use of biomarkers to anticipate ms severity...csf biomarkers in cis patients . teunissen et al.,...

Use of biomarkers to anticipate MS severity

Manuel Comabella

17th STATE OF THE ART Symposium of the SWISS MULTIPLE SCLEROSIS SOCIETY

Outline • Biomarkers: introduction

• Prognostic CSF biomarkers proposed in CIS patients

• Validation of CSF CHI3L1 as prognostic biomarker in CIS

• Validation of additional CSF proteins as potential biomarkers in CIS

• “A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”

Definition*:

*Biomarkers Definitions Working Group. Clin Pharmacol Ther 2001;69: 89-95

Biomarker

Introduction

1. MS diagnosis and disease stratification

2. Prediction of disease course

3. Identification of new therapies beneficial for the disease

4. Personalized therapy based on the prediction of treatment response and identification of patients at high risk for side effects

MS is quite a heterogeneous disease…strong need for biomarkers that capture heterogeneity and may help in:

Introduction

Introduction

1. Molecular biomarkers

2. Imaging biomarkers

Biomarkers in MS:

Molecular biomarkers Category Description

Predictive biomarkers Measured in neurologically asymptomatic individuals to identify those at risk of developing MS (first-degree relatives of MS patients)

Diagnostic biomarkers Can we discriminate patients who have MS from patients with other neurological conditions, autoimmune conditions, or healthy individuals? (patients with symptoms suggestive of MS / CIS / RIS)

Disease activity biomarkers Measured in patients with relapsing-remitting and progressive disease courses and aid in the distinction between MS patients with benign and aggressive disease courses

Treatment response biomarkers

Measured in patients receiving MS therapies in order to identify those individuals who are at risk for treatment failure and/or serious adverse drug reactions

Introduction Molecular biomarkers in MS

Comabella M, Montalban X. Lancet Neurol 2014;13:113

DISCOVERY VALIDATION CLINICAL APPLICATION

strength of evidence

candidatebiomarkers

validatedbiomarkers

clinically usefulbiomarkers

• IgG OB (D)

• IgG index (D)

• anti-AQP4 (D)• anti-JC virus (NZ-R)

• anti-VZV(F-R)

• NAbs (IFNβ-R)• GWAS genes12

(P/D)• NfL (D/DA/NZ-R)

• NfH (DA)

• 25(OH) vit D (P/D/DA/IFNβ-R)

• CD56bright (DC-R/IFNβ-R)

• anti-NZ (NZ-R)

• KFLC (D)• IgM OB (D/DA/IFNß-R/NZ-R)

• KIR4.1 (D)• CXCL13 (D/DA)• Chit (D/DA)• CHI3L1 (D/DA/NZ-R)• OPN (D/DA)• MMP9 (D/DA/IFNß-R)• NO metab. (D/DA)• IL17/TNFα/IL12/IL23 (D/DA)

• fetuin-A (D/DA/NZ-R)• anti-EBNA (P/D/DA)• NCAM (D/DA)• C. factor H (DA)• MBP (D/DA)• GFAP (D/DA)• GPC5 (IFNß-R)• type I IFNs (DA/IFNß-R)• HLA-DRB1*0401/*0408(IFNß-R)

• BAFF (D/DA/IFNß-R)• BDNF (D/DA/IFNß-R/GA-R)

• cytokines1

(D/DA/IFNß-R/GA-R)• adhesion mol.2(D/DA/IFNß-R/NZ-R)

• chemokines/R3

(D/DA/IFNß-R)• MMP/inhibitors4

(D/DA/IFNß-R)• proteomics5

(D/DA/IFNß-R)• microRNA(D/DA/GA-R)

• C3/C4b (D/DA)• sCD146 (DA)• sCD14 (D/DA)• sHLA (D/DA/IFNß-R)6

• sNogo-A (D/DA)• anti-Nogo-A (D/DA)• anti-MBP (D/DA)• anti-MOG (D/DA)• anti-HHV6 (DA)• anti-proteasome (D)• anti-CD46/-59 (DA)• lipocalin 2 (DA)• VEGF-A (DA)• AMCase (D)• APRIL (DA)• CSF cells (D/DA)• MRZ reaction (D/DA)• S/GPL (P/D)

• HMGB1 (D)• TOB1 (D)• S100B / ferritin (D/DA)• isoprostanes(P/D/DA)

• oxysterols (D/DA)• pentosidine (D/DA)• tau / 14-3-3 (D/DA)• NAA / NSE (D/DA)• anti-TUb/β-TUb (D/DA)• anti-NfL (DA)• neurotrophic f.7(D/DA)• Tregs (DA)• K2p5.1 (D/DA)• FGF2 / PDGF-AA (DA)• gMS-classif.1 (D/DA)• myeloid MVs (D/DA)• sAPP/Aβ pept. (D/DA)• apoptosis-rel. mol.8(D/DA/IFNß-R)

• co-signaling mol.9(DA/IFNß-R)

