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The Special Issue: Clinical Proteomics for Precision Medicine Carol E. Parker and Christoph H. Borchers* This special issue originates in the Clinical Day workshop - Clinical Proteomics for Precision Medicine – which was held in conjunction with the 15th annual HUPO World Congress, in Taipei, Taiwan, on Sunday, Sept. 18, 2016. The meeting was organized by Christoph Borchers, Yu-Ju Chen, and chaired by Christoph Borchers and Josh LaBaer. The meeting featured 8 speakers, from 5 countries (Canada, US, Germany, Japan, and Taiwan). These presentations, and work done by researchers attending this workshop form the basis for this special issue of Proteomics- Clinical Applications. The capacity of proteomics and genomics to examine the molecular basis of human disease – and an individual’s response to disease and treatment – has led to a new approach to therapeutics and disease treatment. Using proteomic and genomic data, it is now increasingly possible to predict, for example, which drug or antibody is most likely to lead to successful treatment in a particular patient’s cancer. This individualized approach is called precision medicine, previously known as “personalized medicine”. The lead-off “viewpoint” article, by Roos, et al., discusses the potential for the use of combined proteomics and genomics data to solve problems in neuromuscular and neurodegenerative diseases, and extends this approach to a discussion of the limitations and challenges of proteogenomics in other rare disorders.[1] This article is followed by three review articles. The first, by Adeola, et al., is on the potential for the use of human hair proteomics in disease diagnostics. Hair is already used in forensic and drug-compliance testing, but the author presents a case Dr. C. E. Parker, Dr. C. H. Borchers* University of Victoria - Genome British Columbia Proteomics Centre Victoria, BC, Canada E-mail:
[email protected] Dr. C. H. Borchers Jewish General Hospital Proteomics Laboratory McGill University Lady Davis Institute Montr´ eal, QC, Canada Dr. C. H. Borchers Department of Biochemistry and Microbiology University of Victoria Victoria, BC, Canada Dr. C. H. Borchers Department of Oncology Jewish General Hospital Proteomics Centre McGill University Montreal, QC, Canada * CB
is the CSO of MRM Proteomics, Inc. The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/prca.201600144
DOI: 10.1002/prca.201600144
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that hair proteomics is underutilized and understudied, and that hair can provide a “historic medical repository” of disease-specific proteomes.[2] Next, there is a review by Carr, et al. from the Roos laboratory, on the proteomics of tissue pathophysiology in Duchenne Muscular Dystrophy. By integrating the data from 9 studies, the authors identified 31 proteins involved in dystrophin deficiency, including proteins involved in actin cytoskeleton and cellular energy metabolism.[3] The third review, by Latosinska et al., discusses the clinical proteomics of bladder cancer, where earlier intervention and frequent monitoring have the potential to prevent recurrence. The authors present biomarkers for patient stratification and prognosis, and recent studies identifying new potential therapeutic targets.[4] The first of five research articles is on a method developed by Mohammed, et al., called “External STandard Addition”, which provides the advantages of conventional standard addition (i.e., internal native peptide (NAT) addition), but which can be used on small samples because instead of being added to aliquots of the actual sample the NAT standards are added externally – to a buffer.[5] Next is a research article by Hughes and Morin, on using public data repositories to guide precision medicine. In this article, the authors propose utilizing publically deposited massspectrometry-based proteomics data to perform inter-study comparisons of cell-line or tumor-tissue materials. This approach is supported by the overall agreement of gene expression data on ovarian cancer cell lines, even when different methodologies were used.[6] There has been widespread interest in both saliva and urine as alternatives to plasma as a biofluid for biomarker studies not only because of the proximity of urine and saliva to the sites of different cancers (bladder cancer (see [4] ) and oral cancer, respectively), but also because the wide dynamic range of protein concentrations in plasma makes it a particularly challenging matrix. The next research article, by Hsiao, et al., explores the within day and intraday variations in the protein concentrations of 90 salivary proteins (the 99 quantifiable targets) in normal saliva, as a reference point for the use of saliva as a source of biomarkers.[7] From a methodological point of view, it is notable that this study used a 15 N-labeled protein, with analysis by MRM-MS in triplicate. The final two research papers discuss applications of personalized medicine to lung cancer. The paper by Putri, et al. discusses chemotherapy immunophenoprofiles in Non-Small-Cell Lung Cancer.[8] The authors measured 2424 proteins in peripheral blood mononuclear cells, and correlated the profiles with progression-free survival data. The membrane proteomic profiles of peripheral blood mononuclear cells revealed alterations in the
