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.

Fig 1. Disease progression and dynamic network biomarkers.

(A) Three states during a disease progression. Clearly, there are significant differences between normal and disease states in terms of molecular expressions, and that is why traditional biomarkers can identify the disease state based on the differential information between them. But generally there is no significant difference between normal and pre-disease states, and thus traditional biomarkers may fail to detect the critical state for correctly predicting the disease. (B) Flowchart for calculating the composite index of single-sample dynamic network biomarkers (sDNB), which can detect the pre-disease state based on the three statistical conditions, rather than the differential expressions. Reference samples are required to produce the reference data. The distribution of every gene in terms of expression can be obtained from the reference samples, and the absolute value of the difference between a gene’s expression in an individual sample d and the average value of the gene’s expression in the reference samples is defined as the single-sample expression deviation (sED) of the gene for sample d. The Pearson correlation coefficient (PCC) between two genes in the reference samples is defined as PCC_n. After the expression profile of sample d is added to the reference samples, the new correlation coefficient between the two genes can be obtained as PCC_n+1. The difference between PCC_n and PCC_n+1 can be regarded as the single-sample PCC (sPCC) between the two genes for sample d. The detail computation procedure of the sDNB score I_s is described in Fig 2.

Fig 2. Flowchart of the algorithm for identifying potential sDNB in a single sample.

sED and sPCC can be calculated by the method shown in Fig 1B. The hierarchical clustering algorithm was employed in the clustering process, and the value of 2 minus the absolute value of sPCC was used as the distance between genes for the hierarchical clustering algorithm.

Fig 3. Quantifying the critical states for the influenza virus infection data [8].

(A) Line chart for early-warning signals in all symptomatic adults. (B) Line chart for early-warning signals in all asymptomatic adults. (C) Table of sDNB diagnoses and clinical diagnoses for all adults and samples.

Fig 4

Quantifying the critical states for metastasis in three cancers: (A) LUAD, (B) STAD, and (C) THCA.