Pathology, proteomics and the pathway to personalised medicine. (4th March 2018)

• Record Type: Journal Article
• Authors: Jin, Ping; Lan, Jiang; Wang, Kui; Baker, Mark S.; Huang, Canhua; Nice, Edouard C.
• Is Part Of: Expert review of proteomics. Volume 15:Number 3(2018)
• Page Start: 231
• Page End: 243
• Publication Date: 2018-03-04
• Subjects: Biobanking -- big data -- mass spectrometry -- liquid biopsy -- microarrays -- pathology -- proteomics -- personalized medicine; Proteins -- Biotechnology -- Periodicals; Proteomics -- Periodicals; 572.6
• DOI: 10.1080/14789450.2018.1425618
• Languages: English
• ISSNs: 1478-9450

Abstract

ABSTRACT: Introduction : As we move from a discovery to a translational phase in proteomics, with a focus on developing validated clinical assays to assist personalized medicine, there is a growing need for strong bidirectional interactions with the clinical pathology community. Thus, while on one hand the proteomics community will provide candidate biomarkers to assist in diagnosis, prognosis, surveillance, identification of individualized patient medication, and development and validation of new assays for diagnostic use, on the other the pathology community will assist with specific tissue identification and selection (e.g. laser capture microdissection, tissue sections for MS imaging), biobanking, validation of emerging automated histopathology techniques, preparation and classification of relevant patient medical reports, and assisting with the optimization of experimental design for clinical trials. Areas covered : Here we discuss these topics with a particular emphasis on recent publications and relevant initiatives and outline some of the hurdles that still remain for personalized medicine. Expert commentary : It is clear that effective crosstalk between the proteomics and pathology communities will greatly accelerate crossover of candidate biomarkers to personalized medicine, which will have significant benefits not only for patient wellbeing, but also the global healthcare budget. However, analysis of the big data generated may become rate-limiting.