rubina baglio elisa giovannetti irene bijnsdorp jan vdweering kickof… · •alterations of the...
TRANSCRIPT
Connie Jimenez
Rubina Baglio Elisa Giovannetti
Michiel Pegtel Tom Wurdinger
Irene Bijnsdorp Jan vd Weering
Renee Musters
Data-Independent
Acquisition
Mass Spectrometry
Shot-gun
Mass Spectrometry
IntegrativeAnalysisMulti-Omics,
Functional,
Experimental,
Clinical)
ExtracellularProteomicsLiquid biopsies
(Phospho)ProteomicsSolid biopsy
Clinical Proteomics @VUmc
Bioinformatics, Biostatistics, Computational Biology
Personalized(combination)
therapy
Non-invasive(early)
diagnostics
Collaboration, Training, Dissemination, Valorisation
Label-freeProteomics
Quality control
Careful
study
design
EV/ Exosome Proteomics VUmc
Connie Jimenez, [email protected] www.oncoproteomics.nl
EV/ Exosome Proteomics VUmc
EV proteome analysis of biomarker• -rich proximal fluids: Cancer cell & tumor tissue microenvironment (– secretome)Urine, CSF–
Novel HTP EV capture method bench• -markedagainst ultracentrifugation for EV proteomics
HSP à EV peptide capture method enables global exosome proteomics
Bijnsdorp, Jimenez et al. Feasibility of urinary extracellular vesicle proteome profiling using a robust and simple, clinically applicable isolation method.J Extracell Vesicles. 2017; 6(1):1313091.
2757 80.1%
358
10.4%
UC-EV 3115 IDs
HSP-EV 3085 IDs
328
9.5%
3443 IDs
Figure 2A
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●●
●
●
●
●●●
●●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●●
●●●
●
●
●
●
●
●●●
●
●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●●
●
●
●
●
●
●●
●
●●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●●
●
●●●●
●
●
●
●
●
●
●
●
●
●●
●●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●●
●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●
●●
●●●
●
●
●
●●
●●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●●
●
●
●●
●
●●●
●
●●●
●
●
●
●●
●
●
●●
●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●●●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●●●●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●●●
●
●
●
●●
●
●
●
●●●
●
●
●●
●
●
●
●
●●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●●
●
●
●
●●
●
●
●
●
●●●●
●●
●
●●●●●
●
●
●
●
●●
●
●
●●●
●●
●
●
●
●
●●●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●●
●●●●
●
●●
●●
●
●
●●
●
●●●●●●●
●●
●●
●●●●
●●
●●
●
●
●●
●●
●
●●
●
●
●●
●●●●●●●●●
●●●
●
●
●
●
●
●●●
●●
●
●
●●●●●
●
●
●
●
●
●●●●
●●●
●●●
●●
●
●●
●
●●●●●
●●
●
●
●●●
●●●
●
●●●
●
●●●
●●
●
●
●
●
●
●●●
●
●
●
●●
●●●●●●
●●●●●●
●
●
●●●
●●●
●●●●●●●●●●
●
●●
●
●●
●●
●●●●●●●
●
●●●●●●●
●
●●●
●●●●
●●
●●
●
●
●
●
●●
●
●
●
●
●●●
●
●●●
●●●●●●●●●●
●
●
●●
●●
●
●●●●●
●●●●
●●
●
●●
●●●●
●●
●●●●●
●●●●●●●
●●●●●●●●
●
●●●●
●●●●●●
●
●
●
●●●
●●●●●●●●●
●
●●●●●●
●●
●●●●
●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
UC−EV vs HSP−EV
UC−EV Replicate Average (log2Counts)
HSP
−EV
Rep
licat
e Av
erag
e (l
og2C
ount
s)
0 2 4 6 8 10
2
4
6
8
10r = 0.882ρ = 0.755
Figure 2D
Knol, Jiménez et al. Peptide-mediated 'miniprep' isolation of extracellular vesicles is suitable for high-throughput proteomics. EuPA Open Proteomics Volume 11, June 2016, Pages 11–15
urinary extracellular vesicle proteome
On-going: Large-scale application to urine of prostate cancer patients
EV proteomics pilot on Alzheimer’s disease CSF
EV/ Exosome Proteomics VUmc
CSF EV proteins involved in neurodegenerative diseases
Chiasserini, Jiménez et al. Proteomic analysis of cerebrospinalfluid extracellular vesicles: A comprehensive dataset. J Proteomics. 2014 Apr 24;106C:191-204.
