Two doctoral students at the Johns Hopkins Bloomberg School of Public Health are part of the research team that developed a simple new blood test that can detect the presence of seven different types of cancer by spotting unique patterns in the fragmentation of DNA shed from cancer cells and circulating in the bloodstream.
A report on the research was published online May 29 in Nature.
Mr. Stephen Cristiano, the paper’s first author, is a PhD candidate in the Johns Hopkins Bloomberg School of Public Health’s department of biostatistics. Mr. Jacob Fiksel, also a PhD candidate in the Bloomberg School’s department of biostatistics, was a co-author, along with colleagues at Johns Hopkins Kimmel Cancer Center and Johns Hopkins School of Medicine.
In a proof-of-concept study, the test, called DELFI (DNA evaluation of fragments for early interception), accurately detected the presence of cancer DNA in 57 percent to more than 99 percent of blood samples from 208 patients with various stages of breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancers in the U.S., Denmark and the Netherlands.
DELFI also performed well in tests of blood samples from 215 healthy individuals, falsely identifying cancer in just four cases. The test uses machine learning, a type of artificial intelligence, to identify abnormal patterns of DNA fragments in the blood of patients with cancer. By studying these patterns, the investigators said they could identify the cancers’ tissue of origin in up to 75 percent of cases.Friday Letter Submission