Defining the Role of Mass Spectrometry in Cancer Diagnostics

Abstract

The prospect of uncovering the information contained in the human genome garnered such great promise that it attracted a synergistic blend of researchers from multiple disciplines. Notably, this group included molecular biologists, clinician-scientists, computational mathematicians, and industry input via engineers and, in several cases, management expertise in the form of leadership. This synergistic grouping drove tremendous technological advancement that resulted in the completion of this multinational research project with unprecedented success. The promise of proteomics is proving to be an even greater allure than its predecessor, genomics.Specifically for this discussion, the resurgence of clinical proteomics has energized the stagnating field of cancer biomarker discovery. Although the field of clinical proteomics includes far too many promising technologies to adequately address here, it is safe to say that mass spectrometry (MS), with the great strides in application to protein research, has positioned itself as a key technology platform for biomarker discovery. The promised return of our investment in clinical proteomics is nothing short of precise molecular diagnostics. Again, much as was the case for genomics and indeed inclusive of the field of functional proteomics that is led by many of the same research groups that pushed forward genomics, clinical proteomics is being driven by a diverse group of integrated scientific expertise. Specifically, the active front in the expanding arena of protein biomarker discovery is composed of experts in MS that have been diligently pushing the technical limits of MS, biologists from the postgenomics generation that are applying MS to the new horizon of proteomics and the ever-responsive engineering/industrial segment. In addition, biostatisticians, mathematicians, and computational biologists, already motivated by their achievements in interpreting genomics data, have responded to a similar call for help in analysis of proteomic data. Rounding out this formidable combination of expertise are the clinician-scientists and epidemiologists who provide a critical component in responding to the challenges of developing protein biomarkers for cancer molecular diagnostics.Unprecedented capabilities in sensitivity and resolution coupled with the development of complimentary enabling computational approaches have allowed for dramatic increase in the ability to obtain sequence identification from complex solutions, perform fine-scale structure analysis, determine protein-protein interactions, and map post-translational modifications. In spite of the tremendous advances made in protein MS following the completion of the genomes of target organisms, some of the same old obstacles to protein biomarker discovery still exist. The physical hurdles of component complexity, the tremendous range in individual protein concentration, and the dynamic nature of the proteome are still major barriers to overcome. In addition, the added demands associated with targeting proteins that will function as clinical biomarkers of a disease as genetically diverse as cancer present a unique challenge for the field.It is becoming increasingly clear that the underlying heterogeneity of cancer necessitates that development of accurate diagnostics will be dependent on the discovery of a “panel” of proteins that together can discriminate between subtle disease states with population-wide robustness. This requirement for multiple proteins over a single protein biomarker emphasizes the demand for improved technologies, allowing for measurement of the full compliment of the proteome, “the proteome volume” (for a useful review of many of the current proteomic technologies, see refs. 1-4). In conceptualizing where the technical challenges lie, it is useful to recall the proteomics equivalent to the Schrödinger equation in that we can know everything about one protein or we can know a little about many proteins. The inter