(a nice logo from a deprecated database, and absolutely nothing to do with this review …)
Robest Hurst,
BMC Urology 2009, 9:1
Published in BMC Urology is a wonderful, well written and provoking commentary on the development of biomarkers. The author describes the state-of-the-nation in biomarker development for the characterisation and classification of bladder cancer, and argues that enough-if-enough and now is the time for the biomarker development field to wake up and start developing useful biomarkers. While the article has absolutely nothing to do with bioinformatics (apart from a little reference towards algorithms in the final sentences), I know that many bioinformaticians are working in the biomarker development field.
Bladder cancer is currently monitored most effectively using cystoscopy – an invasive method, but one which is suggested to have a 95% sensitivity. One issue with bladder cancer is that there is an insiduous recurrence; and treated patients of often superficial cancers develop aggressive invasive disease, and this kills 50% of patients… The need for a biomarker is clear, with >95% sensitivity from a non-invasively sampled biosample, and patients would likely be more compliant with post-treatment follow-ups. The issue is reiterated several times that sensitivity of prognostic markers of disease progression is key.
Stick-based protein assays have been developed for analysis of urine samples, but suffer from <70% sensitivity – the author describes “betting lives on a test with worse sensitivity than the gold standard“, and further questions the value of the tests based on the fatal consequence of false-negatives and the cost of follow-up on the false-positives.
I am really happy to read the author’s dissection of microarray and proteomic-based biomarker discovery. The author acknowledges the naive nature of magically robust, sensitive and specific biomarkers from the results, and states the unpredictable nature of the homeostatic ripples that move outwards from a peturbation within interconnectded network of cooperating proteins. The promise of biomarkers is therefore dismissed with the statement that “the probability of finding a single biomarker with the requisite sensitivity and specificity is vanishingly small“. Does this mean that we can pack our bags instead and go home?
Fortunately not! Hurst instead argues that the combination of biomarkers from existing studies into practical panels is the way ahead instead of yet more studies searching for the elusive individual biomarker. With the acknowledgement that all cancers are largely unique, and that thousands of samples would be required to obtain robust samples, the emphasis should be placed on the selection of biomarker panels from small numbers of assays that are largely independent, but which are relective of the overall phenotype, and the historical approach of modelling causality within the system should be abandonned; the leads of the re-boot within the title! Most encouragingly the author also states that “the search for candidate biomarkers needs to be divorced from the validation in clinical populations” and advocates the development of biomarker panels in surrogate model systems with cancer patient specimens as a validative tool rather than a discovery tool.
This stuff is common sense, obvious and clear to bioinformaticians, but not always to the scientists and clinicians closer to the patient. This is a well written article and should be distributed widely; the final sentence really summarises it well “the intelligent development of biomarkers truly is a problem in systems biology.”
) and it just sort-of waited and nothing happened.
