System Biology-Based Prognostic Biomarkers of Clinical Complications in Acutely Injured Patients

In the September 13^th issue of PLoS One (link), John D. Storey and colleagues report on inflammation-related gene expression signatures associated with differential clinical outcome in acute trauma patients.  Specifically, the authors analyzed the expression of inflammation-related genes in 168 blunt-force trauma patients over a 28-day period.  The genes and gene pathways that clustered differently between patients’ clinical outcome subgroups (based on Marshall multiple organ failure clinical score) were assembled into predictive modules of clinical outcomes.  Of particularly interest, the down-regulation of MHC II expression within 48 hours of trauma and up-regulation of p38-MAPK within 100 hours of trauma were particularly robust independent predictors of negative clinical outcome in this patient sample.

Considering that up to 60% of late trauma mortality is caused by infections, sepsis, and multiple organ failure multiple organ, the management of these inflammation-related complications remains a major unmet medical need.  In particular, the ability to predict the individual patient clinical trajectory early during trauma treatment remains a significant challenge for the medical community.  Therefore, the prospect of using gene expression as a prognostic biomarker to manage the care of trauma patients is of particular significance.

Thierry Sornasse for Integrated Biomarker Strategy

Ridge Diagnostics Depression Blood Test Biomarker

The cover of the August 2011 issue of Psychiatric Times features Ridge Diagnostics Depression Blood Test Biomarker Technology.

As I wrote in an earlier post (System biology derived biomarker: fight complexity with complexity), I am convinced that the development of diagnostic biomarkers has a lot to gain from using a system biology approach (i.e. biomarkers based on multiple independent factors). In many cases, relying on a single factor to diagnose or predict complex diseases has turned out to be an intractable problem.  Here, I would like to attract your attention to another example of a system biology based disease diagnosis biomarker for Major Depressive Disorder (MDD). Ridge Diagnostics, based in San Diego, has developed a new blood-based, multi-factor diagnosis for MDD (MDDScore) that is intended to assist the clinician in diagnosing and monitoring patients.  Currently, the diagnosis of MDD is mostly based on subjective criteria (i.e. interview and observation by the clinician) which represent the distal output of a complex pathobiology. By providing an objective means to diagnose patients, MDDScore should shed a new light on the fundamental biological imbalances that cause MDD.

Thierry Sornasse for Integrated Biomarker Strategy

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