Scientists are developing tests that could one day be used to predict how a patient’s disease will progress and even how well they will respond to treatment.
An international team led by researchers at Imperial College London has already tested the method (called VeloCD) and successfully predicted patient outcomes for a range of health conditions, including infections and the likelihood of chronic disease progression.
In a proof-of-concept study, we were able to confidently predict whether children with acute fever were more likely to recover or worsen, and whether healthy adults were more likely to develop influenza or COVID-19 after being exposed to the virus.
The method, based on cutting-edge bioinformatics technology, was also able to predict how well patients with inflammatory bowel disease would respond to a course of treatment by simply analyzing blood samples after the first treatment.
By measuring key markers in the blood, which reveal gene expression levels in response to disease, they can be used to predict the likely trajectory of the disease.
According to the Imperial team, the findings demonstrate how VeloCD can be used to make clinically meaningful predictions of future clinical conditions. Their findings were published today in the journal nature communicationslaying an important foundation for future prognostic testing.
They have applied for a patent for the method and believe it could be developed into a commercially available test for use in hospitals.
Ultimately, the hope is to use this approach to quickly triage patients, allowing clinical staff to identify who needs further care and who can be safely treated and sent home.
We believe that this type of testing is extremely beneficial for patients and medical staff. Providing doctors with tests that can predict the course of a disease could help triage patients more quickly and get the right treatment to the right patient at the right time. ”
Professor Aubrey Cunnington, Head of Infectious Diseases at Imperial College London, senior author of the study
structure
When we get sick, combinations of genes are switched “on” and “off” accordingly. This produces RNA markers that can be detected in the blood. Previous research has shown that patterns of markers can be used to identify the cause of illness, such as whether a fever is caused by a bacterial or viral infection.
In this study, the team used a method called RNA velocity. It was originally developed to study single cells. The Empire-led team has adapted and enhanced the approach of testing whole blood samples to see if markers may indicate prognosis. How the patient might respond to treatment, whether it might get better or worse.
This allows researchers to see not only which genes are turned on or off, but also whether their activity is increasing or decreasing, without repeated measurements over time. From there they could learn information about the person’s future clinical condition and the course of the disease.
To take advantage of this complex data, researchers developed computational methods to predict whether changes in gene expression patterns in one individual’s blood are similar or dissimilar to changes in other individuals with outcomes of interest (e.g., severe disease or mild self-limiting disease).
Dr Claire Dunican, Research Fellow and Bioinformatician in the Department of Infectious Diseases at Imperial College London, who developed, adapted and tested the method, and lead co-corresponding author of the study, said: “Our approach uses state-of-the-art methods to treat patients’ bodies in the moment. “We can get a glimpse into a patient’s future based on how they are responding to the disease. The patterns of gene expression we see in the blood provide clues about what’s going on. By identifying key patterns, we can essentially make predictions.” Disease course – You can actually know not only where the person is now, but where they will be in the next few hours or days, and how they will respond to treatment. ”
testing the concept
To test this approach, the team looked at real-world data from several large-scale studies, including the EU-funded PERFORM study. This included blood samples from around 400 children hospitalized with fever in nine European countries.
Whole blood RNA sequencing showed that more than 2,300 markers generate reliable patterns of mild, moderate, and severe disease. By focusing VeloCD on a subset of just 59 of these markers, the team was able to predict whether a child was likely to progress to mild, moderate, or severe disease. Importantly, this approach allowed us to alert those most likely to become seriously ill and require intensive care.
In another test of this approach, the team used VeloCD to predict whether people would develop influenza or COVID-19 after being exposed to the virus. To do this, the researchers drew on a wealth of data generated by Imperial College London’s ground-breaking Human Challenge programme. In this program, healthy adults were exposed to influenza or SARS-CoV-2 under controlled conditions. Blood samples taken early in the study (day 2) could be used to accurately predict whether a person will become infected in the future, even before a PCR test confirms the infection.
In further validation, the team demonstrated the potential of VeloCD as a tool to highlight complications of HIV and tuberculosis, as well as response to treatment of inflammatory bowel disease, based on RNA markers in the blood.
Dr. Mirshini Kaforou, Associate Professor of Bioinformatics in the Department of Infectious Diseases at Imperial College London and co-author of the study, said: “Clinical teams often have to make informed decisions based on limited evidence within a limited time frame. “Tests based on this approach, which rely on state-of-the-art bioinformatics techniques to decipher dynamic information in a person’s blood, have the potential to be transformative, allowing us to detect disease in advance and predict a patient’s health status in the clinic.” Whether you are on the road to recovery or deterioration, we provide the foresight necessary to provide truly personalized and proactive care. ”
Professor Mauricio Barahona, Professor of Biomathematics, Department of Mathematics, Imperial College London, and co-author of the study, said: “This is one of the most rewarding examples of true cross-disciplinary collaboration, where the application of what was originally a very complex set of mathematical ideas linking high-dimensional geometry and mechanics to biomedical data promises to help us understand the complex manifestations associated with disease progression in individuals.”
The researchers say more work is needed to develop and validate predictive tests based on this approach. However, if research is successful through an intensive development program, pragmatic clinical trials could be available within as little as five years.
The team has made the VeloCD tool available on GitHub.
The research was carried out in collaboration with a number of global partners, including key collaborators from UCL, the University of Cape Town, Queen Mary University of London, the PERFORM consortium and Imperial College London’s Human Challenges programme.
This research was supported by the European Union’s Horizon 2020 research and innovation programme, the UK Research and Innovation (UKRI) Medical Research Council (MRC) and the Engineering and Physical Sciences Research Council (EPSRC), the UK Department for International Development (DFID) and others.
This research was also supported by the NIHR Imperial Biomedical Research Center, a translational research partnership between Imperial College Healthcare NHS Trust and Imperial College London.
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Reference magazines:
Dunican, C. Others. (2026). Predicting disease trajectory using whole blood RNA velocity. Nature Communications. DOI: 10.1038/s41467-026-71685-5. https://www.nature.com/articles/s41467-026-71685-5
