“the physician” - actualitÉs nÉphrologiques - jean...
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Protéomique/Proteomics
“The Physician”
Painting by: Gerrit Dou
Leiden, The Netherlands
1613-1675
Joost P Schanstra,
Inserm U1048
Toulouse
Complex diseases cannot be
adequately described by single
features
A reminder: why omics?
Omics: studying “all” molecules collectively
Limited precision of single marker X Increased precision of two markers, X und Y Higher precision of 3 markers, X, Y und Z
A reminder: why proteomics?
Genomics Proteomics Metabolomics
the potential the current status the useful left-over
250
150
100 75
50
37
25
20
15
Courtesy: C. Lacroix, Toulouse
Urinary proteins versus peptides
Proteins
Peptides
Mw
(kD
a)
- Stable
- Reduced pre analytical
handling no digestion
- Peptides are filtered
under physiological
conditions detection of early-
events before
alteration of the
filtration barrier
Analysis of the urinary peptidome
Mullen et al., Electrophoresis 2012
Liquid chromatography (LC)
Fractionation Measuring abundance
Capillary electrophoresis (CE)
Separation based on physicochemical
characteristics of molecules
Separation mostly based on the charge of molecules
MS-analysis
- 1500 - 2000 protein-polypeptides/sample
- now used to analyse >30000 urine samples
The urinary proteome/peptidome
by CE-MS
time (min)
CASE diseased
treated (Drug)
CONTROL healthy
untreated (Placebo)
Diagnostic/prognostic Pattern
Discriminatory Biomarkers
Discriminatory Biomarkers
Compiled Pattern
Compiled Pattern
Discovery
CASE diseased
treated (Drug)
CONTROL healthy
untreated (Placebo)
Blinded cohort
Diagnostic/prognostic Pattern
Validation
Diagnostic/prognostic Pattern
CASE diseased
treated (Drug)
CONTROL healthy
untreated (Placebo)
CASE diseased
treated (Drug)
CONTROL healthy
untreated (Placebo)
Unblinding
Sensitivity and Specificity
94% 89%
Use of peptidomics to
predict kidney (dys)function
• Progression of chronic kidney disease (CKD).
• Prenatal prediction of early renal failure in CAKUT patients.
• In preclinical research.
Use of peptidomics to
predict kidney (dys)function
• Progression of chronic kidney disease (CKD).
• Prenatal prediction of early renal failure in CAKUT patients.
• In preclinical research.
CKD peptidome biomarker discovery
using cross-sectional data (discovery)
Good et al., Mol Cell Proteomics 2010
273 peptides -> CKD273
Good et al., Mol Cell Proteomics 2010.
Multidimensional model
based on 273 urinary
peptides
« CKD273 »
Independent validation: cross-sectional cohorts
110 CKD
34 HC
AUC: 0.96
Molin et al., J Proteomics 2012.
137 T2D, 62 with DN
AUC: 0.96
Siwy et al., Nephrol Dial Transplant 2014.
165 T2D, 87 with DN
AUC: 0.95
CKD273-classifier score U-albumin (mg/L)
Does the peptidomics-based classifier
also predict progression of CKD? 1
5 eGFR
Measure-
ments
522 patients with CKD (different etiologies)
Follow-up:
4.5 + 2,3 y
Schanstra et al., JASN 2015
CKD273
UAER
Does the peptidomics-based classifier
also predict progression of CKD? 2
Schanstra et al., JASN 2015
Fast progressors (slope decline of ≥-5% per year, n=89)
UAER
CKD273
misclassification 35%
misclassification 25%
CKD273
55% ѵ
UAER
36% ѵ
Does the peptidomics-based classifier
also predict progression of CKD? 3
Pontillo et al., submitted
Ongoing EMA proposal to speed up drug testing in CKD: Primary efficacy endpoints:
• time to occurrence of CKD stage III or;
• incidence rate of CKD stage III or higher.
Prediction of class change? CKD II >III (n=1721) based on baseline UAER or CKD273
UAER CKD273
Does the peptidomics-based classifier
also predict progression of CKD? 4
Combinations of urinary peptides (such as the CKD273 classifier)
allow to predict progression of CKD more efficiently than urinary
albumin and can significantly be additive.
Pontillo et al., submitted
The CKD273 classifier combined
with the classical clinical
parameters (i.e; baseline eGFR
and UAER) improves the
prediction of the class change.
eGFR + UAER + CKD273
eGFR + UAER
Use of peptidomics to
predict kidney (dys)function
• Progression of chronic kidney disease (CKD).
