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Existing and novel biomarkers for precision medicine in systemic sclerosis

Abstract

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

Key points

  • An urgent unmet need exists for validated non-invasive biomarkers for the diagnosis, assessment of disease activity and response to therapy of patients with systemic sclerosis (SSc).

  • Biomarkers can be used as easily measurable surrogate markers for clinical end points to aid in the stratification of patients for clinical trials.

  • Current biomarkers for SSc include cutaneous induration and assessment of serum autoantibodies and nailfold capillaroscopic patterns, although only cutaneous induration has been validated for diagnosis, prognosis or response to treatment.

  • Extracellular vesicles (EVs) contain a large number of macromolecules that reflect the physiological or pathological state of the cells of origin, rendering them a novel and valuable source of biomarkers.

  • Transcriptomic and proteomic analyses of EVs isolated from patients with SSc could provide biomarkers for diagnosis, classification and assessment of disease activity, prognosis and therapeutic response.

  • Novel biomarkers will be of value in the provision of precision and personalized medical care to patients with SSc.

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Fig. 1: Timeline of systemic sclerosis biomarker discovery.
Fig. 2: Biogenesis and macromolecular components of exosomes.
Fig. 3: The potential role of extracellular vesicle biomarkers in personalized medicine.

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References

  1. Snyder, M., Du, J. & Gerstein, M. Personal genome sequencing: current approaches and challenges. Genes Dev. 24, 423–431 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Snyder, M., Weissman, S. & Gerstein, M. Personal phenotypes to go with personal genomes. Mol. Syst. Biol. 5, 273 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Laufer, V. A., Chen, J. Y., Langefeld, C. D. & Bridges, S. L. Jr. Integrative approaches to understanding the pathogenic role of genetic variation in rheumatic diseases. Rheum. Dis. Clin. North Am. 43, 449–466 (2017).

    Article  PubMed  Google Scholar 

  4. Streeter, O. E. Jr, Beron, P. J. & Iyer, P. N. Precision medicine: genomic profiles to individualize therapy. Otolaryngol. Clin. North Am. 50, 765–773 (2017).

    Article  PubMed  Google Scholar 

  5. Collins, D. C., Sundar, R., Lim, J. S. & Yap, T. A. Towards precision medicine in the clinic: from biomarker discovery to novel therapeutics. Trends Pharmacol. Sci. 38, 25–40 (2017).

    Article  PubMed  CAS  Google Scholar 

  6. Hood, L. & Flores, M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized, and participatory. Nat. Biotechnol. 29, 613–624 (2012).

    CAS  Google Scholar 

  7. Gabrielli, A., Avvedimento, E. V. & Krieg, T. Scleroderma. N. Engl. J. Med. 360, 1989–2003 (2009).

    Article  PubMed  CAS  Google Scholar 

  8. Allanore, Y. et al. Systemic sclerosis. Nat. Rev. Dis. Primers 1, 15002 (2015).

    Article  PubMed  Google Scholar 

  9. McCray, C. J. & Mayes, M. D. Update on systemic sclerosis. Curr. Allergy Asthma Rep. 15, 25 (2015).

    Article  PubMed  CAS  Google Scholar 

  10. Denton, C. P. & Khanna, D. Systemic sclerosis. Lancet 390, 1685–1699 (2017).

    Article  PubMed  Google Scholar 

  11. Jimenez, S. A. & Derk, C. T. Following the molecular pathways toward an understanding of the pathogenesis of systemic sclerosis. Ann. Intern. Med. 140, 37–50 (2004).

    Article  PubMed  CAS  Google Scholar 

  12. Varga, J. & Abraham, D. Systemic sclerosis: A prototypic multisystem fibrotic disorder. J. Clin. Invest. 117, 557–567 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Katsumoto, T. R., Whitfield, M. L. & Connolly, M. K. The pathogenesis of systemic sclerosis. Annu. Rev. Pathol. 6, 509–537 (2011).

    Article  PubMed  CAS  Google Scholar 

  14. Ciechomska, M., van Laar, J. & O’Reilly, S. Current frontiers in systemic sclerosis pathogenesis. Exp. Dermatol. 24, 401–406 (2015).

    Article  PubMed  Google Scholar 

  15. Stern, E. P. & Denton, C. P. The pathogenesis of systemic sclerosis. Rheum. Dis. Clin. North Am. 41, 367–382 (2015).

    Article  PubMed  Google Scholar 

  16. Pattanaik, D. et al. Pathogenesis of systemic sclerosis. Front. Immunol. 6, 272 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Young, A. & Khanna, D. Systemic sclerosis: a systemic review on therapeutic management from 2011 to 2014. Curr. Opin. Rheumatol. 27, 241–248 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Nagaraja, V., Denton, C. P. & Khanna, D. Old medications and new targeted therapies in systemic sclerosis. Rheumatology 54, 1944–1953 (2015).

    Article  PubMed  CAS  Google Scholar 

  19. Mendoza, F. A., Mansoor, M. & Jimenez, S. A. Treatment of rapidly progressive systemic sclerosis: current and future perspectives. Expert Opin. Orphan Drugs 4, 31–47 (2016).

    Article  PubMed  Google Scholar 

  20. Mayes, M. D. et al. Prevalence, incidence, survival, and disease characteristics of systemic sclerosis in a large US population. Arthritis Rheum. 48, 2246–2255 (2003).

    Article  PubMed  Google Scholar 

  21. Steen, V. D. & Medsger, T. A. Changes in causes of death in systemic sclerosis, 1972–2002. Ann. Rheum. Dis. 66, 940–944 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Barnes, J. & Mayes, M. D. Epidemiology of systemic sclerosis: incidence, prevalence, survival, risk factors, malignancy, and environmental triggers. Curr. Opin. Rheumatol. 24, 165–170 (2012).

