection•The results informed the development of a cfDNA-based multi-cancer early detection test Jamshidi et al. compare several approaches for circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) tests. A whole-genome methylation-based approach has the best performance among those evaluated. In addition, they define a metric—clinical limit of detection (LOD)—based on tumor fraction to enable future comparison of cfDNA-based tests.