• Prenatal prediction of early renal failure in CAKUT patients.
• In preclinical research.
• Accounts for >50% of chronic kidney disease (CKD)
in children! (< 0.5% in adults)
• Obstructive nephropathies are the most common
cause of CAKUT.
Renal dysfunction prediction in CAKUT
Decramer et al.
Nat Med 2006
Obstructive nephropathy
x
• Fetal bilateral obstructive
nephropathy.
• Rare disease, 1/8000-
25000 male births.
Nearly always associated to renal lesions:
cysts hypo/dysplasia
abnormal cortical and medullar differentiation
hyperechogenicity
upper
lower
Posterior urethral valves (PUV)
To predict post-natal renal function (often chronic
kidney disease (CKD)/ end stage renal disease (ESRD)
Current clinical practice.
- Fetal ultrasound –non invasive-
- Fetal urinary biochemistry: b2-microglobulin, Na+, ….-invasive-
These lack either sensitivity or specificity.
• Meta analysis, 23 studies: « Current evidence demonstrates that
none of the analytes of fetal urine…nor threshold could be shown
to be of particular clinical value. » Morris et al., Prenat Diagn 2007.
• Meta analysis, 13 studies: Ultrasound: same conclusion. Morris et al.,
BJOG 2009.
What is the problem of PUV?
Values of classical parameters predicting post-natal renal
function (ESRD versus non-ESRD) in our PUV cohort
Either high sensitivity or specificity → never both a high sensitivity and
specificity !
§ Morris RK Prenat Diagn 27, 2007
Clinical predictor Sensitivity [95% CI]
(%)
Specificity [95% CI]
(%)
Fetal urine biochemistry
β2m
cutoff >2 mM§ 100 [83-100] 45 [27-65]
cutoff>13 mM§ 31 [13-55] 95 [80-100]
Na
cutoff>50 mM§ 100 [83-100] 27 [13-47]
cutoff>100 mM§ 13 [2-34] 91 [74-98]
Ultrasound parameters
Oligohydramnios 25 [9-48] 64 [44-80]
Absence of amniotic fluid 25 [9-48] 86 [68-96]
Dysplastic multicystic kidneys 31 [13-55] 100 [87-100]
Hyperechogenic kidneys 25 [9-48] 86 [68-96]
Hypoplastic kidneys with
cortico medullar thickening
19 [5-42] 77 [58-91]
Absence of normal cortico
medullary differentiation
81 [58-95] 59 [40-77]
Francoise
Muller
• Fetal urine
• Fetal urinary peptidome analysis
>4000 peptides
Biomarkers to prediction post-natal
renal function
with normal/mild renal failure
up to 2 years old
Peptidome analysis
26 differentially secreted peptides (FDR<0.05)
« no-ESRD »
Early ESRD, confirmed with
autopsy with TOP or
neonatal death
« ESRD »
12PUV model
Support Vector Machine Model
Classification between the 2 groups: 100%
Discovery of fetal urine biomarkers
of PUV
N=15 N=13
Peptide identification
26 differentially excreted peptides
LC-MS/MS and CE-MS/MS analysis
20 peptides sequenced
(all 12 peptides from 12PUV)
Gs, alpha subunit (GNAS1)
1 Down
Imprinted gene
“Gene sous empreinte”
Collagen fragments
19 Up
Matrix/tissue remodelling as a
consequence of the obstruction?
Contrasts with peptide
markers of CKD
No difference in methylation.
Sequencing of GNAS locus in PUV patients
(H Jueppner, Boston)
38 PUV
Independent cohort
Blind analysis
Peptidome analysis
« ? »
« no-ESRD» « ESRD»
Comparison of prediction with renal function at 2 years
12PUV model
Independent validation of
12PUV model
AUC 0.94 [95% CI: 0.82-0.99]
Sensitivity 88% - Specificity 95%
Se
ns
itiv
ity
100-Sensitivity
Klein et al., Science Translational Medicine 2013
n=22
n=16
***
12
PU
V s
co
re
Both high sensitivity and
specificity !
Prediction of ESRD using the 12PUV model in
fetal urine in the blinded cohort (N=38)
Implementation
What about the “portability” of the analysis?
(i.e. can we do the analysis “anywhere” and still compare the results?)
