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Clinical Laboratories and Pathology Groups

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Clinical Laboratories and Pathology Groups

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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:

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:

“Junk DNA” No More: Johns Hopkins Investigators Develop Method of Identifying Cancers from Repeat Elements of Genetic Code

Genomewide Repeat Landscapes in Cancer and Cell-Free DNA

AI Detects Cancer VIA DNA Repeats in Liquid Biopsies

Genetic “Dark Matter” Could Help Monitor Cancer

AI Explores “Dark Genome” to Shed Light on Cancer Growth

Harvard Researchers Create Chip-based, Liquid Biopsy Device That Offers a Novel Way to Monitor Treatment of Ovarian Cancer Patients and Only Costs $1

The ATC Chip identifies ovarian cancer cells floating in ascites and may be useful for diagnosing other types of malignancies that involve ascites, like pancreatic cancer

Pathologists will be interested to learn that researchers at Harvard Medical School and Massachusetts General Hospital are developing a “liquid biopsy” technology specifically to enable point-of-care monitoring of the progress of patients undergoing treatment for certain types of cancers.

The goal is to develop a method that community hospitals can use to monitor treatment of ovarian cancer patients without the need for expensive medical laboratory equipment, noted a report published by Biosciencetechnology.com. Researchers estimate that their ‘liquid biopsy’ technology could cost as little as $1 per test when eventually cleared for use in clinical settings.
(more…)

Researchers Determine That Individuals’ ‘Breathprint’ Are Unique; May Have Potential for Clinical Laboratory Testing When Coupled With Mass Spectrometry Technology

Pathologists may be interested to learn that everyone’s breath reveals a signature composition of metabolites that may reflect a lifetime of diet, state of health, illnesses, and exposure to chemicals

New research shows that a person’s “breathprint” is as unique as a fingerprint and may be as effective as bodily fluids in diagnosing diseases. That same research effort is showing that it is feasible to combine breath specimens and mass spectrometry to accurately identify disease. That could give clinical laboratories a new methodology to use when creating diagnostic assays.

These findings are part of a new study conducted by researchers at the Swiss Federal Institute of Technology (ETH Zurich) in Zurich. The study was published by the journal PLOS ONE. (more…)

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