• GWAS genes10(IFNß-R)• candidate genes11

(IFNß-R/GA-R)• MHC2TA (IFNß-R)• APLA (IFNß-R)• IL17F (IFNß-R)• ABCB1/ABCG2 (MT-R)• IL21 (AL-R)

A B C

Comabella M, Montalban X. Lancet Neurol 2014;13:113

Introduction M

olec

ular

bio

mar

kers

in M

S

• In most patients who later develop MS, the disease usually initiates with an acute episode of neurological disturbance known as a clinically isolated syndrome (CIS)

• At this stage, MRI and CSF oligoclonal bands are important tools to predict conversion to MS. However, the role of other body fluid biomarkers is controversial or needs yet to be confirmed

CSF biomarkers in CIS patients

IgG oligoclonal bands


Tintoré M et al. Neurology 2008;70:1079-83

Presence of IgG OB doubles the risk for having a second

attack, independently of MRI findings

IgM oligoclonal bands


Villar LM et al. J Clin Invest. 2005;115:187-94

CIS patients with IgM OB developed a second attack

earlier than patients without IgM OB

Fetuin-A: glycoprotein involved in a wide range of biological functions…

Regulation of calcium metabolism

Cell-cell interactions

Opsonization

Role as a negative acute phase reactant

ELISA

Tumani et al., Neurosci Lett 2009; 452: 214-217

CSF levels are lower in CIS patients who convert to CDMS


CXCL13

Acts via its specific receptor CXCR5

Key regulator of B cell recruitment in MS

Present in active MS lesions

Levels increased in CSF of MS patients

Brettschneider et al., PLoS one 2010; 5: e11986


P<0.001

CSF levels are increased in CIS patients who convert to CDMS

ELISA


Martínez-Yélamos et al., Neurology 2001; 57: 722-724

14-3-3 protein

Abundantly expressed in neurons / glial cells

Roles in neuronal signaling transduction

Associated with neurodegenerative disorders

Its presence reflects neuronal damage

Western Blot

14-3-3 positive

14-3-3 negative

Detection of 14-3-3 protein is associated with shorter time to CDMS

(↑ frequency of EDSS ≥2)


Teunissen et al., Neurology 2009; 72: 1322-1329

ELISA

CSF levels of NF-L are higher in CIS patients who convert to CDMS

Neurofilaments

Structural NE proteins composed of 3 subunits:

• heavy (NF-H)

• medium (NF-M)

• light (NF-L)

Axonal diameter is influenced by the amount of phosphorilation of NF

Pathological processes that cause axonal damage release NF proteins into CSF detection

Levels of NF: good biomarker for axonal damage


• Lack of validation of CSF biomarkers in large cohorts of CIS patients evaluating the prognostic “weight” of proposed biomarkers in multivariable analyses

These are promising prognostic CSF biomarkers in CIS patients…

Study #1 Objective: identify proteins associated with conversion to MS?...

Each pool contains CSF samplesfrom 5 distinct CIS patients

Final volume per pool: 1.5 ml (300 µl per patient)

screeningphase

LC- MS/MSLC- MS/MS

i-TRAQ: isobaric tag for relative and absolute quantitation

Study #1

aDirection of protein expression in CISRR vs. CISCIS. bNumber of pools in which differences were detected

List of differentially expressed proteins identified in the screening phase

Study #1

Individual samples – alternative technique (ELISA)

First validation cohort

Study #1

• C: control• Group 1: NC• Group 2: C

P = 2.3X10-5

• C: control• Group 1: NC• Group 2: C

P = 2.3X10-5

Study #1

CHI3L1: CSF levels are increased in CIS patients who convert to MS

P = 0.018

OND CISCIS CISRR(n=16) (n=26) (n=26)

P = 0.018

OND CISCIS CISRR(n=16) (n=26) (n=26)

Second validation cohort ELISA

Study #1

Study #1 Baseline CHI3L1 levels cut-off (mean + 2SD): 287.9 ng/ml

High CSF levels are associated with shorter

time to CDMS

Study #1 CHI3L1

CSF levels are associated with brain MRI abnormalities at BL and disability progression during follow-up

NGD= number of gadolinium enhancing lesions; NT2L = number of T2 lesions

Study #2

• To validate CHI3L1 as prognostic biomarker in CSF samples from a large cohort of CIS patients

Study #2 Multicenter collaborative study of patients…

• CIS suggestive of CNS demyelination not attributable to other diseases

• Entry window of 3 months* since onset of neurological symptoms

*Clinical examination was performed within the first 3 months and MRI within the first 5 months after the CIS event

Study #2

University of Ulm, Ulm MS center ErasMS, Rotterdam Medical University of Lublin , Poland

Karolinska University Hospital, Stockholm Ospedale Maggiore Policlinico , Milan Charles University , Prague University Hospital Basel, Basel Medical University of Graz, Graz Innsbruck Medical University, Innsbruck