    Article  PubMed  Google Scholar 

  23. Hummers, L. K. The current state of biomarkers in systemic sclerosis. Curr. Rheumatol. Rep. 12, 34–39 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Castro, S. V. & Jimenez, S. A. Biomarkers in systemic sclerosis. Biomark. Med. 4, 133–147 (2010).

    Article  PubMed  CAS  Google Scholar 

  25. Abignano, G., Buch, M., Emery, P. & Del Galdo, F. Biomarkers in the management of scleroderma: an update. Curr. Rheumatol. Rep. 13, 4–12 (2011).

    Article  PubMed  Google Scholar 

  26. Castelino, F. V. & Varga, J. Current status of systemic sclerosis biomarkers: applications for diagnosis, management and drug development. Expert Rev. Clin. Immunol. 9, 1077–1090 (2013).

    Article  PubMed  CAS  Google Scholar 

  27. Affandi, A. J., Radstake, T. R. & Marut, W. Update on biomarkers in systemic sclerosis: tools for diagnosis and treatment. Semin. Immunopathol. 37, 475–487 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Hasegawa, M. Biomarkers in systemic sclerosis: their potential to predict clinical courses. J. Dermatol. 43, 29–38 (2016).

    Article  PubMed  CAS  Google Scholar 

  29. Ligon, C. & Hummers, L. K. Biomarkers in scleroderma: progressing from association to clinical utility. Curr. Rheumatol. Rep. 18, 17 (2016).

    Article  PubMed  CAS  Google Scholar 

  30. Manetti, M. Emerging biomarkers in systemic sclerosis. Curr. Opin. Rheumatol. 28, 606–612 (2016).

    Article  PubMed  CAS  Google Scholar 

  31. NIH Definitions Working Group in Biomarkers and Surrogate Endpoints: Clinical Research and Applications: Proceedings of the NIH-FDA Conference, Bethesda, MD, 15–16 April 1999, in ICS 1205, 1e (International Congress) Ch. 1 (ed. Downing, G.) 1–9 (Elsevier, Amsterdam, 2000).

  32. Lesko, L. J. & Atkinson, A. J. Jr. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu. Rev. Toxicol. 41, 347–366 (2001).

    Article  CAS  Google Scholar 

  33. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89–95 (2001).

    Article  Google Scholar 

  34. Anderson, J. E. et al. Methods and biomarkers for the diagnosis and prognosis of cancer and other diseases: towards personalized medicine. Drug. Resist. Updat. 9, 198–210 (2006).

    Article  PubMed  CAS  Google Scholar 

  35. Collins, C. D. et al. The application of genomic and proteomic technologies in predictive, preventive and personalized medicine. Vascul. Pharmacol. 45, 258–267 (2006).

    Article  PubMed  CAS  Google Scholar 

  36. Isserlin, R. & Emili, A. Nine steps to proteomic wisdom: a practical guide to using protein-protein interaction networks and molecular pathways as a framework for interpreting disease proteomic profiles. Proteom. Clin. Appl. 1, 1156–1168 (2007).

    Article  CAS  Google Scholar 

  37. Kostka, D. & Spang, R. Finding disease specific alterations in the co-expression of genes. Bioinformatics 20 (Suppl. 1), S32–S36 (2004).

    Google Scholar 

  38. Xu, M. et al. An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer. BMC Genomics 9 (Suppl. 1), S12 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Ross, J. S. Biomarkers and drug development 2009. Expert Opin. Med. Diagn. 3, 471–478 (2009).

    Article  PubMed  CAS  Google Scholar 

  40. Wagner, J. A. Overview of biomarkers and surrogate endpoints in drug development. Dis. Markers 18, 41–46 (2002).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Colburn, W. A. & Lee, J. W. Biomarkers, validation and pharmacokinetic-pharmacodynamic modelling. Clin. Pharmacokinet. 42, 997–1022 (2003).

    Article  PubMed  CAS  Google Scholar 

  42. Venitz, J. Using exposure-response and biomarkers to streamline drug development. Ernst Schering Res. Found. Workshop 59, 47–63 (2007).

    Article  CAS  Google Scholar 

  43. Sarker, D. & Workman, P. Pharmacodynamic biomarkers for molecular cancer therapeutics. Adv. Cancer Res. 96, 213–268 (2007).

    Article  PubMed  CAS  Google Scholar 

  44. Hollebecque, A., Massard, C. & Soria, J. C. Implementing precision medicine initiatives in the clinic: a new paradigm in drug development. Curr. Opin. Oncol. 26, 340–306 (2014).

    Article  PubMed  CAS  Google Scholar 

  45. Carrigan, P. & Krahn, T. Impact of biomarkers on personalized medicine. Handb. Exp. Pharmacol. 232, 285–311 (2016).

    Article  PubMed  CAS  Google Scholar 

  46. Fleming, T. R., DeGruttola, V., & DeMets, D. L. Surrogate endpoints. AIDS Clin. Rev. 1997–1998, 129–143 (1998).

    Google Scholar 

  47. Lafyatis, R. & Jimenez, S. A. in Scleroderma: From Pathogenesis to Comprehensive Management Ch. 16 (eds Varga, J. et al.) 245–260 (Springer, New York, 2017).

  48. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Peters, B. A. et al. Accurate whole-genome sequencing and haplotyping from 20 human cells. Nature 487, 190–195 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Kurkurba, K. R. & Montgomery, S. B. RNA sequencing and analysis. Cold Spring Harb. Protoc. 2015, 951–969 (2015).

