Extracellular vesicles. Rienk Nieuwland

Transcription

1 Extracellular vesicles in the AMC Rienk Nieuwland

2 Clinical relevance of EVs

3 VOC members (AMC) René Berckmans, PhD Frank Coumans, PhD (VENI) Elmar Gool, PhD student Aleksandra Gasecka, MD/PhD student/cardiologist Chi Hau, research technician Doortje Horjus, PhD student Najat Hajji, research technician Ton van Leeuwen, PhD Rienk Nieuwland, PhD Edwin van der Pol, PhD (VENI) Linda Rikkert, PhD student Leonie de Rond, PhD student Marianne Schaap, research technician Guus Sturk, PhD Johannes Thaler, MD/PhD/Haematologist Yuanjie Yu, PhD student

4 EV research lines AMC 1. Detection and isolation 2. Standardization 3. Therapy 4. Pathology 5. Biomarker

5 1. Detection and isolation 1. Signal analysis 2. Development Flow cytometry Raman microspectroscopy Surface plasmon resonance imaging Transmission electron microscopy Physical parameters (size, refractive index) 3. Isolation Size exclusion chromatography

6 1.1 Signal analysis L d D I + I ΔI J Extracell Vesicles Dec 10;3: doi: /jev.v

7 1.1 Signal analysis

8 1.2 Flow cytometry Leonie de Rond, PhD; Poster 19: generic fluorescent dyes

9 1.2 Hybrid Raman microspectroscopy and resistive pulse sensing (label-free) Hybrid AMC Cees Otto (UT)

10 1.2 Surface Plasmon Resonance imaging Elmar Gool, PhD

11 1.2 Transmission Electron Microscopy Linda Rikkert, PhD; poster 18: comparison of TEM protocols

12 1.2 Physical parameters

13 Proof of the pudding: Have developments lead to improved detection?

14 1.2 Size exclusion chromatography JEV 2014, 3:

15 1.2 Size exclusion chromatography

16 EV research lines AMC Detection and isolation 2. Standardization 3. Therapy 4. Pathology 5. Biomarker

17 2. Standardization Prerequisite for clinical application 1. Pre-analytical variables (METVES, ISTH) 2. Flow cytometry ISEV-ISTH-ISAC workgroup Scatter to size Scatter to size and refractive index (Flow-SR) Hollow silica beads Exometry

18 2.1 Pre-analytical variables Circulation Research 2017; 120:

19 2.2 Flow cytometry workgroup

20 2.2 From scatter to size (requires refractive index) Light scattering (a.u.) 20

21 2.2 From scatter to size & refractive index

22 2.2 Standardization Aim Contact Convert EV scatter signal to diameter

23 EV research lines AMC Detection and isolation Standardization 3. Therapy 4. Pathology 5. Biomarker

24 3. Therapy Aim: Contact: Collaboration: CD39/CD73 EVs for treatment of inflammatory disease (Arthrogen) VOC

25 EV research lines AMC Detection and isolation Standardization Therapy 4. Pathology 5. Biomarker

26 4. Department of Intensive Care Aim Contact Collaboration Role of EVs in transfusion-associated outcome of critically ill and injured patients Sanquin, VOC

27 4. Department of Pathology Aim Contact Collaboration Role of EVs in development of diabetic nephropathy Slotervaart, VUmc, VOC

28 4. Lab of Experimental Immunology Aim Contact Collaboration Role innate immune effector cell-derived EVs in dendritic cell-driven immune regulation UU

29 EV research lines AMC Detection and isolation Standardization Therapy Pathology 5. Biomarker

30 5. EVs as biomarker 1. Cancer-ID 2. Parasites 3. Coagulation Department of Vascular medicine (AMC) 4 th PhD student

31 5.1 Cancer-ID

32 5.2 Biomarker Leishmania major exosomes Merlin van Loenen Aim Contact Collaboration Exosomes from parasites for diagnosis, drug and vaccine development (Parasitology) Swiss Tropical Institute, Aarhus Univ.

33 5.3 Coagulation Venous thromboembolism (VTE) is second cause of death of all hospitalized cancer patients OR 6.5 when patients receive chemotherapy Risk for thrombosis highest in first 3 months and in metastasized disease Current guidelines recommend against routine thromboprophylaxis in ambulatory patients Arch Intern Med 2000;160: ; Crit Rev Oncol Hematol 2008; 66: ; Ann Oncol 2010; 21: Suppl 5: v274-6; LoS Med 2012; 9(7): e ; Blood 2013; 122: ; J Thromb Haemost 2013; 11: 56-70; J Clin Oncol 2013; 31: 654-6; Cochrane Database Syst Rev 2016; 12(2): CD008500

34 Study questions Can we identify cancer patients at high risk of developing VTE? Can we prevent thrombosis in cancer patients at risk of developing VTE?

35 Clinical prediction models Khorana score Risk score: 0 points = low risk; 1-2 points: intermediate risk; 3 points: high risk Blood 2008; 111:

36 876 patients Various types of cancer

37 Conclusion The present findings do not support the use of any of the examined scores to select patients for thromboprophylaxis Haematologica 2017; 102(9):

38 5.3 Coagulation (hemostasis) 41

39 18,000 x g 137,000 x g Supernatant 18,000 x g 137,000 x g Supernatant 18,000 x g 137,000 x g Supernatant 200 nm donor 1 donor 2 donor 3 Blood 2011; 117:

40 Tumors release TF+ EVs Lancet 1995;346:1004-5; Br J Cancer 2007;96:290-5; J Thromb Haemostas 2017;15:1-10

41 Can a TF-EV clotting test predict VTE in cancer patients?

42 Procoagulant activity of TF-EVs for prediction of VTE in cancer patients Prospective cohort study 648 patients 40 patients developed VTE (6.1%) 6 hospitals Various types / stages of cancer Primary outcome: VTE (6-months follow-up)

43 Prediction of VTE

44 VTE prediction in pancreatic cancer

45

46 Statuten Fryske Ferien foar

47 René Berckmans Harry Büller Frank Coumans Frederiek van Doormaal Nick van Es Elmar Gool Aleksandra Gasecka Chi Hau Doortje Horjus Najat Hajji Ankie Kleinjan Romaric Lacroix (France) Ton van Leeuwen Rienk Nieuwland Edwin van der Pol Linda Rikkert Leonie de Rond Marianne Schaap Pia Siljander (Finland) Guus Sturk Johannes Thaler Sami Valkonen (Finland) Zoltán Varga (Hungary) Yuanjie Yu Yuana Yuana Acknowledgements