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patient’s immune system in neutrophil, T cells, and antigen presenting pathways. The final paper in this special issue, by Perzanowska, et al. describes the development of an MRM-based cytokeratin marker assay for the study of lung-cancer pleural effusions.[9] Sampling pleural effusion is a less-invasive than tissue collection and histopathology examination, and can help to guiding therapy in patients that are inoperable and who otherwise can only provide small biopsy samples. This assay quantitated 17 cytokeratins, was evaluated on 118 patients, and was able to differentiate between the three lung-cancer histological types – adenocarcinoma, squamous cell carcinoma, and small cell lung cancer. As can be seen from the above papers, this is an exciting time for clinical proteomics. In fact, mass spectrometry is already in the clinic – the introduction of bench-top MALDI-TOF instruments has already revolutionized microbiology and bacterial identification, and more than 5000 instruments have already been installed in clinics throughout the world. Currently, however, the full potential of MALDI-MS – and MS instruments in general – is still not are not being realized. Unfortunately, MS is still not being widely used for clinical diagnostics, except for the area of neonatal screening for metabolic diseases. MS has a great potential for multiplexed analysis of panels of protein and metabolite biomarkers. Panel of biomarker analysis provide higher specificity than single-protein assays, and are already being offered to the community by companies provide truly personalized medicine by offering genetic, proteomic, metabolic, and microbial profiling. MS has already been shown to have the important advantage that the development of quantitative assays is rapid and relatively inexpensive development. It is therefore of great interest for the development of companion diagnostics (“no new drugs without biomarkers”) and for assays that are specific high-throughput, specific, and which require only small amounts of sample. In conclusion, the past several years have demonstrated that proteomics can be quantitative, and can be made reproducible between laboratories – but only if strict quality control is maintained on sample preparation, data acquisition, and instrument performance (i.e., “standardization”). Small-molecule mass spectrometry – in particular, metabolomics by LC/MS – has been used for decades for prenatal screening, usually combined with dried blood spot analysis. Translation of proteomics into the clinic, however, has been slower, partly because of the real and
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perceived complexity of LC/MS/MS instrumentation, and the need for trained personnel. Recently, however, increases in automation and the introduction of benchtop MALDI instruments into the clinic for bacterial identification have both literally and figuratively “opened the door” to clinical proteomics. This is now the new frontier – the translation of quantitative MS-based proteomics methods to the clinic. While this is an exciting time for proteomics, the lessons learned from the “SELDI fiasco”[10–12] must never be forgotten. There can be no shortcuts – data quality and reproducibility must be the primary concern at all times. When proteomics is used in the clinic, peoples’ lives are truly at stake. [1] A. Roos, R. Thompson, R. Horvath, H. Lochm¨ uller, A. Sickmann, Proteomics: Clin. Appl. 2018, 2, 1700073. [2] H. A. Adeola, J. C. Van Wyk, A. Arowolo, R. M. Ngwanya, K. Mkentane, N. P. Khumalo, Proteomics: Clin. Appl. 2018, 2, 1700048. [3] S. J. Carr, R. P. Zahedi, H. Lochm¨ uller, A. Roos, Proteomics: Clin. Appl. 2018, 2, 1700071. [4] A. Latosinska, M. Frantzi, A. Vlahou, A. S. Merseburger, H. Mischak, Proteomics: Clin. Appl. 2018, 2, 1700074. [5] Y. Mohammed, J. Pan, S. Zhang, J. Han, C. H. Borchers, Proteomics: Clin. Appl. 2018, 2, 1600180. [6] C. S. Hughes, G. B. Morin, Proteomics: Clin. Appl. 2018, 2, 1600179. [7] Y.-C. Hsiao, L. J. Chu, Y.-T. Chen, L.-M. Chi, K.-Y. Chien, W.-F. Chiang, Y.-T. Chang, S.-F. Chen, W.-S. Wang, Y.-N. Chuang, S.-Y. Lin, C.-Y. Chien, K.-P. Chang, Y.-S. Chang, J.-S. Yu, Proteomics: Clin. Appl. 2018, 2, 1700039. [8] D. U. Putri, P.-H. Feng, Y.-H. Hsu, K.-Y. Lee, F.-W. Jiang, L.-W. Kuo, Y.-J. Chen, C.-L. Han, Proteomics: Clin. Appl. 2018, 2, 1700040. [9] A. Perzanowska, A. Fatalska, G. Wojtas, A. Lewandowicz, A. Michalak, G. Krasowski, C. H. Borchers, M. Dadlez, D. Domanski, Proteomics: Clin. Appl. 2018, 2, 1700084. [10] K. A. Baggerly, J. S. Morris, K. R. Coombes, Bioinformatics 2004, 20, 777. [11] E. F. Petricoin, A. M. Ardekani, B. A. Hitt, P. J. Levine, V. A. Fusaro, S. M. Steinberg, G. B. Mills, C. Simone, D. A. Fishman, E. C. Kohn, L. A. Liotta, Lancet 2002, 359, 572. [12] M. T. Davis, P. L. Auger, S. D. Patterson, Clin. Chem. 2010, 56, 244. [13] A. G. Chambers, A. J. Percy, J. Yang, A. G. Camenzind, C. H. Borchers, Mol. Cell. Proteomics 2013, 12, 781. [14] A. G. Chambers, A. J. Percy, D. B. Hardie, C. H. Borchers, J. Am. Soc. Mass Spectrom. 2013, 24, 1338. [15] A. G. Chambers, A. J. Percy, J. Yang, C. H. Borchers, Mol. Cell. Proteomics 2015, 14, 3094.
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