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