Novel Machine Learning Blood Test Detects Cancers with Genome-wide Mutations in Single Molecules of Cell-free DNA
Published:26 Aug.2023 Source:Johns Hopkins Medicine
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. 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. To develop GEMINI, investigators examined whole-genome sequences of cancers from 2,511 people across 25 different cancers from the Pan-Cancer Analysis of Whole Genomes study, identifying distinct mutation frequencies across the genome in different tumor types, also identified genomic regions with the highest number of mutations.
Researchers evaluated GEMINI's ability to detect sequence alterations in cfDNA from 365 people in the Longitudinal Urban Cohort Ageing Study (LUCAS), a cohort of people at high risk of having lung cancer. In 89 samples from patients from the LUCAS cohort who had lung cancer, GEMINI combined with DELFI (DNA evaluation of fragments for early interception) correctly detected cancers 91% of the time. The investigators also studied the use of GEMINI in other study samples, including seven patients who did not have any detectable tumors at the time of blood collection. Additional experiments determined that GEMINI could distinguish between subtypes of lung cancers and could detect early liver cancers.