Patient Health Benefit
Many articles that question current results make good suggestions and should be taken as a measure of the best practices for the field and as measures of study quality.
First, an adequate number of subjects to represent the entire patient population eligible for the test should be required in the study. Many of the current studies include as few as 20-80 subjects, wherein as few as 25% of the patients have the pathology or outcome under investigation. Along with others, I suggest that 200 subjects (of which at least 10-20% should exhibit the phenotype or outcome under investigation) is a minimum number to identify biomarkers that may hold true when larger populations are examined.
Besides study size, the second aspect to be considered is suitable patient diversity. Too many published studies use patient populations that poorly represent the likely spectrum of patients who would be real-world candidates for the test. There are several causes for low patient diversity, one of the most common being the reliance on one or two clinical centers and a single clinician to make the diagnostic decision about the patient’s disease status. This type of geographic and diagnostician bias skews the biomarker, which results in a lack of widespread utility. I recommend that at least 5-10 different clinical centers and diagnosing clinicians be used to assemble the patient cohort.
Third, proper estimation of performance is key, as many published studies inadequately estimate biomarker future performance. A solution would be to insist on more statistical rigor from the primary investigators and to clearly indicate that split-sample cross validation of the training dataset be required for publication.
Fourth, there must be an accurate demonstration of statistical significance. Almost all publications describing biomarkers fail to test statistical significance using the class permutation test. Whole-genome microarrays measure tens-of-thousands of analytes, as do several proteomic techniques. A test that measures many analytes with too few patients can easily derive biomarkers that are not based on biology but arise due to random variations.
Fifth, I encourage workers to apply the various algorithms available as it would improve results. Simple biomarker derivation approaches using techniques such as gene list rankings by the t-statistic, correlation coefficient, nearest centroid, and other analysis of variance and correlation methods are common.
Several more powerful and typically successful methods are available, such as linear discriminates, support vector machines (linear and nonlinear), and neural nets.
Successful development of a biomarker that meets these five criteria would allow the biomarker to be considered an early research finding, or a research biomarker.
Sixth, to validate this biomarker further, the investigator must take another step. Investigators should forward validate using a new cohort of patients of similar size as or larger than that used in the biomarker development phase. Importantly, this group should not include patients that were part of the biomarker development group, and the new patients preferably should come from different clinical centers and be diagnosed by different physicians than those in the biomarker development.
Assuming that the research biomarker continues to show acceptable performance in the forward validation test, then the biomarker can be considered as a probable clinical biomarker.
The seventh step in biomarker validation is to conduct clinical trials that aim to demonstrate patient survival benefit, or outcome benefit, resulting from the application of the test, as compared to current standards of medical practice.
If this last test is successful the biomarker may be considered a clinical benefit validated biomarker.