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Genetic “Dark Matter” Could Help Monitor Cancer

A digital illustration of a DNA double helix.
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Repeating genetic sequences in DNA are sometimes known as “junk DNA” or genetic “dark matter”. But thanks to new advances, we are learning more and more about what these sequences are, and how they may not be junk after all.

Researchers from the Johns Hopkins Kimmel Cancer Center have developed a machine-learning approach that identifies these sequences in both tumor DNA and cell-free DNA (cfDNA) that are shed into the blood.

The new approach, described in Science Translational Medicine, could provide a method to monitor cancer treatment or even detect cancers non-invasively.

Associations with repeated genetic sequences

“When you think about existing cancer genes and the DNA sequences around them, they’re just chock full of these repeats,” said Victor E. Velculescu, a professor of oncology and co-director of the Cancer Genetics and Epigenetics Program at the Johns Hopkins Kimmel Cancer Center and the senior author of the study.

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The researchers named their approach ARTEMIS ­– analysis of repeat elements in disease.

ARTEMIS analyzed over 1,200 types of these repeating genetic elements in both normal and tumor tissues from 525 cancer patients, finding a median of 807 altered elements in each tumor.

Nearly two-thirds of the identified repeats (820 of 1,280) had not previously been found altered in human cancer.

“Until ARTEMIS, this dark matter of the genome was essentially ignored, but now we’re seeing that these repeats are not occurring randomly,” Velculescu explained. “They end up being clustered around genes that are altered in cancer in a variety of different ways, providing the first glimpse that these sequences may be key to tumor development.”

Next, they created an ARTEMIS score for each sample using a machine-learning model. This provides a summary of the repeat element changes that were predictive of cancer. The ARTEMIS scores were able to distinguish the 525 tumor samples from normal tissue samples with high performance, with an area under the curve (AUC) of 0.96 – with 1 considered a perfect score. Additionally, higher ARTEMIS scores were linked to shorter progression-free and overall survival of the patients.

ARTEMIS was also evaluated for non-invasive cancer detection, applying it to 287 blood samples from patients with and without lung cancer. This classified patients with an AUC of 0.82 but combining ARTEMIS with another method – which detects changes in the size and distribution of cfDNA fragments – increased it to 0.91. Similar results were achieved for detecting lung cancer patients from those with cirrhosis or viral hepatitis.

ARTEMIS was also able to identify where in the body the tumor originated across 12 different tumor types with an average accuracy of 78%.

Illuminating the “dark genome”

“Our study shows that ARTEMIS can reveal genome-wide repeat landscapes that reflect dramatic underlying changes in human cancers,” said Akshaya K. Annapragada, lead author of the study and MD/PhD student at Johns Hopkins. “By illuminating the so-called ‘dark genome,’ the work offers unique insights into the cancer genome and provides a proof-of-concept for the utility of genome-wide repeat landscapes as tissue and blood-based biomarkers for cancer detection, characterization and monitoring.”

Velculescu explained that the project’s next steps are to evaluate the approach in larger clinical trials: “You can imagine this could be used for early detection for a variety of cancer types, but also could have uses in other applications such as monitoring response to treatment or detecting recurrence. This is a totally new frontier.”

Reference: Annapragada AV, Niknafs N, White JR, et al. Genome-wide repeat landscapes in cancer and cell-free DNA. Scie Transl Med. 2024;16(738):eadj9283. doi: 10.1126/scitranslmed.adj9283

This article is a rework of a press release issued by Johns Hopkins Medicine. Material has been edited for length and content.