Diagnostic Discoveries Through mNGS

Recently, we reported on a seven-year study evaluating the use of metagenomic next-generation sequencing (mNGS) testing for diagnosis of central nervous system (CNS) infections. Here, Brad Murray, CEO and co-founder of Delve Bio and co-author of the study, outlines the details of this research.

What were the most surprising findings from the study on mNGS testing for CNS infections, and how do they change our understanding of diagnostic testing for these infections?
 

While we knew that mNGS could detect more pathogens than traditional testing, we were pleased to have real-world evidence of how much greater the diagnostic yield is compared to traditional methods, as well as data on the diversity of pathogens found in cerebral spinal fluid (CSF) – mNGS detected nearly 800 unique organisms in CSF. Across the nearly 5000 samples, mNGS detected 34 percent more pathogens than all the other direct-detection methods detected, and 22 percent more than all standard of care. In some of those cases, mNGS was able to find a pathogen even when all other microbiological testing was negative.

Clinicians tend to approach diagnostic testing by developing a hypothesis and then choose tests based on that hypothesis. All too often, tested hypotheses can come back negative because the hypothesis wasn’t correct, the test wasn’t sensitive enough, or the test was ordered too late. This really calls for pulling mNGS earlier in the clinical workup and stepping away from hypothesis-based testing, particularly in very complex cases.

How does mNGS testing compare with traditional methods in terms of accuracy and diagnostic yield, particularly for CNS infections caused by rare or unexpected pathogens?
 

mNGS delivered the highest diagnostic yield of any test, identifying more pathogens than all other methods of direct organism detection (including culture, antigen testing, and PCR) combined. Additionally, it had the highest sensitivity and specificity in comparison to testing either through direct detection methods like culture and PCR, or through indirect methods such as serology. With a positive predictive value of 97.1 percent and negative predictive value of 92.3 percent, clinicians can be confident in detecting pathogens in a sample, as well as confidently confirming the lack of pathogens in a sample – both results can radically impact the treatment course.

Of the approximately 800 different pathogens detected, 45.5 percent were DNA viruses, 26.5 percent were RNA viruses, 16.6 percent were bacteria, and 11.4 percent were fungi and parasites. Not only were rare pathogens detected, such as St. Louis encephalitis virus or Balamuthia mandrillaris, but more common pathogens like Hepatitis C virus were also detected, highlighting the need for a broad, agnostic test for all pathogens in CSF. Often patients in the study had undergone 20–40 different microbiological tests before a clinician finally ordered mNGS. Earlier testing could have saved not only time and resources, but also time spent by patients in intensive care units, which represented 38.7 percent of the cases.

Given the high specificity of mNGS, can you discuss the implications for clinical decision-making of positive and negative mNGS test results?
 

With high positive and negative predictive values as mentioned previously, clinicians can be confident in detecting a pathogen in a sample, as well as confidently confirming the lack of pathogens in a sample – both of which can radically impact the treatment course. All too often, broad-spectrum, expensive, and toxic antimicrobials (viral, fungal, or bacterial) are prescribed ahead of knowing which pathogen is present. A positive result can provide clinicians with critical information on what antimicrobials to prescribe and tailor treatments to the actual pathogen present.

Arguably equally important, a negative result can also guide antimicrobial treatment by enabling clinicians to de-escalate antimicrobials that are often empirically and broadly applied. A negative mNGS result can support diagnostic stewardship programs that aim to tackle antimicrobial resistance (AMR) by providing data that leads to lowering the use of antimicrobials. 

Additionally, in meningitis/encephalitis cases, clinical presentation can be complex with many confounding symptoms – clinicians often are deciding between multiple treatment pathways and choosing the right pathway has significant treatment implications. For example, if a patient has suspected autoimmune encephalitis, powerful immunosuppressants are prescribed; however, if the patient has an undiagnosed infection, this immunosuppressant can hamper a patient’s ability to fight an infection and often leads to deadly outcomes. A rapid, negative mNGS result quickly allows clinicians to confidently prescribe immunosuppressants without the fear of an infection flare. 

You noted that mNGS identified certain infections that were undetected by other methods. Can you elaborate on how mNGS adds value in diagnosing infections in immunocompromised patients?
 

The cohort in our study included 35.8 percent immunocompromised (IC) patients and the mNGS positivity rate for this group was higher. There are limitations with other testing in IC patients due to their limitations in mounting a detectable immune response – typical markers of infection, such as elevated white blood cell count, are not reliable in IC patients. Additionally, IC patients often suffer from less common pathogens that may be missed in standard microbiological testing. mNGS detects any nucleic acids in a sample, regardless of white blood cell count, and with broad, agnostic detection, mNGS can detect the uncommon pathogens afflicting IC populations.

What challenges did your team encounter in implementing mNGS testing over such an extended period?
 

I think the biggest challenge is not the technology itself, but rather being able to access it in a way that is clinically actionable. When UCSF started doing this, sequencing and interpretation took a long time. The team at UCSF worked to get turnaround times down to two weeks, but that is still too long to help most patients. That’s why a significant focus of our effort has been in creating a clinically actionable service – including a turnaround time of 48 hours from when we receive a sample, and robust support from infectious disease experts on treatment implications based on findings.

With the return of international travel and evolving pathogen landscapes, what role do you foresee for mNGS in future outbreak response and pathogen surveillance?
 

mNGS already plays an important role in outbreak response and pathogen surveillance. Pathogens continue to change, with the rate increasing alongside climate changes. We’re seeing evolutions in pathogens that change clinical presentations, and, just as importantly, we’re seeing pathogens expand into new areas. For example, pathogens that cause fever illnesses that were once limited to the Amazon may start appearing in the US. mNGS is an important guard against this because it doesn’t ask a clinician for a hypothesis – you take one sample and screen it for everything, including the unlikely results.

mNGS was key in managing a recent Fusarium outbreak in the US and Mexico. It identified and tracked the fungus to contaminated medical supplies at clinics in a specific region, affecting patients who traveled to Mexico for medical tourism. mNGS has also been used to trace hepatitis infections in transplant recipients who received organs from the same donor.

How is the cost of mNGS testing, which remains a barrier for some institutions, evolving?
 

The cost of sequencing and data processing is decreasing, improving affordability and accessibility. While advancing technology will help reduce costs, it’s equally important to recognize the value of mNGS. Diagnosing meningitis or encephalitis can take days to weeks, with ICU costs exceeding $16,000 per day. Our study showed patients often underwent 20–40 microbiological tests before diagnosis. mNGS could replace most of these tests, streamline clinical workups, and shorten hospital stays, significantly reducing the costs of managing complex infections.

What advancements in mNGS technology are needed to make it a routine part of CNS infection diagnosis and management?
 

mNGS shifts the approach to infectious disease diagnostics. Instead of identifying the pathogen first and testing second, clinicians can start with, “I have a sick patient – let’s quickly determine if it’s infectious.” Educating clinicians on how mNGS improves outcomes is key to making it a standard of care. Equally important is having commercial services that provide rapid results and top-tier clinical support, which is what we’re currently working on.