Revolutionizing Cancer Detection: New Machine Learning Method Deciphers Genomic “Dark Matter”

ARTEMIS, a new machine learning tool by Johns Hopkins researchers, identifies repeat DNA sequences in cancer, enabling noninvasive diagnosis and insights into cancer genetics. It marks a significant advancement in cancer detection and monitoring, with potential applications in early detection and treatment response evaluation.

In a series of laboratory tests, the researchers first examined the distribution of 1.2 billion kmers defining unique repeats, finding them enriched in genes commonly altered in human cancers. For example, of 736 genes known to drive cancers, 487 contained an average fifteenfold higher than expected number of repeat sequences.

Finally, they evaluated whether the ARTEMIS blood test could identify where in the body a tumor originated in patients with cancer. When trained with information from the PCAWG participants, the tool could classify the source of tumor tissues with an average of 78%among 12 tumor types. The investigators then combined ARTEMIS and DELFI to assess blood samples from a group of 226 individuals with breast, ovarian, lung, colorectal, bile duct, gastric, or pancreatic tumors.

Reference: “Genome-wide repeat landscapes in cancer and cell-free DNA” by Akshaya V. Annapragada, Noushin Niknafs, James R. White, Daniel C. Bruhm, Christopher Cherry, Jamie E. Medina, Vilmos Adleff, Carolyn Hruban, Dimitrios Mathios, Zachariah H. Foda, Jillian Phallen, Robert B. Scharpf and Victor E. Velculescu, 13 March 2024,The work was supported in part by the Dr. Miriam and Sheldon G.

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