Statistical Methodology for the Design and Analysis of Protein Mass Spectrometry Studies

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Current key goals in cancer research are to identify molecular information that can be used to (a) diagnose and stage disease, (b) monitor response to treatment, (c) predict which patients will respond to therapy and (d) predict patient outcome. This will enable the potential early detection and improved staging of cancer. In addition it will enable treatments to be targeted at individuals who have some chance of benefiting and continued in those showing a worthwhile level of improvement. The proteins that are circulating in the blood are a possible source of information. Using a very small amount of blood, current technology is able to generate a ?proteomic signature? for a patient, which gives information on the concentration of thousands of different proteins that are present in the sample. The aim is then to compare samples from different types of patients, for example cancer versus non-cancer or responder versus non-responder, to determine if any of proteins differ in their concentration. If such proteins are identified they could be used to aid the diagnosis of cancer or inform appropriate treatment for future patients. Statistical analysis of data is an important part of any research process, enabling conclusions regarding scientific hypotheses to be drawn from the data in association with a measure of uncertainty. In the field of proteomics, valid and efficient statistical design and analysis are crucial to ensure that this scientific research provides robust and convincing conclusions. This is a new and rapidly developing scientific field that could have a direct impact on patient care and there is a great need for research to explore the statistical methodology associated with such studies. This is a hugely challenging area for statisticians and the methods that are already available needs to be assessed in terms of their specific application to proteomic data. The findings of this research will only be directly relevant to specialists in this field but ultimately this will result in increased confidence that differences detected in proteomic signatures between groups of patients are true biological differences, enabling the technology to benefit patient care in the future.