First CSF EV proteome
On-going: Optimization of HSP EV capture for CSF exosome proteomics to enable large scale profiling in brain diseases
Prostate cancer EV researchDepartment of Urology, Irene Bijnsdorp
1. Proteomics profiling or urinary EVs:- Diagnostic & prognostic testing
- Developed a clinical applicable urinary EV-capture method- Bijnsdorp et al, J Extracell Vesicles, 2017
- Large biobank: urine and blood sample collection- Current: validation of markerpanel
2. Biological role of prostate cancer EVs in metastasis:- Focus on preventing bone metastasis
- Mouse studies demonstrated that cancer EVsinfluence early metastasis:
formation and type of bone metastases
Early metastasis formed
miR-CmiR-BmiR-APC3-ctrl
ConsorGum: Jimenez, Jenster and Schalken)
Predictive and monitoring biomarkers are essential to guide patient therapy in pancreatic
cancer
à Find predictive and monitoring miRNAs
Methods: PCR array
Circula<ng microRNAs as dynamic biomarkers of response to treatment with FOLFIRINOX combina<on therapy in advanced pancrea<c ductal adenocarcinoma. Laura L Meijer, Adam E Frampton, Ingrid Garajova, Chiara Caparello, Tessa Y S Le Large, Niccola Funel, Enrico Vasile, JusHn Stebbing, Jonathan Krell, Geert Kazemier, Elisa GiovanneL hMp://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(17)30464-6.pdf
miR-29a is significantly upregulated in EVs a8er treatment in non-progressive pa;ents
Before treatment A8er treatment
Valida;onà to explore the monitoring poten;alFunc;onalà experiments
Circula;ng microRNAs as dynamic biomarkers of response to treatment with FOLFIRINOX combina;on therapy in advanced pancrea;c ductal adenocarcinoma. Laura L Meijer, Adam E Frampton, Ingrid Garajova, Chiara Caparello, Tessa Y S Le Large, Niccola Funel, Enrico Vasile, JusDn Stebbing, Jonathan Krell, Geert Kazemier, Elisa GiovanneH hIp://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(17)30464-6.pdf
Before
trea
tmen
t
After t
reat
men
t-6
-5
-4
-3
-2
-1
0
1
miR-28
∆ct$value$normalize
d$with
$miR493$
Before
trea
tmen
t
After t
reat
men
t-6
-5
-4
-3
-2
-1
0
1
miR-29a
**
∆ct$value$normalize
d$with
$miR493$
Before
trea
tmen
t
After t
reat
men
t-6
-5
-4
-3
-2
-1
0
1
miR-181a
∆ct$value$normalize
d$with
$miR493$
Before treatment After treatment
miR-29a in EVs
P=0.0024
Cell-free miR-29a
ERG Research
Small RNA • sorting into EVs and functional transferPegtel et al., PNAS 2010-Koppers- -Lalic et al., Cell Reports 2014
Viral• RNAs and EVs in autoimmunityPegtel PNAS 2014-Baglio et al., PNAS 2016-Van Dongen et al., MMBR 2016-
EV small • RNAs for Liquid biopsy approaches- Van Eijndhoven et al., JCI insight 2016
Exosome biogenesis and release •Verweij et al., EMBO 2011-Verweij- & Bebelman et al., JCB accepted
Baglio • group: EVs in the tumor-microenvironment- Baglio et al., Clin Canc Res 2017
• Integratie beeldvorming & moleculaire analyse voor
een complete diagnose
At presentation;
t=0 months
End of
treatment;
t=7 months
Follow up;
t=9 months
T
Allogenic
PB
SC
T
?
FDG-PET
T
miR21-5p let7a-5
0 6 9
0.2
0.4
0.6
0.8
1.0
months
00 6 9
0.2
0.4
0.6
0.8
1.0
months
0
rela
tive d
ecre
ase
Bloed test
Van Eijndhoven et al., JCI insight 2016
250nm
α-pHluorin-10nm
100nm
CD63-pHluorinpHluorin• : pH-sensitive GFP variantFused in 1• st extracellular loop of CD63
CD63-pHluorin positive MVB
CD63-pHluorin posi:ve Exosomes
• Development of reporter for visualization of exosome release from living single cells
Cancer cells contain hundreds of acidic vesicles (MVBs) that contain CD63-Phluorin
Verweij & Bebelman et al, JCB accepted
• Bone cancer cells release EVs that “educate” mesenchymal stem cells (MSC) to favor cancer growth and lung metastasis formation
• Alterations of the MSC cytokine expression profile can be revoked using anti-inflammatory agents in xenograft models
Baglio et al. Clin Cancer Res, 2017
0
5
10
15
20
Lung
met
asta
sis
num
ber
**
MSC EducatedMSC
+ IL-6R Ab
Lung metastasesMSC
EducatedMSC + IL-6R Ab
pSTA
T3/D
API
Click # TL2014102113371621 Oct 2014 13:37:28Bin:M (4), FOV12.5, f1, 30sFilter: OpenCamera: IVIS 11215, DW434
Series: cage 339m4,5,6Experiment: osteosarcomaLabel: Comment: Analysis Comment:
2
3
4
5
6
7
8
9
108
Color BarMin = 2e+07Max = 1e+08
bkg subflat-fieldedcosmic
Click # TL2014102113491321 Oct 2014 13:49:24Bin:M (4), FOV12.5, f1, 30sFilter: OpenCamera: IVIS 11215, DW434
Series: cage 340m4,5,6Experiment: osteosarcomaLabel: Comment: Analysis Comment:
2
3
4
5
6
7
8
9
108
Color BarMin = 2e+07Max = 1e+08
bkg subflat-fieldedcosmic
MSCIL-6
Cancer cell
EV-associatedTGFβ
Tumor growthMetastasis
Tumor microenvironment and inflammation:Preclinical mouse models to define cancer EV-induced alterations of the tumor microenvironment
Using in vivo tumor models and next-generation techniques we aim to: • Globally define the immune profile alterations induced by cancer EVs • Evaluate the efficacy of specific combinations of immunomodulatory drugs to
block the pro-metastatic effects of cancer EVs • Identify circulating EV-associated biomarkers for treatment response prediction
We are currently looking for PhD candidates, visit www.exosomes.nl or www.werkenbijvumc.nl
Tumor microenvironment and inflammation:Preclinical mouse models to define cancer EV-induced alterations of the tumor microenvironment