    Google Scholar 

  53. Favicchio, R. et al. Strategies in functional proteomics: Unveiling the pathways to precision oncology. Cancer Lett. 382, 86–94 (2016).

    Article  PubMed  CAS  Google Scholar 

  54. Zhou, L. et al. Clinical proteomics-driven precision medicine for targeted therapy: current overview and future perspectives. Expert Rev. Proteom. 13, 367–381 (2016).

    Article  CAS  Google Scholar 

  55. Honda, K. et al. Proteomic approaches to the discovery of cancer biomarkers for early detection and personalized medicine. Jpn J. Clin. Oncol. 43, 103–109 (2013).

    Article  PubMed  Google Scholar 

  56. Huang, L., Michael, S. A., Chen, Y. & Wu, H. Current advances in highly multiplexed antibody-based single-cell proteomic measurements. Chem. Asian J. 12, 1680–1691 (2017).

    Article  PubMed  CAS  Google Scholar 

  57. Hathout, Y. Proteomic methods for biomarker discovery and validation. Are we there yet? Expert Rev. Proteom. 12, 329–331 (2015).

    Article  CAS  Google Scholar 

  58. Richens, J. L., Lunt, E. A., Sanger, D., McKenzie, G. & O’Shea, P. Avoiding nonspecific interactions in studies of the plasma proteome: practical solutions to prevention of nonspecific interactions for label-free detection of low-abundance plasma proteins. J. Proteome Res. 8, 5103–5110 (2009).

    Article  PubMed  CAS  Google Scholar 

  59. Bruderer, R. et al. New targeted approaches for the quantification of data-independent acquisition mass spectrometry. Proteomics 17, 1700021 (2017).

    Article  CAS  Google Scholar 

  60. Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteom. 1, 845–867 (2002).

    Article  CAS  Google Scholar 

  61. Garabedian, A. et al. Towards discovery and targeted peptide biomarker detection using nanoESI-TIMS-TOF MS. J. Am. Soc. Mass Spectrom. https://doi.org/10.1007/s13361-017-1787-8 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Ishizaki, J. et al. Targeted proteomics reveals promising biomarkers of disease activity and organ involvement in antineutrophil cytoplasmic antibody-associated vasculitis. Arthritis Res. Ther. 19, 218 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Nie, S. et al. Deep-dive targeted quantification for ultrasensitive analysis of proteins in nondepleted human blood plasma/serum and tissues. Anal. Chem. 89, 9139–9146 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell Biol. 6, 577–583 (2005).

    Article  PubMed  CAS  Google Scholar 

  65. Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25, 125–131 (2007).

    Article  PubMed  CAS  Google Scholar 

  66. Ellington, A. D. & Szostak, J. W. In vitro selection of RNA molecules that bind specific ligands. Nature 346, 818–822 (1990).

    Article  PubMed  CAS  Google Scholar 

  67. Tuerk, C. & Gold, L. Systematic evolution of ligands by exponential enrichment: RNA Ligands to bacteriophage T4 DNA polymerase. Science 249, 505–510 (1990).

    Article  PubMed  CAS  Google Scholar 

  68. Gramolini, A., Lau, E. & Lui, P. P. Identifying low-abundance biomarkers: aptamer-based proteomics potentially enables more sensitive detection in cardiovascular diseases. Circulation 134, 286–289 (2016).

    Article  PubMed  Google Scholar 

  69. Yoshida, Y., Waga, I. & Horii, K. Quantitative and sensitive protein detection strategies based on aptamers. Proteom. Clin. Appl. 6, 574–580 (2012).

    Article  CAS  Google Scholar 

  70. Thiviyanathan, V. & Gorenstein, D. G. Aptamers and the next generation of diagnostic reagents. Proteom. Clin. Appl. 6, 563–573 (2012).

    Article  CAS  Google Scholar 

  71. Fleming, T. R. & DeMets, D. L. Surrogate end points in clinical trials: are we being misled? Ann. Intern. Med. 125, 605–613 (1996).

    Article  PubMed  CAS  Google Scholar 

  72. Temple, R. Are surrogate markers adequate to assess cardiovascular disease drugs? JAMA 282, 790–795 (1999).

    Article  PubMed  CAS  Google Scholar 

  73. Perez-Gracia, J. L. Strategies to design clinical studies to identify predictive biomarkers in cancer research. Cancer Treat. Rev. 53, 79–97 (2017).

    Article  PubMed  Google Scholar 

  74. Wilhelm-Benartzi, C. S. et al. Challenges and methodology in the incorporation of biomarkers in cancer clinical trials. Crit. Rev. Oncol. Hematol. 110, 49–61 (2017).

    Article  PubMed  Google Scholar 

  75. Chau, C. H., Rixe, O., McLeod, H. & Figg, W. D. Validation of analytic methods for biomarkers used in drug development. Clin. Cancer Res. 14, 5967–5976 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  76. U.S. Food & Drug Administration. Guidance for industry — pharmacogenomics data submissions. U.S. Food & Drug Administration https://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm079849.pdf (2005).

  77. Goodsaid, F. & Frueh, F. Biomarker qualification pilot process at the U.S. Food and Drug Administration. AAPS J. 9, E105–108 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Lesko, L. J. & Atkinson Jr, A. J. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Pharmacol. Toxicol. 41, 347–366 (2001).

    CAS  Google Scholar 

  79. Boers, M., Brooks, P., Strand, C. V. & Tugwell, P. The OMERACT filter for outcome measures in rheumatology. J. Rheumatol. 25, 198–199 (2004).

