Proteomics is about measuring proteins, and ideally, about measuring many of them. Not surprisingly, given the universal importance of proteins in biological systems and the variety of their properties when employed as machines, signals, structural materials, and food, proteomics has enabled real progress in many areas of biology while falling short in others. Fortunately, we are beginning to understand the differences between these cases, and have begun to adapt the technology and the way we use it to address the harder questions successfully.
For simplicity, I refer here to proteomics in the service of basic biology as Type 1 proteomics, and to population proteomics (mainly clinical applications) as Type 2. My belief, outlined in this article, is that we are at an inflection point in terms of what Type 2 proteomics can deliver in clinical applications.
Type 1 proteomics is making major contributions toward illuminating biological model systems for clues to basic mechanisms. Phosphorylation, glycosylation, and a host of other mechanistically important post-translational modifications have been revealed and made measurable. Increasingly, complete inventories of proteins are being generated from matched samples (e.g., SILAC comparisons of cells under perturbation or label-free analyses of disease vs. normal clinical samples). These results will provide the foundational data needed for a real systems biology one that incorporates experimental data exposing the operation of complex cellular regulatory mechanisms.
Proteomics has had less success so far in finding clinical biomarkers, a classical Type 2 application and an area of biology in which population heterogeneity is a key limitation. Essentially, the quest for biomarkers is a search for biological mechanisms that are invariant, or nearly so, across a real population of individuals.
In principle, all mechanistic discoveries from Type 1 work can be considered candidate biomarkers to the extent that the mechanism in question is related to disease, drug treatment, etc.
Published biomarker studies have identified some disease association for almost 25% of the 20,000 human proteins. Unfortunately, none of these prospective discoveries has yet been confirmed at the level required to achieve FDA clearance for a clinically useful protein test.
That proteins can be excellent clinical biomarkers is indisputable: 109 proteins are measured by FDA-cleared tests and another 96 by generally available laboratory-developed tests (homebrews) through the efforts of a multibillion dollar in vitro diagnostics (IVD) industry. Cardiac troponin, for example, when measured in the blood, is the clinical definition of a heart attack.
Yet the rate at which new protein analytes are cleared has remained flat at about 1.5 new proteins per year for the last 15 years, much lower than the rate during the initial wave of monoclonal antibody-driven discoveries and far less than the number required to address critical diagnostically underserved indications such as Alzheimer disease, COPD, and stroke.
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.
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 quantification, are difficult and expensive to construct and more difficult to multiplex in a reliable fashion as required in large-scale candidate verification.
Source:N. Leigh Anderson, Ph.D., Genetic Engineering & Biotechnology News