Next Generation Sequencing (NGS) vs. Standard of Care (SOC) Diagnostics

Although medical professionals have long used patient samples to detect disease-causing pathogens, next generation sequencing is poised to open a whole new frontier in terms of clinical diagnostic precision. Furthermore, diagnostic methods using next generation sequencing have the potential to detect bacteria, viruses, and fungi with far greater speed and ease.

When it comes to diagnosing many common and serious pathogen-caused illnesses, traditional methods are often slow and laborious. Otherwise known as standard of care (SOC), these traditional methods also run the risk of looking for pathogens in the wrong places. Broadly defined, the term SOC encompasses the full battery of diagnostic tests that clinicians have used for decades.

Next generation sequencing methods, by contrast, begin with a far broader field of pathogens and can search through this field incredibly rapidly. In fact, these methods are capable of sequencing multiple strands of patient sample DNA at once to quickly identify a single offending pathogen among hundreds that might cause a target illness.

These were the findings of a recent study by researchers from Johns Hopkins Medicine in Baltimore, Maryland. Led by senior author and Johns Hopkins University School of Medicine associate professor of pathology Patricia Simner, PhD, the researchers reported their findings on June 13, 2022, in the American Society for Microbiology’s Journal of Clinical Microbiology.

The study focused on samples obtained from human lungs through bronchoalveolar lavage (passing a bronchoscope into the lungs through the mouth or nose and then releasing a fluid wash to be collected for analysis). In screening these samples for respiratory pathogens, the Johns Hopkins team became among the very first researchers to directly compare NGS and SOC diagnostic approaches in the laboratory setting.

In the first half of their study, Simner and her team juxtaposed the diagnostic ability of SOC methods with that of metagenomic NGS (a form of NGS that involves reading both host and microbial DNA to home in on a disease-causing pathogen. The second half of the study focused on a new, highly targeted NGS system called the RPIP (Respiratory Pathogen Infectious Diseases/Antimicrobial Resistance Panel). This method employs capture probes (fragments of single-stranded DNA with structural parallels to the DNA of specific pathogens) to greatly improve searching ability.

The results of the study were striking if a bit inconclusive. Both the RPIP and metagenomic NGS approaches were virtually identical to SOC when it came to identifying specific pathogens in the respiratory samples. When reached for comment, Simner said that both of the NGS techniques under study showed great promise for respiratory disease diagnostics and medial diagnostics in general. However, more research and development will be necessary to refine these techniques before they can be viewed as legitimate replacements for established SOC techniques.

Through technological advancement, researchers may be able to address the inconsistencies that may be the single biggest problem with NGS diagnosis processes. The Johns Hopkins study determined that the effectiveness of RPIP and metagenomic NGS varied significantly depending on the specific type of pathogen sought. While NGS processes were quite good at identifying viruses, they were inept when it came to identifying fungi. Results for bacteria were close to those of SOC diagnostics when pathogen numbers were large but decreased rapidly as the number of pathogens decreased.

Despite the study’s determination that SOC diagnostics still reign supreme, it also pointed to the tremendous promise that NGS diagnostics hold. Although NGS cannot yet detect all the organisms that SOC can, both RPIP and metagenomic NGS methods successfully identified organisms that SOC methods would have missed.

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