    Google Scholar 

  80. Lassere, M. A users guide to measurement in medicine. Osteoarthritis Cartilage 14 (Suppl. 1), 10–14 (2006).

    Article  Google Scholar 

  81. Prentice, R. L. Surrogate endpoints in clinical trials: definition and operational criteria. Stat. Med. 8, 431–440 (1989).

    Article  PubMed  CAS  Google Scholar 

  82. U.S. Food & Drug Administration. Guidance for industry and FDA staff — qualification process for drug development tools. U.S. Food & Drug Administration http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm230597.pdf (2014).

  83. Seibold, J. R. & McCloskey, D. A. Skin involvement as a relevant outcome measure in clinical trials of systemic sclerosis. Curr. Opin. Rheumatol. 9, 571–575 (1997).

    Article  PubMed  CAS  Google Scholar 

  84. Merkel, P. A. et al. OMERACT 6. Current status of outcome measure development for clinical trials in systemic sclerosis: report from OMERACT 6. J. Rheumatol. 30, 1630–1647 (2003).

    PubMed  Google Scholar 

  85. Furst, D. et al. Systemic sclerosis — continuing progress in developing clinical measures of response. J. Rheumatol. 34, 1194–1200 (2007).

    PubMed  Google Scholar 

  86. Khanna, D. & Merkel, P. A. Outcome measures in systemic sclerosis: an update on instruments and current research. Curr. Rheumatol. Rep. 9, 151–157 (2007).

    Article  PubMed  Google Scholar 

  87. Khanna, D. et al. The American College of Rheumatology provisional composite response index for clinical trials in early diffuse cutaneous systemic sclerosis. Arthritis Rheumatol. 68, 299–311 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Kahaleh, M. B. et al. A modified scleroderma skin scoring method. Clin. Exp. Rheumatol. 4, 367–369 (1986).

    PubMed  CAS  Google Scholar 

  89. Furst, D. E. et al. The modified Rodnan skin score is an accurate reflection of skin biopsy thickness in systemic sclerosis. J. Rheumatol. 25, 84–88 (1998).

    PubMed  CAS  Google Scholar 

  90. Steen, V. D. & Medsger, T. A. Jr. Improvement in skin thickening in systemic sclerosis associated with improved survival. Arthritis Rheum. 44, 2828–2835 (2001).

    Article  PubMed  CAS  Google Scholar 

  91. Kaldas, M. et al. Sensitivity to change of the modified Rodnan skin score in diffuse systemic sclerosis — assessment of individual body sites in two large randomized controlled trials. Rheumatology 48, 1143–1146 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Ziemek, J. et al. The relationship between skin symptoms and the scleroderma modification of the health assessment questionnaire, the modified Rodnan skin score, and skin pathology in patients with systemic sclerosis. Rheumatology 55, 911–917 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Khanna, D. et al. Standardization of the modified Rodnan skin score for use in clinical trials of systemic sclerosis. J. Scleroderma Relat. Disord. 2, 11–18 (2017).

    Article  PubMed  Google Scholar 

  94. Kissin, E. Y. et al. Durometry for the assessment of skin disease in systemic sclerosis. Arthritis Rheum. 55, 603–609 (2006).

    Article  PubMed  Google Scholar 

  95. Merkel, P. A. et al. Validity, reliability, and feasibility of durometers measurements of scleroderma skin disease in a multicenter treatment trail. Arthritis Rheum. 59, 699–705 (2008).

    Article  PubMed  Google Scholar 

  96. Moore, T. L., Lunt, M., McManus, B., Anderson, M. E. & Herrick, A. L. Seventeen-point dermal ultrasound scoring system — a reliable measure of skin thickness in patients with systemic sclerosis. Rheumatology 42, 1559–1563 (2003).

    Article  PubMed  CAS  Google Scholar 

  97. Abignano, G. & Del Galdo, F. Quantitating skin fibrosis: innovative strategies and their clinical implications. Curr. Rheumatol. Rep. 16, 404 (2014).

    Article  PubMed  CAS  Google Scholar 

  98. Santiago, T. et al. A preliminary study using virtual touch imaging and quantification of the assessment of skin stiffness in systemic sclerosis. Clin. Exp. Rheumatol. 34 (Suppl. 100), S137–S141 (2016).

    Google Scholar 

  99. Merkel, P. A. et al. Patterns and predictors of change in outcome measures in clinical trials in scleroderma: an individual patient meta-analysis of 629 subjects with diffuse cutaneous systemic sclerosis. Arthritis Rheum. 64, 3420–3429 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Moore, O. A. et al. Quantifying change in pulmonary function as a prognostic marker in systemic sclerosis-related interstitial lung disease. Clin. Exp. Rheumatol. 33 (Suppl. 91), S111–S116 (2015).

    PubMed  Google Scholar 

  101. Goh, N. S. et al. Short term pulmonary function trends are predictive of mortality in interstitial lung disease associated with systemic sclerosis. Arthritis Rheumatol. 69, 1670–1678 (2017).

    Article  PubMed  CAS  Google Scholar 

  102. Campbell, R. M. & LeRoy, E. C. Pathogenesis of systemic sclerosis: a vascular hypotheses. Semin. Arthritis Rheum. 4, 351–368 (1975).

    Article  PubMed  CAS  Google Scholar 

  103. LeRoy, E. C. Systemic sclerosis. A vascular perspective. Rheum. Dis. Clin. North Am. 22, 675–694 (1996).

    Article  PubMed  CAS  Google Scholar 

  104. Fleming, J. N. & Schwartz, S. M. The pathology of scleroderma vascular disease. Rheum. Dis. Clin. North Am. 34, 41–55 (2008).

