New model to identify biomarkers in patients with COVID-19 and other diseases

Researchers who recently developed a mathematical model indicating why treatment responses vary widely among people with COVID-19 have now used the model to identify biological markers linked to these different responses.

The team, led by scientists from Massachusetts General Hospital (MGH) and the University of Cyprus, note that the model can be used to provide a better understanding of the complex interactions between disease and response and can help clinicians to provide optimal care for diverse patients.

The book, published in EBioMedicine, was initiated because COVID-19 is extremely heterogeneous, meaning that disease following SARS-CoV-2 infection ranges from asymptomatic states to life-threatening states such as respiratory failure or acute respiratory distress syndrome (ARDS), in which fluid builds up in the lungs.

“Even within the subset of critically ill COVID-19 patients who develop ARDS, there is substantial heterogeneity. Significant efforts have been made to identify ARDS subtypes defined by clinical features or biomarkers,” explains co-lead author Rakesh K. Jain, PhD, director of the EL Steele Laboratories for Tumor Biology at the MGH and Andrew Werk Cook Professor of Radiation Oncology at Harvard Medical School (HMS). “To predict disease progression and personalize treatment, it is necessary to determine associations between clinical features, biomarkers and underlying biology. Although this can be done in many clinical trials, this process is time consuming and extremely expensive.

As an alternative, Jain and his colleagues used their model to analyze the effects that different patient characteristics have on outcomes after treatment with different therapies. This allowed the team to determine the optimal treatment for different categories of patients, to reveal the biological pathways responsible for different clinical responses and to identify markers of these pathways.

The researchers simulated six types of patients (defined by the presence or absence of different comorbidities) and three types of therapies that modulate the immune system.

Using a new treatment efficacy scoring system, we found that elderly and hyperinflamed patients respond better to immunomodulation therapy than obese and diabetic patients..”

Lance Munn, PhD, Study Co-Lead Author and Corresponding Author, Deputy Director of Steele Labs and Associate Professor, Harvard Medical School

“We also found that the optimal time to initiate immunomodulation therapy differs from patient to patient and also depends on the drug itself.” Certain biological markers that differed based on patient characteristics determined the optimal time to initiate treatment, and these markers indicated particular biological programs or mechanisms that impacted a patient’s outcome. The markers also matched clinically identified markers of disease severity.

For COVID-19 as well as other conditions, the team’s approach could allow researchers to enrich a clinical trial with the patients most likely to respond to a given drug. “Such enrichment based on prospectively predicted biomarkers is a potential strategy to increase the precision of clinical trials and accelerate the development of therapies,” says co-lead author Triantafyllos Stylianopoulos, PhD, associate professor at the University of Cyprus .