Traditionally, medicine has taken a “one-size-fits-all” approach to disease prevention and treatment strategies. However, as time has passed, and lessons have been learned, changes have begun to treat the individual rather than the average human. For many years, the need for administration of transfused blood from a donor with a compatible blood group to the recipient has been known but more recently greater attention has turned to look at other aspects.
Protein expression is dynamic, correlates with disease timing, severity, and response to therapy. The proteome of a given tissue will not be the same during infection as it was prior to infection or during recovery and is therefore very informative for tailoring individual diagnostics and therapy.
Mass spectrometry (MS) based quantitative proteomics has therefore emerged as an important tool in the precision medicine armoury, enabling known and unknown biomolecules to be identified in a single analysis. The sensitivity of the technology also enables discrimination between isoforms of the same molecule. Publication of the draft human proteome and subsequent publications of specific tissue proteomes are helping to build data that will enable comparative analysis. Comparison of patient samples with this database, or ultimately with the patient’s own archived healthy samples, would allow scientists and clinicians to associate changes in the proteomes with particular disease states and monitor responses to therapies. However, despite the promising results from this area, very few validated biomarkers or assays are available to doctors or patients currently, likely due to a multitude of factors.
Identifying Low Concentration Targets in Protein-Rich Samples
The identification of low concentration proteins within a complex sample, such as urine or blood has been one such hurdle. The presence of high molecular weight or high abundance proteins, such as albumin or immunoglobulins, in these samples, can overwhelm scarce proteins. Depletion or enrichment of these specimens by antibody purification or chromatographic separation has often been required, complicating and protracting workflows and increasing the opportunities for the introduction of sample bias or processing errors. Advances in technology however, such as developments in SWATH-MS which has a simplified workflow and has been successfully applied to a wide variety of biological samples , will hopefully see improvements in this area in the near future.
Data Processing – Proteome Analysis, Interpretation and Metadata Integration
The complexity of the data generated has also been a stumbling block in introducing the technique to mainstream medicine. Unlike a PCR-based test or genetic screening, proteome analysis does not provide a simple yes/no answer but rather requires interpretation. Developers have now produced software, such as Simcyp and Gastroplus, that will enable protein quantification results to be merged with genetic information and physiological data, such as organ volume or body fat composition, to give a more integrated analysis. When it comes to assessing the pharmacokinetic and pharmacodynamic aspects of drugs on patients however, even these integrated software packages fail to fully incorporate the information relating to interindividual variability. A lack of well-characterized tissue repositories has also been highlighted as a gap that requires filling to enable satisfactory comparators for “unknown” samples.
Proteomic Biomarker Reliability
Biomarkers have already been identified for a number of cancers, intestinal bowel disease and cardiac events as well as for rapid diagnosis of infectious diseases, including E. coli and Salmonella. However, the failure of identified biomarkers to apply over large populations is problematic and further work to verify and validate reliable biomarkers are clearly needed.
The implementation of proteomics as a clinical tool shows promise for the future of precision medicine, however there are clearly still challenges to be overcome in translating basic research into medicine. Most conditions incorporate multiple systems and cell types and so the identification and validation of biomarkers across many tissue types will be key to success.