    Article  PubMed  Google Scholar 

  105. Kahaleh, B. Vascular disease in scleroderma: mechanisms of vascular injury. Rheum. Dis. Clin. North Am. 34, 57–71 (2008).

    Article  PubMed  Google Scholar 

  106. Trojanowska, M. Cellular and molecular aspects of vascular dysfunction in systemic sclerosis. Nat. Rev. Rheumatol. 6, 453–460 (2010).

    Article  PubMed  CAS  Google Scholar 

  107. Matucci-Cerinic, M., Kahaleh, B. & Wigley, F. M. Review: evidence that systemic sclerosis is a vascular disease. Arthritis Rheum. 65, 1953–1962 (2013).

    Article  PubMed  CAS  Google Scholar 

  108. Altorok, N., Wang, Y. & Kahaleh, B. Endothelial dysfunction in systemic sclerosis. Curr. Opin. Rheumatol. 26, 615–620 (2014).

    Article  PubMed  CAS  Google Scholar 

  109. Pattanaik, D., Brown, M. & Postlethwaite, A. E. Vascular involvement in systemic sclerosis (scleroderma). J. Inflamm. Res. 4, 105–125 (2011).

    PubMed  PubMed Central  CAS  Google Scholar 

  110. Herrick, A. L. Pathogenesis of Raynaud’s phenomenon. Rheumatology 44, 587–596 (2005).

    Article  PubMed  CAS  Google Scholar 

  111. Grassi, W. & De Angelis, R. Capillaroscopy: questions and answers. Clin. Rheumatol. 26, 2009–2016 (2007).

    Article  PubMed  Google Scholar 

  112. Maricq, H. R. & LeRoy, E. C. Patterns of finger capillary abnormalities in connective tissue disease by “wide-field” microscopy. Arthritis Rheum. 16, 619–628 (1973).

    Article  PubMed  CAS  Google Scholar 

  113. Herrick, A. L. & Cutolo, M. Clinical implications from capillaroscopic analysis in patients with Raynaud’s phenomenon and systemic sclerosis. Arthritis Rheum. 62, 2595–2604 (2010).

    Article  PubMed  Google Scholar 

  114. Maricq, H. R. et al. Diagnostic potential of in vivo microscopy in scleroderma and related disorders. Arthritis Rheum. 23, 183–189 (1980).

    Article  PubMed  CAS  Google Scholar 

  115. Maricq, H. R., Weinberger, A. B. & LeRoy, E. C. Early detection of scleroderma-spectrum disorders by in vivo capillary microscopy: a prospective study of patients with Raynaud’s phenomenon. J. Rheumatol. 9, 289–291 (1983).

    Google Scholar 

  116. Cutolo, M. et al. Assessing microvascular changes in systemic sclerosis diagnosis and management. Nat. Rev. Rheumatol. 6, 578–587 (2010).

    Article  PubMed  Google Scholar 

  117. Chen, Z. Y. et al. Association between fluorescent antinuclear antibodies, capillary patterns, and clinical features in scleroderma spectrum disorders. Am. J. Med. 77, 812–822 (1984).

    Article  PubMed  CAS  Google Scholar 

  118. Caramaschi, P. et al. Scleroderma patients nailfold videocapillaroscopic patterns are associated with disease subset and disease severity. Rheumtology 46, 1566–1569 (2007).

    Article  CAS  Google Scholar 

  119. Cutolo, M. et al. Nailfold videocapillaroscopic patterns and serum autoantibodies in systemic sclerosis. Rheumatology 43, 719–726 (2004).

    Article  PubMed  CAS  Google Scholar 

  120. Smith, V. et al. Do worsening scleroderma capillaroscopic patterns predict future severe organ involvement? A pilot study. Ann. Rheum. Dis. 71, 1636–1639 (2012).

    Article  PubMed  Google Scholar 

  121. Sulli, A. et al. Timing of transition between capillaroscopic patterns in systemic sclerosis. Arthritis Rheum. 64, 821–825 (2012).

    Article  PubMed  Google Scholar 

  122. Bredemeier, M. et al. Nailfold capillary microscopy can suggest pulmonary disease activity in systemic sclerosis. J. Rheumatol. 31, 286–294 (2004).

    PubMed  Google Scholar 

  123. Hofstee, H. M. et al. Nailfold capillary density is associated with the presence and severity of pulmonary arterial hypertension in systemic sclerosis. Ann. Rheum. Dis. 68, 191–195 (2009).

    Article  PubMed  CAS  Google Scholar 

  124. Sebastiani, M. et al. Predictive role of capillaroscopic skin ulcer risk index in systemic sclerosis: a multicenter validation study. Ann. Rheum. Dis. 71, 67–70 (2012).

    Article  PubMed  CAS  Google Scholar 

  125. Lambova, S. & Muller-Ladner, U. Capillaroscopic findings in systemic sclerosis — are they associated with disease duration and presence of digital ulcers. Discov. Med. 12, 413–418 (2011).

    PubMed  Google Scholar 

  126. Cutolo, M., Pizzorni, C., Sulli, A. & Smith, V. Early diagnostic and predictive value of capillaroscopy in systemic sclerosis. Curr. Rheumatol. Rev. 9, 249–253 (2013).

    Article  PubMed  Google Scholar 

  127. Cutolo, M. et al. Nailfold videocapillaroscopic features and other clinical risk factors for digital ulcers in systemic sclerosis: A multicenter prospective cohort study. Arthritis Rheumatol. 68, 2527–2539 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  128. van den Hoogen, F. et al. 2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 65, 2737–2747 (2013).

