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.
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 novok-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:
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.
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Further development of this novel technology could result in new, more sensitive assays for clinical laboratories to use in the effort to improve antimicrobial stewardship in hospitals
Researchers at McMaster University in Ontario, Canada, have used artificial intelligence (AI) to identify a potential antibiotic that neutralizes the drug-resistant bacteria Acinetobacter baumannii, an antibiotic resistant pathogen commonly found in many hospitals. This will be of interest to clinical laboratory managers and microbiologists involved in identifying strains of bacteria to determine if they are antimicrobial-resistant (AMR) superbugs.
Using machine learning, the scientists screened thousands molecules to look for those that inhibited the growth of this specific pathogen. And they succeeded.
“We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii,” the researchers wrote in their published study.
They discovered that the molecule abaucin inhibited the growth of the antibiotic-resistant pathogen in vitro.
This shows how machine learning and AI technologies are giving biomedical researchers tools to identify new therapeutic drugs that are effective against drug-resistant strains of bacteria. This same research can be expected to lead to new clinical laboratory assays that determine if superbugs can be attacked by specific therapeutic drugs.
“When I think about AI in general, I think of these models as things that are just going to help us do the thing we’re going to do better,” Jonathan Stokes, PhD, Assistant Professor of Biomedicine and Biochemistry at McMaster University in Ontario, Canada, and lead author of the study, told USA Today. Clinical laboratory scientists and microbiologists will be encouraged by the McMaster University scientists’ findings. (Photo copyright: McMaster University.)
McMaster Study Details
Jonathan Stokes, PhD, head of the Stokes Laboratory at McMaster University, is Assistant Professor of Biomedicine/Biochemistry at McMaster and lead author of the study. Stokes’ team worked with researchers from the Broad Institute of MIT and Harvard to explore the effectiveness of AI in combating superbugs, USA Today reported.
“This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen,” the researchers wrote in Nature Chemical Biology.
Stokes Lab utilized the high-throughput drug screening technique, spending weeks growing and exposing Acinetobacter baumannii to more than 7,500 agents of drugs and active ingredients of drugs. When 480 compounds were uncovered that blocked the growth of bacteria, this information was then provided to a computer that was trained to run an AI algorithm, CNN reported.
“Once we had our [machine learning] model trained, what we could do then is start showing that model brand-new pictures of chemicals that it had never seen, right? And based on what it had learned during training, it would predict for us whether those molecules were antibacterial or not,” Stokes told CNN.
The model spent hours screening more than 6,000 molecules. It then narrowed the search to 240 chemicals, which were tested in the lab. The scientists pared down the results to the nine most effective inhibitors of bacteria. They then eliminated those that were either related to existing antibiotics or might be considered dangerous.
The researchers found one compound—RS102895 (abaucin)—which, according to Stokes, was likely created to treat diabetes, CNN reported. The scientists discovered that the compound prevented bacterial components from making their way from inside a cell to the cell’s surface.
“It’s a rather interesting mechanism and one that is not observed amongst clinical antibiotics so far as I know,” Stokes told CNN.
Because of the effectiveness of the antibiotic during testing on mice skin, the researchers believe this method may be useful for creating antibiotics custom made to battle additional drug resistant pathogens, CNN noted.
Defeating a ‘Professional Pathogen’
Acinetobacter baumannii (A. baumannii)—the focus of Stoke’s study—is often found on hospital counters and doorknobs and has a sneaky way of using other organisms’ DNA to resist antibiotic treatment, according to CNN.
“It’s what we call in the laboratory a professional pathogen,” Stokes told CNN.
A. baumannii causes infections in the urinary tract, lungs, and blood and typically wreaks havoc to vulnerable patients on breathing machines, in intensive care units, or undergoing surgery, USA Today reported.
A. baumannii is resistant to carbapenem, a potent antibiotic. The Centers for Disease Control and Prevention (CDC) reported that in 2017 the bacteria infected 8,500 people in hospitals, 700 of those infections being fatal.
Further, in its 2019 “Antibiotic Resistance Threats in the United States” report, the CDC stated that one out of every four patients infected with the bacteria died within one month of their diagnosis. The federal agency deemed the bacteria “of greatest need” for new antibiotics.
Thus, finding a way to defeat this particularly nasty bacteria could save many lives.
Implications of Study Findings on Development of new Antibiotics
The Stokes Laboratory study findings show promise. If more antibiotics worked so precisely, it’s possible bacteria would not have a chance to become resistant in the first place, CNN reported.
Next steps in Stokes’ research include optimizing the chemical structure and testing in larger animals or humans, USA Today reported.
“It’s important to remember [that] when we’re trying to develop a drug, it doesn’t just have to kill the bacterium,” Stokes noted. “It also has to be well tolerated in humans and it has to get to the infection site and stay at the infection site long enough to elicit an effect,” USA Today reported.
Stokes’ study is a prime example of how AI can make a big impact in clinical laboratory diagnostics and treatment.
“We know broad-spectrum antibiotics are suboptimal and that pathogens have the ability to evolve and adjust to every trick we throw at them … AI methods afford us the opportunity to vastly increase the rate at which we discover new antibiotics, and we can do it at a reduced cost. This is an important avenue of exploration for new antibiotic drugs,” Stokes told CNN.
Clinical laboratory managers and microbiologists may want to keep an open-mind about the use of AI in drug development. More research is needed to give substance to the McMaster University study’s findings. But the positive results may lead to methods for fine tuning existing antibiotics to better combat antimicrobial-resistant bacteria, USA Today reported.
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