In Search of Cell Patterns in Blood for Individualized Medicine
Immune monitoring includes the combination of various procedures of diagnosis, which provide information about the immune state of a patient. Depending on the clinical discipline, humoral factors, like cytokines, antibody titres or complement factors, the cellular composition of peripheral blood, functional-cellular parameters or rather a combination of them all, can be determined. Immune monitoring programmes already assist in the diagnosis and treatment of many complex clinical manifestations. For example, physicians use immune monitoring in transplantation medicine, in cases of sepsis, in the course of immune modulatory therapies, and in vaccine strategies to evaluate specific immune responses. The immune monitoring core facility aims to identify immunophenotypic signatures as biomarkers for monitoring disease activity and prediction of treatment responses.
Siglec-1 a new biomarker in lupus diagnostics
Siglec-1 is an adhesion molecule specifically expressed in peripheral monocytes that was originally identified by cell-specific transcriptomic studies as a type I interferon surrogate marker. Interestingly, in cooperation with our clinical liaison partners it could be shown that an increased expression of Siglec-1 correlates with disease activity in patients suffering in SLE and Sjögren’s syndrome (Biesen et al., 2008; Rose et al., 2016). Based on these promising results, Siglec-1 has been extensively investigated as a new biomarker in recent years and has been validated in various clinical studies (Rose et al. 2013; Rose et al. 2016; Rose et al. 2017), so that it could be shown to have a higher diagnostic potential than other gold standards in lupus diagnostics, e.g. autoantibody titres or the consumption of complement factors. Meanwhile, this biomarker has been successfully translated from bench to routine diagnostics and the quantitative measurement of this parameter is offered in the portfolio of the diagnostic laboratory of the Charité (“Labor Berlin”). Therefore, this is a paradigm of how modern high-throughput technologies can be used to identify promising, new molecular biomarkers, which will be successfully validated in well-designed clinical trials. This is all the more remarkable because similar to the development of drugs, only very few biomarker candidates overcome hurdles of further validation studies. In the context of Siglec-1 as type I interferon surrogate marker, it was also investigated in detail why up to now no other blood-based biomarkers, such as the interferon signature, i.e. a specific pattern of genes regulated by IFN-α/β in blood cells, has been reached diagnostic routine in rheumatology. Here, we could show by a comprehensive study comparing own gene expression data and those published by other groups, that interferon signatures are strongly influenced by the cellular composition of white blood cells. Each type of immune cell type responds with another set of interferon-associated genes and it could be concluded that only interferon signatures generated in a cell-specific manner are useful for diagnostic purposes (Strauss et al., 2017).
NK cells as predictors for a successful anti-TNF-a therapy response
Unfortunately, at this stage it is not possible to predict whether a patient will respond to a specific therapy or not before starting of therapy. However, this is exactly the goal of the so-called personalized or individualized medicine aiming to match the best possible therapy to the individual patient. This minimizes the risk of adverse side effects and can save enormous health care costs, since on average only 50%-60% of rheumatoid patients treated with expensive biologics benefit from a noticeable improvement in their disease symptoms. In collaboration with Prof. Jochen Sieper, Prof. Dennis Poddubnyy and Dr. Uta Syrbe at the Charité campus Benjamin Franklin, we conducted an extensive immune monitoring study on ankylosing spondylitis patients treated with TNF-α blocking drugs. It could be shown that in blood of these patients, the frequency of certain natural killer cell subpopulations allows a prediction of the treatment success with TNF-α blockers even before starting therapy. These results have just been submitted for publication in “Scientific Reports”.
Multidimensional data sets generated especially by mass cytometry requires new algorithms which automatically recognize and quantify relevant cell populations. For this purpose, a software (“immunoClust”, Sörensen et al., 2015) has been developed by bioinformaticians and mathematicians of the DRFZ and the department for medical bioinformatics of the rheumatology at the Campus Charité Mitte.
The complex immune monitoring approach described is used to accompany therapy studies to identify predictors of therapy responses. Furthermore, attempts are currently being made to develop algorithms which allow automation of the primary data analysis. Ultimately, it is necessary to look for ways to adjust and optimize immune monitoring of larger sample quantities with regard to a high-throughput method.
Type I interferon signatures
Dr. rer.nat. Andreas Grützkau
Dr. rer. nat. Sabine Baumgart
Type I interferon signatures
Strauß R, Rose T, Flint SM, Klotsche J, Häupl T, Peck-Radosavljevic M, Yoshida T, Kyogoku C, Flechsig A, Becker AM, Dao KH, Radbruch A, Burmester GR, Lyons PA, Davis LS, Hiepe F, Grützkau A, Biesen R. Type I interferon as a biomarker in autoimmunity and viral infection: a leukocyte subset-specific analysis unveils hidden diagnostic options. J Mol Med (Berl). 2017 Mar 29.
Rose T, Grützkau A, Klotsche J, Enghard P, Flechsig A, Keller J, Riemekasten G, Radbruch A, Burmester G-R, Dörner T, Hiepe F and Biesen R. Are interferon-related biomarkers advantageous for monitoring disease activity in SLE? A longitudinal benchmark study. Rheumatol. 2017 (in press)
Rose T, Szelinski F, Lisney A, Reiter K, Fleischer SJ, Burmester GR, Radbruch A, Hiepe F, Grützkau A, Biesen R, Dörner T. SIGLEC1 is a biomarker of disease activity and indicates extraglandular manifestation in primary Sjögren’s syndrome. RMD Open. 2016 Dec 30;2(2):e000292.
Sörensen T, Baumgart S, Durek P, Grützkau A, Häupl T. immunoClust–An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A. 2015 Jul;87(7):603-15.
Rsk2 controls synovial fibroblast hyperplasia and the course of arthritis.Derer A, Böhm C, Grötsch B, Grün JR, Grützkau A, Stock M, Böhm S, Sehnert B, Gaipl U, Schett G, Hueber AJ, David JP. Ann Rheum Dis. 2016 Feb;75(2):413-21. doi: 10.1136/annrheumdis- 2014-205618. PMID: 25414238 [PubMed – indexed for MEDLINE]
Type I interferon signatures