Linda Switzar (Presenter)
Leiden University Medical Center
Bio: Linda Switzar is a postdoctoral researcher at the Center for Proteomics and Metabolomics and the Human genetics department of the Leiden University Medical Center (LUMC) in Leiden, The Netherlands. She is currently working on a ZonMW project entitled “Antisense therapy for several major rare diseases”, in which her primary role is the development of MS-based assays for the analysis of protein biomarkers for therapeutic monitoring. In addition, she also performs in-depth protein characterization, such as disulfide bond mapping and identification of enzymatic cleavage sites, on protein targets related to genetic diseases.
Authorship: L. Switzar (1,2), P. Spitali (2), M. Bladergroen (1), S. Nicolardi (1), C. Cobbaert (3), A. Aartsma-Rus (2), Y. van der Burgt (1,3)
(1) Center for Proteomics and Metabolomics, (2) Department of Human Genetics, (3) Department of Clinical Chemistry, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| Short Abstract Duchenne muscular dystrophy (DMD) is a genetic disorder that is characterized by continuous muscle damage. Recently, a novel drug therapy aimed at muscle restoration and delay of disease progression has been conditionally approved for treatment of patients that suffer from this severe and fatal disease. Besides the established functional outcome measures, protein biomarkers offer an attractive alternative for monitoring the effectiveness of treatment. In this study, an established combination of immunocapture and mass spectrometry (MS) will be employed for serum protein quantification in a multiplexed assay for evaluation of various DMD biomarker candidates. |
Long Abstract
Duchenne muscular dystrophy (DMD) is a severe muscle disease affecting 1 in 5000 new born males worldwide. This genetic disorder is characterized by sensitivity to muscle damage during normal exercise, resulting in continuous loss of muscle tissue and function, and eventually premature death. Recently, a therapy aimed at muscle restoration and delay of disease progression has been conditionally approved, while several other therapies are in (clinical) development. [1, 2] Drug efficacy for DMD is currently primarily evaluated with functional outcome measures, e.g. the 6-minute walk test that was used as a primary endpoint to obtain conditional marketing authorization for Translarna™ in Europe [3]. Regulators have indicated that trials aiming at slowing down disease progression should be performed over a period of 18-24 months, because DMD is progressing relatively slow. In order to facilitate clinical evaluation and marketing authorization of potential treatments, it is crucial to attain a proper means to monitor the effectiveness of treatment. To this end, protein biomarkers offer an attractive alternative for monitoring the effectiveness of treatment at secondary endpoints.
The clinical utility of protein biomarkers is well established with more than 200 proteins that are routinely and quantitatively assayed in plasma or serum in clinical laboratories. [4] In addition, protein biomarkers can also be used for therapeutic response monitoring and, as such, are an important tool to facilitate drug development. The only serum biomarker currently used for early detection of muscular dystrophy is serum creatinine kinase (CK), but this biomarker is not suitable for therapeutic drug monitoring. For this reason, the discovery of new protein biomarkers for DMD has received a great deal of attention [5-7], resulting in a large number of biomarker candidates that can often be linked to DMD based on protein function and pathway association. However, none of these candidates has reached the stage of clinical application in therapeutic monitoring of drug treatment yet.
The introduction rate of new clinical protein biomarker assays has declined to approximately 1.5 new protein analytes approved by the US Food and Drug Administration per year. [4] It has been argued that a single biomarker may not be sufficient to describe a patients’ health status since often different biological processes are involved in disease and ongoing in parallel. With this in mind, our objective is to develop a biomarker panel consisting of several proteins linked to DMD that can provide detailed information on the effect of drug treatment on the patient. In the current study we will evaluate a protein biomarker panel, ultimately aiming for therapeutic drug monitoring in DMD. For this purpose, we will analyze patient serum samples that have been collected in our DMD exon skipping group during a long-term clinical study.
Validation of candidate biomarkers, that are often low abundant serum proteins, requires a targeted and robust workflow, preferably with high-throughput (HT) capacity. Immunoassays display high sensitivity and allow for the HT analysis of large samples sets, but lack multiplexing capability and suffer from specificity issues. Mass spectrometry (MS) has been increasingly recognized as an alternative or complementary technique to overcome many of the interference issues associated with current, immunoassay-based, clinical protein assays. [8] While immunoassays rely on indirect quantification, MS can provide absolute structural specificity and can measure dozens of analytes in a single analysis at very little incremental cost per added analyte. Stable-isotope standards and capture by anti-peptide antibodies (SISCAPA) is an established and commercialized strategy that selectively enriches proteotypic signature peptides derived after enzymatic digestion of the plasma or serum sample followed by MS read-out. [9] In this approach, the selectivity of immunoassays is combined with the specificity of MS into a multiplexed and HT method. The workflow consists of: 1) proteolytic digestion of serum proteins, 2) addition of stable isotopically labelled (SIL, “heavy”) peptide standards, 3) immunocapture of the target peptides and heavy peptide standards using a monoclonal anti-peptide antibody, 4) relative quantitation of the target peptides with MS.
