Why is it so difficult to find biomarkers that work? As we have learned in drug development, most disease mechanisms involve complex but important differences among people, leading to the inconvenient finding that most drugs provide intended levels of benefit to a minority of patients. Hence, the large clinical trials that dominate the cost of late-stage drug development. Biomarker proteomics has now encountered the same “statistical barrier.”
Unfortunately, overcoming this barrier has proven quite difficult using the favored tools of deep coverage (Type 1) proteomics, with their high cost per sample and limited quantitative precision.
What should be the central component of the “biomarker pipeline” is missing: an easily accessible capacity to accurately measure, in large clinical sample sets, the candidate biomarkers emerging from proteomic (or genomic) studies.
Werner Zolg crystallized this requirement by pointing out that good analytical data from at least 1,500 samples is required to support a convincing case for serious, i.e., commercial, interest in any protein biomarker. Of the thousands of papers in biomarker proteomics, I can only think of one that involved more than 1,000 samples. All the rest fall short of the Zolg number.
This means that the biomarkers “discovered” in these studies have not been tested to a level that establishes real clinical utility (often referred to as “verification”). Absence of such data leaves us speculating as to the fraction of published candidates that ultimately ought to find use in medicine, but a persuasive case can be made that the failure rate is greater than that of drug candidates going into Phase I trials, and probably exceeds 95%.
Clinical verification of new protein biomarkers is constrained by several factors, including lack of grant funding available to “confirm the discoveries of others” and, until recently, the lack of a suitable technology base. Immunoassays, the default method of high-throughput protein quantitation, are difficult and expensive to construct and more difficult to multiplex in a reliable fashion as required in large-scale candidate verification.
Mass spectrometry has now emerged as the favored path for development of the targeted assays required for Type 2 research, largely as a result of applying to peptides the multiple reaction monitoring (MRM) technology long used by analytical chemists for quantitation of smaller molecules.
MRM measurements provide near-absolute structural specificity, true internal standardization and flexible multiplexing, none of which is available in conventional immunoassays.
MRM has also overcome one of the long-standing criticisms of proteomics—reproducibility. At one point, in the wake of the SELDI debacle, it was believed, especially in the genomics community, that the methods of proteomics were simply not reliable enough to get the same result in different labs.
Multilaboratory efforts, largely spearheaded by the NCI’s CPTAC program, have now shown that peptide MRM measurements are accurate and consistent across different labs and instrument platforms, as analytical chemists knew they would be.