Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

Abstract

Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to “diagnose disease”, sDNB is based on the information of differential associations, thereby having the ability to “predict disease” or “diagnose near-future disease”. Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.

Author summary

The concept of dynamic network biomarkers (DNB) was proposed for detecting the critical state or tipping point of a complex disease (a pre-disease state immediately preceding the disease state), and has been applied to study the mechanism of cell fate decision and immune checkpoint blockade. But DNB cannot be used to identify the critical state or tipping point for a single patient because evaluating DNB for critical state required the data of multiple samples. The proposed method can identify the critical state of a complex disease for a single patient by implementing the concept of DNB. This method not only can be applied to detect the critical state or tipping point of a single sample, but also can be used to study the mechanism of complex disease at a single sample level. The ability of accurately and efficiently identifying the critical state for a single sample can benefit the development of personalized medicine.