Glasgow
Toulouse Glasgow (Scotland) Toulouse (France)
P=0.72
Mail Tuesday, October 15, 2013 from Dr Elena Levtchenko (Leuven, Belgium) Fetus with PUV, oligohydramnios, and dense renal parenchyma . “She is considering pregnancy termination and we had a very difficult discussion. Is it possible to send you a sample to your 12PUV score? It might help to make a decision.”
“The results of fetal autopsy have confirmed the diagnosis of urethral valves and severe bilateral renal dysplasia with cortical cysts. Furthermore, the fetus had significantly delayed lung maturation.”
Fetal autopsy
Implementation – PUV case
October 2013
1 week
Fetal urine sampling
(October 17, 2013) Results send to physician
(sample scored ESRD)
Use of peptidomics to
predict kidney (dys)function
• Progression of chronic kidney disease (CKD).
• Prenatal prediction of early renal failure in CAKUT patients.
• In preclinical research.
Urine peptidomics in preclinical research?
1) Readouts in animal models mostly relies on histology (final) and
one-molecule readout (e.g. urinary albumin, cytokine(s), NGAL,
…).
2) Translatability to humans is in most cases uncertain.
Develop a mouse urinary multi-marker (better describing the complex
pathophysiology) and develop a “humanized” readout
Klein et al., work in progress
T2DM models ob/ob
db/db
Mouse urinary
peptidome
Ortholog peptides
CKD273
« Humanized » model = « Humanized » readout Improved
translatability?
Test drugs
Concept
307 differentially excreted peptides
59 peptides identified (w/ seq)
30 orthologs to CKD273
21 peptides in « humanized »
model
Humanized classifier allows detection of
disease and effect of treatment
Independent validation
AER/Glomerular sclerosis
0 20 40 600
1000
2000
3000
4000
Glomerular PAS+ (%)
AE
R (
mg/2
4h)
r=0.3361n.s.
Correlation to glomerular sclerosis
Validation of 21 ortholog peptides in humans
Klein et al., work in progress
• Combinations of urinary peptides (such as the CKD273
classifier) seem promising in prediction of CKD progression.
Conclusions
• Truly informed prenatal counselling. • Stratification of patients that will benefit most from
prenatal intervention.
• The fetal urinary peptide based 12PUV classifier is the
first tool displaying both high sensitivity and specificity to
predict the post-natal renal outcome.
• Use of urinary proteomics for potentially improving the
translatability of animal models of kidney disease.
Future and ongoing opportunities
Use of proteome analysis for stratification of patients
• T2DN: EU Priority (n=3280) (Dr Peter Rossing, Steno Diabetes Center,
Kopenhagen, Denmark). -urinary proteome based stratification
(CKD273) of T2D patients for intervention-
• CAKUT: Bioman (n=300) (Prof Stéphane Decramer, University Hospital
Tolouse, France) and EURenOmics (Prof Franz Schaefer, University of
Heidelberg, Germany). -proof of concept of CAKUT progression
biomarkers in amniotic fluid-
• PUV: PROFET/OMICS-CARE (n=250)(Joost Schanstra, Inserm U1048,
Toulouse, France) – submitted for H2020/E-RARE funding. -European
consortium for proteomics-guided fetal stratification of bilateral
congenital anomalies of the kidney and the urinary tract-
• CKD-rein (N=3600) (Dr Benedicte Stengel, INSERM-Université Paris-Sud,
France) -among other objectives: detection of CKD patients at risk for
progression-
• Renal graft rejection: Biomargin (N>650) (Prof Pierre Marquet, INSERM-
Limoges, France) -non-invasive biomarkers for the follow up of renal
grafts-
• LUPUS: PeptiduLUP(GCLR: Dr Noemie Jourde-Chiche, Prof Eric Daugas;
Dr Philippe Remy, France) -Use of urinary peptidomics in diagnosis and
prognosis of Lupus nephritis-
Future and ongoing opportunities
Justyna Siwy
Petra Zurbig
Adela Torres
Mohammed Dakna
Claudia Pontillo
Harald Mischak
Julie Klein
Cecile Caubet
Benjamin Breuil
Flavio Bandin
Jean-Loup Bascands
Stephane Decramer
Francoise Muller
Angelique Stalmach
William Mullen
Holger Husi
Chrystelle Lacroix
Bernard Montsarrat
PHRC
Elena Levtchenko
Paul Winyard
Franz Schaefer
Gerrit Dou Leiden, 1613-1675
The Physician
Stéphane Decramer Toulouse, 2010