Biomarkers in limbo (for now)

gene-arrayThere has been no small amount of hand-wringing at the various the biomarker conferences over the last year or two and the predominant issue has been a familiar one to anybody who is engaged in biomarker research or drug discovery – why does the life science industry continue to see diminishing returns from ever greater investment? There is a pervasive feeling of the biomarker field having reached some kind of intellectual impasse that requires a new direction, and so it came as no surprise to me that given the opportunity to vent about the issues that concern them, this was also the dominant topic for the scientists who spoke at the various panel discussions that I attended.

In spite of the escalation of activity and investment in the field, many of the biomarkers in current use are years or even decades old and most of them have not been substantially improved upon since their discovery. Much more useful than the current PSA test for prostate cancer for example, would be a test with which a physician could confidently triage prostate cancer patients into groups of those who would probably require medical intervention and those whose disease is relatively quiescent and who would likely die of old age before their prostate cancer ever became a real health problem.

The deluge of biological data being generated, particularly in the various ‘omics fields is undoubtedly valuable, yet it continues to accumulate at rates faster than it can be assimilated into our current understanding and its burgeoning scale confronts researchers ever more forcibly with the reality of the vast complexity of biological systems. In the biomarker field, the dominant approach of searching for correlative disease markers largely sidesteps the issue of the underlying biological complexity, and while this approach has enjoyed considerable success, the limitations of being forced to work with correlative data rather than causative knowledge are starting to become apparent.

Cancer biomarkers are a prime example of this limitation. Since we don’t yet have a good handle on the subtle chains of cause and effect that deviate a cell down the path towards neoplasia, we are forced to wait until there are obvious alarm bells ringing, signaling that something has already gone horribly wrong. The ovarian cancer marker CA125 for example, only achieves any significant prognostic accuracy once the cancer has already progressed beyond the point at which therapeutic intervention would have had a good chance of being effective. To use an analogy from the challenge of complexity in the behavioral sciences – broken glass and blood on the streets are the “markers” of a riot already in progress but what you really need for successful intervention are the early signs of unrest in the crowd before any real damage is done.

Some of the causative, systems-based  approaches to disease at the cell signaling level that were highlighted at these conferences, hold great promise for moving beyond the impasse in which the biomarker field now finds itself. The mapping of genotypic and phenotypic data onto cell signaling networks is a step in the right direction insofar as it attempts to establish an underlying causal and biological connectivity between the observed correlates, although the pathway maps that it yields are hardwired, static, representations of cellular pathways that are in reality dynamic and plastic networks. There is real cause for optimisim in the potential evolution of biomarker research away from the purely correlative approach that currently dominates the field, towards more causative approaches built upon knowledge of the behavior of the underlying biological systems.

Finally, it was telling that despite a general consensus that new approaches are needed in the biomarker field which address the issue of turning the wealth of biological data into real knowledge – the great majority of the presentations and posters at the various biomarker conferences were still focused upon advances in data gathering of one kind or another. To be fair though, it should not be expected that such a significant paradigm shift in the biomarker field will happen overnight. Real progress however, will require decision makers in the pharma and biotech industries to step outside of their comfort zones somewhat and to invest in these new approaches. Throwing ever greater sums of money at the problem using the same approaches is clearly not working and as the old adage goes – doing what you’ve always done, you’re only going to get what you’ve always gotten.

The author Gordon Webster, has spent his career working at the intersection of biology and computation and specializes in computational approaches to life science research and development.

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