One way of analyzing the apparent failures in diagnostics is by classifying them into three distinct categories (Figure ). The first category of failing biomarkers includes those that are based on fraudulent publications. These are quite rare and, despite some highly-publicized cases [3
], fraud is not the major reason for failing biomarkers.
Summary of reasons for biomarker failure to reach the clinic. Fraud is a very rare reason for biomarker failures; most biomarkers represent true discoveries but their clinical characteristics are not good enough to be used at the clinic.
The second and largest category of biomarkers that never reach the clinic (and counted as failures) includes those that have been discovered and validated by using robust and reliable techniques (true discovery). These biomarkers have successfully gone through the process of discovery and validation, with reproducible and concordant results between studies, from early to late stages. However, these biomarkers fall short in their ability to contribute decisively to patient care, except for providing some incremental, but clinically not essential, information. For example, the urokinase plasminogen activator/plasminogen activator inhibitor 1 (uPA/PAI 1) combination of biomarkers has long been regarded as a prognostic indicator of breast carcinoma, and many retrospective and prospective studies and meta-analyses have confirmed their prognostic value [4
]. However, they have not been widely adopted, especially in North America, despite availability of excellent ELISA methodologies for their measurement, because clinicians do not seem to find this information necessary in deciding how to treat their patients. Rather, they decide on treatment options without this information, thus saving costs. Clinicians usually prefer to over-treat some patients, instead of using prognostic biomarkers with less than perfect prediction. Imperfect prognostic biomarkers could spare a fraction of patients from over-treatment (true positives), but at the cost of not treating some patients who could benefit from treatment (false negatives). Another example is the tumor suppressor p53. A search in PubMed for the term 'p53 AND breast cancer prognosis' identifies 1470 papers, with the vast majority confirming the prognostic value of p53, despite its sparse use at the clinic, if any, due to the reasons mentioned above (imperfect or weak prognostic value).
Other examples in this category include those biomarkers that have been discovered and validated thoroughly by industry, and although found to have some use in clinical prediction, the strength of their predictive ability is not enough to persuade clinicians to use them, or clinical practice guideline developers to recommend them. An example is the novel ovarian cancer biomarker, B7-H4 (discovered by diaDexus Inc., San Francisco, CA, USA), which was validated for its ability to diagnose ovarian cancer [5
]. Recently, an independent group confirmed the diagnostic ability of this biomarker, but also demonstrated that it is not better than the classical biomarker, CA125 [6
]. Consequently, the company that discovered it, in the absence of a clear clinical utility, decided not to market it. It is quite expensive to conduct the necessary clinical trials to obtain approval from the Food and Drug Administration (FDA). There are numerous examples of this sort in the literature, that is, of reasonable and working biomarkers that fall short of fulfilling a clear clinical need and thus unlikely to be profitable if marketed.
There are also numerous examples of biomarkers which have good sensitivity (>70% at 90% to 95% specificity) in detecting late carcinoma but poor sensitivity in detecting disease in asymptomatic patients, especially at the extremely high specificity required for screening (for example, >99.5%, as is the case with ovarian carcinoma) [6
]. Consequently, none of the available ovarian cancer biomarkers are suitable for screening, and it may be unlikely that we will find any that can perform at these clinically dictated and highly demanding specifications (for example, >80% sensitivity for early and asymptomatic disease, at ≥ 99.5% specificity; to achieve a reasonable positive predictive value of ≥10%).
From this discussion, it can be concluded that a very large number of candidate biomarkers have been discovered, and have been confirmed by reliable methods to provide diagnostic, prognostic or predictive information in certain groups of patients. Unfortunately, this information could not be translated into action for better patient management and outcomes. So, work on biomarkers is continuing. However, these biomarkers are not recommended in practice guidelines for use in the diagnosis or treatment of cancer [7
], despite their statistically significant (but clinically not useful) diagnostic, predictive or prognostic information. Some examples of well-validated biomarkers and their possible reason of failure at the clinic are outlined in Table .
Why well-validated biomarkers still fail to reach the clinic?
There is another group of biomarkers which may initially look highly promising (or even revolutionary) but for which shortcomings have been identified, either at the discovery or validation phase. For example, in my previous commentary [8
], I identified pre-analytical, analytical and post-analytical shortcomings of many published biomarkers, which could invalidate the original performance claims. This group of biomarkers should be considered as 'false discovery'. They will not reach the clinic because the original performance claims could not be independently reproduced in subsequent validation studies.