Johns Hopkins Research Team Uses Machine Learning on DNA “Dark Matter” in Blood to Identify Cancer
Findings could lead to new biomarkers clinical laboratories would use for identifying cancer in patients and monitoring treatments
As DNA “dark matter” (the DNA sequences between genes) continues to be studied, researchers are learning that so-called “junk DNA” (non-functional DNA) may influence multiple health conditions and diseases including cancer. This will be of interest to pathologists and clinical laboratories engaged in cancer diagnosis and may lead to new non-invasive liquid biopsy methods for identifying cancer in blood draws.
Researchers at Johns Hopkins Kimmel Cancer Center in Baltimore, Md., developed a technique to identify changes in repeat elements of genetic code in cancerous tissue as well as in cell-free DNA (cf-DNA) that are shed in blood, according to a Johns Hopkins news release.
The Hopkins researchers described their machine learning approach—called ARTEMIS (Analysis of RepeaT EleMents in dISease)—in the journal Science Translational Medicine titled, “Genomewide Repeat Landscapes in Cancer and Cell-Free DNA.”
ARTEMIS “shows potential to predict cases of early-stage lung cancer or liver cancer in humans by detecting repetitive genetic sequences,” Genetic Engineering and Biotechnology News (GEN) reported.
This technique could enable non-invasive monitoring of cancer treatment and cancer diagnosis, Technology Networks noted.
“Our study shows that ARTEMIS can reveal genomewide repeat landscapes that reflect dramatic underlying changes in human cancers,” said study co-leader Akshaya Annapragada (above), an MD/PhD student at the Johns Hopkins University School of Medicine, in a news release. “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 genomewide repeat landscapes as tissue and blood-based biomarkers for cancer detection, characterization, and monitoring.” Clinical laboratories may soon have new biomarkers for the detection of cancer. (Photo copyright: Johns Hopkins University.)
Detecting Early Lung, Liver Cancer
Artemis is a Greek word meaning “hunting goddess.” For the Johns Hopkins researchers, ARTEMIS also describes a technique “to analyze junk DNA found in tumors” and which float in the bloodstream, Financial Times explained.
“It’s like a grand unveiling of what’s behind the curtain,” said geneticist Victor Velculescu, MD, PhD, Professor of Oncology and co-director of the Cancer Genetics and Epigenetics Program at Johns Hopkins Kimmel Cancer Center, in the news release.
“Until ARTEMIS, this dark matter of the genome was essentially ignored, but now we’re seeing that these repeats are not occurring randomly,” he added. “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.”
ARTEMIS could “lead to new therapies, new diagnostics, and new screening approaches for cancer,” Velculescu noted.
Repeats of DNA Sequences Tough to Study
For some time technical limitations have hindered analysis of repetitive genomic sequences by scientists.
“Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches,” the study authors wrote in their Science Translational Medicine paper.
“We developed a de novo k-mer (short sequences of DNA)-finding approach called ARTEMIS to identify repeat elements from whole-genome sequencing,” the researchers wrote.
The scientists put ARTEMIS to the test in laboratory experiments.
The first analysis involved 1,280 types of repeating genetic elements “in both normal and tumor tissues from 525 cancer patients” who participated in the Pan-Cancer Analysis of Whole Genomes (PCAWG), according to Technology Networks, which noted these findings:
- A median of 807 altered elements were found in each tumor.
- About two-thirds (820) had not “previously been found altered in human cancer.”
Second, the researchers explored “genomewide repeat element changes that were predictive of cancer,” by using machine learning to give each sample an ARTEMIS score, according to the Johns Hopkins news release.
The scoring detected “525 PCAWG participants’ tumors from the healthy tissues with a high performance” overall Area Under the Curve (AUC) score of 0.96 (perfect score being 1.0) “across all cancer types analyzed,” the Johns Hopkins’ release states.
Liquid Biopsy Deployed
The scientists then used liquid biopsies to determine ARTEMIS’ ability to noninvasively diagnose cancer. Researchers used blood samples from:
- 287 people with and without lung cancer who were part of the Danish Lung Cancer Screening Study, and
- 208 people at risk of liver cancer.
Results, according to Johns Hopkins:
- ARTEMIS classified patients with lung cancer with an AUC of 0.82.
- ARTEMIS detected people with liver cancer, as compared to others with cirrhosis or viral hepatitis, with a score of AUC 0.87.
Finally, the scientists used their “ARTEMIS blood test” to find the origin of tumors in patients with cancer. They reported their technique was 78% accurate in discovering tumor tissue sources among 12 tumor types.
“These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer,” the researchers wrote in Science Translational Medicine.
Large Clinical Trials Planned
Velculescu said more research is planned, including larger clinical trials.
“While still at an early stage, this research demonstrates how some cancers could be diagnosed earlier by detecting tumor-specific changes in cells collected from blood samples,” Hattie Brooks, PhD, Research Information Manager, Cancer Research UK (CRUK), told Financial Times.
Should ARTEMIS prove to be a viable, non-invasive blood test for cancer, it could provide pathologists and clinical laboratories with new biomarkers and the opportunity to work with oncologists to promptly diagnosis cancer and monitor patients’ response to treatment.
—Donna Marie Pocius
Related Information:
Genomewide Repeat Landscapes in Cancer and Cell-Free DNA
AI Detects Cancer VIA DNA Repeats in Liquid Biopsies
