Novel machine learning blood test detects cancers with genome-wide mutations in single molecules of cell-free DNA
Novel blood testing technology being developed by researchers at the Johns Hopkins Kimmel Cancer Center that combines genome-wide sequencing of single molecules of DNA shed from tumors and machine learning may allow earlier detection of lung and other cancers.
The test, called GEMINI (Genome-wide Mutational Incidence for Non-Invasive detection of cancer), looks for changes to DNA throughout the genome. First, a blood sample is collected from a person at risk for developing cancer. Then, cell-free DNA (cfDNA) shed by tumors is extracted from the plasma and sequenced using cost-efficient whole genome sequencing. Single molecules of DNA are analyzed for sequence alterations and are used to obtain mutation profiles across the genome. Finally, a machine learning model trained to identify changes in cancer and non-cancer mutation frequencies in different regions of the genome is used to distinguish people who have cancer from those who do not have cancer. The classifier generates a score ranging from 0 to 1, with a higher score reflecting a higher probability of having cancer.
In a series of laboratory tests of GEMINI, investigators found that the approach, when followed by computerized tomography imaging, detected over 90% of lung cancers, including among patients with stage I and II disease. A description of the work, a proof-of-concept study, will be published online July 27 in the journal Nature Genetics.