    Google Scholar 

  129. Nihtyanova, S. I. & Denton, C. P. Autoantibodies as predictive tools in systemic sclerosis. Nat. Rev. Rheumatol. 6, 112–116 (2010).

    Article  PubMed  CAS  Google Scholar 

  130. Villalta, D. et al. Diagnostic accuracy and predictive value of extended autoantibody profile in systemic sclerosis. Autoimmun. Rev. 12, 114–120 (2012).

    Article  PubMed  CAS  Google Scholar 

  131. Domsic, R. T. Scleroderma: the role of serum autoantibodies in defining specific clinical phenotypes and organ system involvement. Curr. Opin. Rheumatol. 26, 646–652 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  132. Sirotti, S. et al. Personalized medicine in rheumatology: the paradigm of serum autoantibodies. Auto. Immun. Highlights 8, 10 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Mueller, M. et al. Relation of nailfold capillaries and autoantibodies to mortality in patients with Raynaud phenomenon. Circulation 133, 509–517 (2016).

    Article  PubMed  CAS  Google Scholar 

  134. Sulli, A. et al. Progression of nailfold microvascular damage and antinuclear antibody pattern in systemic sclerosis. J. Rheumatol. 40, 634–639 (2013).

    Article  PubMed  CAS  Google Scholar 

  135. Xu, G. J. et al. Systemic autoantigen analysis identifies a distinct subset of scleroderma with coincident cancer. Proc. Natl Acad. Sci. USA 113, 57526–57534 (2016).

    Google Scholar 

  136. Milano, A. et al. Molecular subsets in the gene expression signatures of scleroderma skin. PLoS ONE 3, e2696 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Sargent, J. L. & Whitfield, M. L. Capturing the heterogeneity in systemic sclerosis with genome-wide expression profiling. Expert Rev. Clin. Immunol. 7, 463–473 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Sargent, J. L. et al. A TGFβ-responsive gene signature is associated with a subset of diffuse scleroderma with increased disease severity. J. Invest. Dermatol. 130, 694–705 (2010).

    Article  PubMed  CAS  Google Scholar 

  139. Pendergrass, S. A. et al. Limited systemic sclerosis patients with pulmonary arterial hypertension show biomarkers of inflammation and vascular injury. PLoS ONE 5, e12106 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  140. Lenna, S. et al. Increased expression of endoplasmic reticulum stress and unfolded protein response genes in peripheral blood mononuclear cells from patients with limited cutaneous systemic sclerosis and pulmonary arterial hypertension. Arthritis Rheum. 65, 1357–1366 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Derrett-Smith, E. C. et al. Limited cutaneous systemic sclerosis skin demonstrates distinct molecular subsets separated by a cardiovascular development gene expression signature. Arthritis Res. Ther. 19, 156 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Mahoney, J. M. et al. Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms. PLoS Comput. Biol. 11, e1004005 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  143. Farina, G., Lafyatis, D., Lemaire, R. & Lafyatis, R. A four-gene biomarker predicts skin disease in patients with diffuse cutaneous systemic sclerosis. Arthritis Rheum. 62, 580–588 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Rice, L. M. et al. A longitudinal biomarker for the extent of skin disease in patients with diffuse cutaneous systemic sclerosis. Arthritis Rheumatol. 67, 3004–3015 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  145. Lofgren, S. et al. Integrated multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity. JCI Insight 1, e89073 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  146. Taroni, J. N. et al. Molecular characterization of systemic sclerosis esophageal pathology identifies inflammatory and proliferative signatures. Arthritis Res. Ther. 17, 194 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  147. Taroni, J. N. et al. A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis. Genome Med. 9, 27 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  148. Hinchcliff, M. et al. Molecular signatures in skin associated with clinical improvement during mycophenolate treatment in systemic sclerosis. J. Invest. Dermatol. 133, 1979–1989 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  149. Chakravarty, E. F. et al. Gene expression changes reflect clinical response in placebo-controlled randomized trial of abatacept in patients with diffuse cutaneous systemic sclerosis. Arthritis Res. Ther. 17, 159 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  150. Rice, L. M. et al. Fresolimumab treatment decreases biomarkers and improves clinical symptoms in systemic sclerosis patients. J. Clin. Invest. 125, 2795–2807 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Taroni, J. N., Martyanov, V., Mahoney, J. M. & Whitfield, M. L. A functional genomic meta-analysis of clinical trials in systemic sclerosis: toward precision medicine and combination therapy. J. Clin. Invest. Dermatol. 137, 1033–1041 (2017).

    Article  CAS  Google Scholar 

  152. Etheridge, A. et al. The complexity, function and application of RNA in circulation. Front. Genet. 4, 115 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  153. Etheridge, A. et al. Extracellular microRNA: a new source of biomarkers. Mutat. Res. 717, 85–90 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  154. Witwer, K. W. Circulating microRNA biomarker studies: pitfalls and potential solution. Clin. Chem. 61, 56–63 (2015).

    Article  PubMed  CAS  Google Scholar 

  155. Bartel, D. P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004).

    Article  PubMed  CAS  Google Scholar 

  156. Eulalio, A., Huntzinger, E. & Izaurralde, E. Getting to the root of miRNA-mediated gene silencing. Cell 132, 9–14 (2008).

    Article  PubMed  CAS  Google Scholar 

  157. Treiber, T., Treiner, N. & Meister, G. Regulation of microRNA biogenesis and function. Thromb. Haemost. 107, 605–610 (2012).

