System biology derived biomarker: fight complexity with complexity

The biomarker development community suffers from a strange disease: “single factor biomarker compulsive focus”.  This condition manifests itself by a strong propensity to try to identify and develop biomarkers based on a single analyte or factor.  While it seems that dealing with a single factor at a time would be a neat and effective approach, it suffers from a major flaw: biological processes are complex events and can rarely be described by a single biological factor.  First, a single biological process usually can involve a great number of biological factors with varying degree of connectivity.  Second, many biological factors can be involved in multiple apparently unrelated biological processes.  Thus, a biomarker based on a single biological factor may show significant statistical association with a specific biological process under controlled conditions but, in an uncontrolled real-world situation, its predictive value may vanish due to the confounding effects of co-morbidities and/or concomitant drugs.  Although it is theoretically possible to circumvent this limitation by systematically identifying and controlling for confounding factors, I would argue that developing biomarkers based on multiple factors (i.e. derived from a system biology approach) could prove more effective.

First, multi-factor biomarkers provide a means to capture the true complexity of biological processes.  As discussed above, most pathobiological processes affect multiple systems through time and space.  Capturing this complexity through the identification of multiple independent biological factors is more likely to yield a specific and unique signature of pathobiology.  Second, there is a statistical benefit to multi-factor biomarkers. 

Imagine that you want to predict a certain phenotype (e.g. disease, response to treatment) based on single factor biomarker.  While this biomarker shows significant statistical association with the phenotype, its intrinsic distribution leaves a zone of predictive uncertainty between the high-end of the control group (blue) and the low-end of the target group (green) [see fig. 1].

Fig. 1

Now, imagine that you have identified a second independent factor with the same properties (i.e. certain degree of overlap).  By combining these two factors into a multi-factor biomarker, the overlap between the control and target groups is abolished, removing the prediction uncertainty of each factor alone (see fig.2).

Fig.2

This statistical advantage resulting from combining 2 factors scales to greater numbers of factor combinations as long as the combined factors are independent (i.e. minimal covariance).

This approach has been successfully used by Genomic Health in the development of their oncotype DX diagnostics for the risk of recurrence in breast and colon cancer patients that would justify the use of chemotherapy.  These 2 diagnostics are based on the quantitative analysis of multiple RNA molecules that yield an integrated prediction of the risk of recurrence (see Genomic Health Laboratory Videos for an overview of the process).  Similarly, Dr. David Eidelberg has used multi-factor functional brain imaging to derive specific patterns of brain activity associated with different stages of neurodegenerative diseases such as Parkinson’s disease (Reference: Metabolic Brain Networks in Neurodegenerative Disorders: A Functional Imaging Approach).

Thierry Sornasse for Integrated Biomarker Strategy