The SISCAPA-MS approach has been previously applied in our group to the multiplexed analysis of apolipoprotein A-I and apolipoprotein B-100 for potential clinical use in cardiovascular disease risk assessment. [10] The automated SISCAPA-MS workflow resulted in well-controlled analytical quality, essential for implementation of MS-based protein assays in routine clinical testing. All sample preparation steps, including elution and spotting on a MALDI target plate, have been automated using a Hamilton robot platform and MS readout was performed with MALDI-TOF. This workflow can be used for the development of a biomarker panel for DMD due to the modular set-up of the automated sample preparation protocols.
For the current project, seven protein targets were selected, including complement C3, apolipoprotein B-100 and c-reactive protein, based on the results from previous DMD biomarker discovery studies and the availability of SISCAPA kits. All selected targets will be combined into a single multiplexed SISCAPA-MS assay, which requires optimization of the amount of antibody, spiked SIL peptide and volume of serum. Furthermore, the proteotypic signature peptide for some of these targets contains a C-terminal lysine, which makes it more challenging to analyze with matrix-induced laser dissociation ionization (MALDI)-MS. In addition, since the selected protein targets have an endogenous abundance ranging from ng to mg per liter, the dynamic range of the MS instrument also becomes an important parameter. Therefore, we will perform a comparative analysis using different instruments and ionization methods, including MALDI-time-of-flight (TOF), MALDI-fourier transform ion cyclotron resonance (FTICR) and electrospray ionization (ESI)-triple quadrupole (QqQ) MS.
References & Acknowledgements:
Acknowledgements
The authors thank Morteza Razavi (Director of Lab Operations) from SISCAPA assay technologies for technical advice on multiplexing SISCAPA assays.
References
1. Hoffman EP, McNally EM (2014) Exon-Skipping Therapy: A Roadblock, Detour, or Bump in the Road? Science Translational Medicine. 6(230), 230fs214-230fs214.
2. Jarmin S, Kymalainen H, Popplewell L, Dickson G (2013) New developments in the use of gene therapy to treat Duchenne muscular dystrophy. Expert Opinion on Biological Therapy. 14(2), 209-230.
3. Haas M, Vlcek V, Balabanov P, Salmonson T, Bakchine S, Markey G, Weise M, Schlosser-Weber G, Brohmann H, Yerro CP, et al. (2015) European Medicines Agency review of ataluren for the treatment of ambulant patients aged 5 years and older with Duchenne muscular dystrophy resulting from a nonsense mutation in the dystrophin gene. Neuromuscular Disorders. 25(1), 5-13.
4. Anderson NL (2010) The Clinical Plasma Proteome: A Survey of Clinical Assays for Proteins in Plasma and Serum. Clinical Chemistry. 56(2), 177-185.
5. Ayoglu B, Chaouch A, Lochmüller H, Politano L, Bertini E, Spitali P, Hiller M, Niks EH, Gualandi F, Pontén F, et al. (2014) Affinity proteomics within rare diseases: a BIO-NMD study for blood biomarkers of muscular dystrophies. EMBO Molecular Medicine. 6(7), 918-936.
6. Hathout Y, Brody E, Clemens PR, Cripe L, DeLisle RK, Furlong P, Gordish-Dressman H, Hache L, Henricson E, Hoffman EP, et al. (2015) Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy. Proceedings of the National Academy of Sciences. 112(23), 7153-7158.
7. Nadarajah VD, van Putten M, Chaouch A, Garrood P, Straub V, Lochmüller H, Ginjaar HB, Aartsma-Rus AM, van Ommen GJB, den Dunnen JT, et al. (2011) Serum matrix metalloproteinase-9 (MMP-9) as a biomarker for monitoring disease progression in Duchenne muscular dystrophy (DMD). Neuromuscular Disorders. 21(8), 569-578.
8. Becker JO, Hoofnagle AN (2012) Replacing immunoassays with tryptic digestion-peptide immunoaffinity enrichment and LC–MS/MS. Bioanalysis. 4(3), 281-290.
9. Razavi M, Johnson LDS, Lum JJ, Kruppa G, Anderson NL, Pearson TW (2013) Quantification of a Proteotypic Peptide from Protein C Inhibitor by Liquid Chromatography–Free SISCAPA-MALDI Mass Spectrometry: Application to Identification of Recurrence of Prostate Cancer. Clinical Chemistry. 59(10), 1514-1522.
10. van den Broek I, Nouta J, Razavi M, Yip R, Bladergroen MR, Romijn FPHTM, Smit NPM, Drews O, Paape R, Suckau D, et al. (2015) Quantification of serum apolipoproteins A-I and B-100 in clinical samples using an automated SISCAPA–MALDI-TOF-MS workflow. Methods. 81(0), 74-85.
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