A "toolbox" to help diagnose complex diseases
Medical scientists know the root causes of a variety of diseases, but one key aspect continues to elude them: the abnormal interactions that many diseases exhibit.
Now, using computational biology, a team of Canadian and U.S. researchers has succeeded in analyzing large volumes of data to deconstruct and reconstruct the networks of interactions leading to a disease.
By doing so, they’ve been able to identify new phenotypes that can help clinicians optimize therapeutic strategies tailored to individual patients.
The research was led by Université de Montréal pharmaceutical science professor Morgan Craig with the collaboration of collagues at Harvard University, Stanford University and the University of California, San Francisco.
Their research appeared this month in the Cell Press journal Patterns.
A "toolbox" for diagnostic assistance
Complex disorders, due to their multifactorial etiology, require new tools capable of leveraging large quantitative data sets to sketch out the equivalent of a "food web" in ecology – that is, a network of interactions between various cells and proteins.
Constructing this network allows us to study the differences between healthy and sick individuals and to identify, on one hand, the phenotypes at work in the disease and, on the other, potential therapeutic strategies.
“Here, we applied these quantitative approaches to cyclic thrombocytopenia (CT), a very rare and complex blood disorder,” explained Craig, a researcher at CHU Sainte-Justine children’s hospital in Montreal.
“Our study consists of three subjects with biomarkers for CT, in which multiple cells and proteins undergo abnormal oscillations, leading to a deficient platelet concentration in the blood, which corresponds to the clinical picture of the disease,” she said.
“However, one of the subjects had atypical biomarkers.”
Added the study’s first author, Harvard’s Madison Ski Krieger: "We then applied a collection of mathematical techniques (empirical dynamics), which, based on longitudinal data, allowed us to dissect the dynamics of the abnormal physiological interactions in the disease in each patient.
“The development of this diagnostic toolbox has helped us validate a plausible therapeutic intervention.”
Distinguishing multiple phenotypes
“What's really new here is that our approach allows us to distinguish the multiple phenotypes of the disease by examining its pathophysiological mechanisms, regardless of whether they have the same clinical manifestation,” said Craig.
“In other words, although the symptoms of a disease may be the same, the mechanisms responsible for that disease may differ.”
This would explain why some drugs do not work in certain patients, she said.
“What's exciting is that this quantitative approach has the potential to be applied to multiple complex disorders and populations to improve our understanding of a given disease and guide clinicians toward personalized therapeutic targets.”
The team is continuing its research in order to study drug resistance in cancer and to better understand the mechanisms underlying COVID-19.