    Article  PubMed  CAS  Google Scholar 

  158. Olive, V., Minella, A. C. & He, L. Outside the coding genome, mammalian microRNAs confer structural and functional complexity. Sci. Signal. 8, re2 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  159. Tanaka, S. et al. Alteration of circulating miRNAs in SSc: miR-30b regulates the expression of PDGF receptor β. Rheumatology 52, 1963–1972 (2013).

    Article  PubMed  CAS  Google Scholar 

  160. Makino, K. et al. Circulating miR-142-3p levels in patients with systemic sclerosis. Clin. Exp. Dermatol. 37, 34–39 (2012).

    Article  PubMed  CAS  Google Scholar 

  161. Honda, N. et al. miR-150 down-regulation contributes to the constitutive type I collagen overexpression in scleroderma dermal fibroblasts via the induction of integrin β3. Am. J. Pathol. 182, 206–216 (2013).

    Article  PubMed  CAS  Google Scholar 

  162. Honda, N. et al. TGF-β-mediated downregulation of microRNA-196a contributes to the constitutive upregulated type I collagen expression in scleroderma dermal fibroblast. J. Immunol. 18, 3323–3331 (2012).

    Article  CAS  Google Scholar 

  163. Makino, K. et al. The downregulation of microRNA let-7a contributes to the excessive expression of type I collagen in systemic and localized scleroderma. J. Immunol. 190, 3905–3915 (2013).

    Article  PubMed  CAS  Google Scholar 

  164. Sing, T. et al. microRNA-92a expression in the sera and dermal fibroblast increases in patients with scleroderma. Rheumatology 51, 1550–1556 (2012).

    Article  PubMed  CAS  Google Scholar 

  165. Wuttge, D. M. et al. Specific autoantibody profiles and disease subgroups correlate with circulating micro-RNA in systemic sclerosis. Rheumatology 54, 2100–2107 (2015).

    Article  PubMed  CAS  Google Scholar 

  166. Théry, C., Ostrowsky, M. & Segura, E. Membrane vesicles as conveyors of immune responses. Nat. Rev. Immunol. 9, 581–593 (2009).

    Article  PubMed  CAS  Google Scholar 

  167. Gyorgy, B. et al. Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles. Cell. Mol. Life Sci. 68, 2667–2688 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  168. Raposo, G. & Stoorvogel, W. Extracellular vesicles: exosomes, microvesicles, and friends. J. Cell Biol. 200, 373–383 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  169. Colombo, M., Raposo, G. & Thery, C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu. Rev. Cell Dev. Biol. 30, 255–289 (2014).

    Article  PubMed  CAS  Google Scholar 

  170. Théry, C., Zitvogel, L. & Amigorena, S. Exosomes: composition, biogenesis and function. Nat. Rev. Immunol. 2, 569–579 (2002).

    Article  PubMed  CAS  Google Scholar 

  171. Vlassov, A. V., Magdaleno, S., Setterquist, R. & Conrad, R. Exosomes: current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials. Biochim. Biophys. Acta 1820, 940–948 (2012).

    Article  PubMed  CAS  Google Scholar 

  172. Pant, S., Hilton, H. & Burczynski, M. E. The multifaceted exosome: biogenesis, role in normal and aberrant cellular function, and frontiers for pharmacological and biomarker opportunities. Biochem. Pharmacol. 83, 1484–1494 (2012).

    Article  PubMed  CAS  Google Scholar 

  173. Lotvall, J. et al. Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles. J. Extracell. Vesicles 3, 26913 (2014).

    Article  PubMed  Google Scholar 

  174. Hsu, V. W. & Prekeris, R. Transport at the recycling endosome. Curr. Opin. Cell Biol. 22, 528–534 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  175. Hessvik, N. P. & Llorente, A. Current knowledge on exosome biogenesis and release. Cell. Mol. Life Sci. 75, 193–208 (2018).

    Article  PubMed  CAS  Google Scholar 

  176. Ratajczak, J. et al. Embryonic stem cell-derived microvesicles reprogram hematopoietic progenitors: evidence for horizontal transfer of mRNA and protein delivery. Leukemia 20, 847–856 (2006).

    Article  PubMed  CAS  Google Scholar 

  177. Skog, J. et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat. Cell Biol. 10, 1470–1476 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  178. Valadi, H. et al. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol. 9, 654–659 (2007).

    Article  PubMed  CAS  Google Scholar 

  179. Gusachenko, O. N., Zenkova, M. A. & Vlassov, V. V. Nucleic acids in exosomes: disease markers and intercellular communication molecules. Biochemistry 78, 1 (2013).

    PubMed  CAS  Google Scholar 

  180. Shurtleff, M. J. et al. Broad role for YBX1 in defining the small noncoding RNA composition of exosomes. Proc. Natl Acad. Sci. USA 114, E8987–E8995 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  181. Shelke, G. V., Jang, S. D., Yin, Y., Lasser, C. & Lotvall, J. Human mast cells release extracellular vesicle-associated DNA. Matters https://doi.org/10.19185/matters.201602000034 (2016).

    Article  Google Scholar 

  182. Nemeth, A. et al. Antibiotic-induced release of small extracellular vesicles (exosomes) with surface-associated DNA. Sci. Rep. 7, 8202 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  183. Simpson, R. J., Lim, J. W., Moritz, R. L. & Mathivanan, S. Exosomes: proteomic insights and diagnostic potential. Expert Rev. Proteom. 6, 267–283 (2009).

    Article  CAS  Google Scholar 

  184. Simpson, R. J., Jensen, S. S. & Lim, J. W. Proteomic profiling of exosomes: current perspectives. Proteomics 8, 4083–4099 (2008).

    Article  PubMed  CAS  Google Scholar 

  185. Thery, C., Amigorena, S., Raposo, G. & Clayton, A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr. Protoc. Cell Biol. 30, 3.22.1–3.22.29 (2006).

    Article  Google Scholar 

  186. Zeringer, E., Barta, T., Li, M. & Vlassov, A. V. Strategies for isolation of exosomes. Cold Spring Harb. Protoc. 2015, 319–323 (2015).

    PubMed  Google Scholar 

  187. Michalska-Jakubus, M., Kowal-Bielecka, O., Smith, V., Cutolo, M. & Krasowska, D. Plasma endothelial microparticles reflect the extent of capillaroscopic alterations and correlate with the severity of involvement in systemic sclerosis. Microvasc. Res. 110, 24–31 (2017).

    Article  PubMed  CAS  Google Scholar 

  188. Zhu, H., Luo, H. & Zuo, X. MicroRNAs: their involvement in fibrosis pathogenesis and use as diagnostic biomarkers in scleroderma. Exp. Mol. Med. 45, e41 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  189. Steen, S. O. et al. The circulating cell-free microRNA profile in systemic sclerosis is distinct from both healthy controls and systemic lupus erythematosus. J. Rheumatol. 42, 214–221 (2015).

    Article  PubMed  CAS  Google Scholar 

  190. Wermuth, P. J., Piera-Velazquez, S. & Jimenez, S. A. Exosomes isolated from serum of systemic sclerosis patients display alterations in their content of profibrotic and antifibrotic microRNA and induce a profibrotic phenotype in cultured normal dermal fibroblast. Clin. Exp. Rheumatol. 35 (Suppl. 106), 21–30 (2017).

    PubMed  PubMed Central  Google Scholar 

  191. Simpson, R. J., Kalra, H. & Mathivanan, S. ExoCarta as a resource for exosomal research. J. Extracell. Vesicles. 1, 18374 (2012).

    Article  CAS  Google Scholar 

  192. Keerthikumar, S. et al. ExoCarta: A web-based compendium of exosomal cargo. J. Mol. Biol. 248, 688–692 (2016).

    Article  CAS  Google Scholar 

  193. Kalra, H. et al. Vesiclepedia: A compendium for extracellular vesicles with continuous community annotation. PLoS Biol. 10, e1001450 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  194. Kim, D. K., Lee, J., Simpson, R. J., Lotvall, J. & Gho, Y. S. EVpedia: A community web resource for prokaryotic and eukaryotic extracellular vesicles research. Semin. Cell Dev. Biol. 40, 4–7 (2015).

    Article  PubMed  CAS  Google Scholar 

  195. Choi, D. S., Kim, D. K., Kim, Y. K. & Gho, Y. S. Proteomics of extracellular vesicles: exosomes and ectosomes. Mass Spectrom. Rev. 34, 474–490 (2015).

    Article  PubMed  CAS  Google Scholar 

  196. Schey, K. L., Luther, J. M. & Rose, K. L. Proteomics characterization of exosome cargo. Methods 87, 75–82 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  197. Abramowicz, A., Widlak, P. & Pietrowska, M. Proteomic analysis of exosomal cargo: the challenge of high purity vesicle isolation. Mol. Biosyst. 12, 1407–1419 (2016).

    Article  PubMed  CAS  Google Scholar 

  198. Wermuth, P. J., Piera-Velazquez, S. & Jimenez, S. A. Identification of novel systemic sclerosis biomarkers employing aptamer proteomic analysis. Rheumatology https://doi.org/10.1093/rheumatology/kex404 (2017).

    Article  Google Scholar 

  199. Burmester, G. R., Bijlsma, J. W. J., Cutolo, M. & McInnes, I. B. Managing rheumatic and musculoskeletal diseases — past, present and future. Nat. Rev. Rheumatol. 13, 443–448 (2017).

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

The authors acknowledge the expert assistance of A. Pagano in the preparation of this manuscript. The work of the authors is supported in part by grants R21AR071644 (to S.A.J., P.J.W. and S.P.-V.) and R01AR19616 (to S.A.J.) from the NIH. The work of P.J.W. is supported by a Research Award from the Scleroderma Foundation.

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The authors declare no competing interests.

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S.A.J. and P.J.W. researched data for the article and wrote the article. All authors contributed equally to discussions of the content of the article and reviewed and/or edited the manuscript before submission.

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Correspondence to Sergio A. Jimenez.

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Related links

EVpedia: http://student4.postech.ac.kr/evpedia2_xe/xe/

ExoCarta: http://www.exocarta.org/

Vesiclepedia: http://www.microvesicles.org/

Glossary

Cutaneous induration

Thickening of the dermal and hypodermal layers of the skin as a result of oedema, inflammation or infiltration of immune cells.

Selected reaction monitoring

An emerging targeted mass spectrometry technique for peptide biomarker discovery and validation that is able to quantify the hundreds to several thousands of peptides that are present in complex biofluids in a single experiment.

Aptamers

Short single-stranded modified oligonucleotides that are able to bind to proteins, peptides and small molecules with extremely high specificity.

Biomarker sensitivity

The ability of a biomarker to be measured with adequate precision and with a magnitude of change capable of detection.

Biomarker specificity

The ability of a biomarker to distinguish between patients with different disease subtypes or between patients who do and do not respond to therapy.

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Wermuth, P.J., Piera-Velazquez, S., Rosenbloom, J. et al. Existing and novel biomarkers for precision medicine in systemic sclerosis. Nat Rev Rheumatol 14, 421–432 (2018). https://doi.org/10.1038/s41584-018-0021-9

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