, Barcelona

University of Ulm, Ulm MS center ErasMS, Rotterdam Medical University of Lublin , Poland

Karolinska University Hospital, Stockholm Ospedale Maggiore Policlinico , Milan Charles University , Prague University Hospital Basel, Basel Medical University of Graz, Graz Innsbruck Medical University, Innsbruck Cemcat , Barcelona

BioMS

Hospital Clinic, Barcelona Hospital Gregorio Mara ñó n, Madrid Hospital Puerta del Hierro, Madrid Hospital Universitario Ram ó n y Cajal, Madrid

Hospital Clinic, Barcelona Hospital Gregorio Mara ñó n, Madrid Hospital Puerta del Hierro, Madrid Hospital Universitario Ram ó n y Cajal, Madrid

REEM

Others Universit é de Toulouse - Hopital Purpan, Toulouse Universit é de Toulouse - Hopital Purpan, Toulouse

15 European MS centers…

813 CIS

CSF samples

559 Controls

NINDC: non-inflammatory neurological disease controls INDC: inflammatory neurological disease controls

NINDC INDC 438 121

Study #2

Variables Mean / Frequency Follow-up time 5.4 years

Conversion to MS by Poser criteria

419 patients (51.5%)

Conversion to MS by McDonald criteria*

486 patients (60%)

Most frequent clinical presentation

Optic neuritis (36.4%)

Positive IgG oligoclonal bands

587 patients (75%)

EDSS 3.0 during follow-up 96 patients (16%)

CIS cohort summary…

*2005 McDonald criteria

Study #2 Quantification of CSF CHI3L1 levels

• ELISA: METRA, EIA kit (Quidel Corporation)

undiluted CSF samples assays performed in one single center (Cemcat)

Study #2

Multivariable Cox proportional hazard regression models

CSF CHI3L1 levels

1. Time to MS (Poser) 2. Time to MS (McDonald) 3. Time to EDSS 3.0

Adjusted by: Barkhof criteria at baseline MRI Presence IgG OB Age at CIS onset Treatment

Analysis

Study #2 Results

CSF CHI3L1 levels are ↑ in CIS patients and inflammatory controls

CSF CHI3L1 levels are ↑ in CIS patients who convert to CDMS

Study #2

Variables HR 95% CI P value

Time to MS - Poser CHI3L1 levels 1.69 1.34 – 2.14 1.1 x 10-5

Barkhof criteria 1.71 1.36 – 2.16 6.0 x 10-6

Oligoclonal bands 1.61 1.21 – 2.14 1.1 x 10-8

Age at CIS onset 0.96 0.95 – 0.98 2.4 x 10-7

Treatment 1.51 1.19 – 1.91 2.3 x 10-16

Time to MS - McDonald CHI3L1 levels 1.61 1.31 – 1.96 3.7 x 10-6


Age at CIS onset 0.98 0.97 – 0.99 3.2 x 10-5

Treatment 2.15 1.77 – 2.62 2.5 x 10-14

Time to EDSS 3.0 CHI3L1 levels 3.82 2.36 – 6.19 5.3 x 10-8

Multivariable Cox regression analysis…

CSF CHI3L1 levels are an independent

risk factor for conversion to MS

Only statistically significant variables resulting from the multivariable analysis are shown in the Table. HR: hazard ratio. 95% CI: 95% confidence intervals

Study #2

Variables HR 95% CI P value

Time to MS - Poser CHI3L1 levels 1.69 1.34 – 2.14 1.1 x 10-5

Barkhof criteria 1.71 1.36 – 2.16 6.0 x 10-6


Age at CIS onset 0.96 0.95 – 0.98 2.4 x 10-7

Treatment 1.51 1.19 – 1.91 2.3 x 10-16

Time to MS - McDonald CHI3L1 levels 1.61 1.31 – 1.96 3.7 x 10-6


Age at CIS onset 0.98 0.97 – 0.99 3.2 x 10-5

Treatment 2.15 1.77 – 2.62 2.5 x 10-14

Time to EDSS 3.0 CHI3L1 levels 3.82 2.36 – 6.19 5.3 x 10-8

Multivariable Cox regression analysis…

…and for the development of

disability

Only statistically significant variables resulting from the multivariable analysis are shown in the Table. HR: hazard ratio. 95% CI: 95% confidence intervals

CSF CHI3L1 levels are an independent

risk factor for conversion to MS

Study #2

Time to MS by Poser criteria

Time to MS by McDonald criteria

Variables High CHI3L1 Low CHI3L1

Md time

(95% CI)

28.8 months

(19.8 to 37.9)

77.7 months

(61.9 to 93.5)


Md time

(95% CI)

12.9 months

(11.4 to 14.4)

42.3 months

(30.4 to 54.1)

Best cut-off to classify CHI3L1 levels into LOW / HIGH: 170 ng/ml (44%)

High CSF CHI3L1 levels are associated with shorter time to MS

p=3.2x10-9 p=5.6x10-11

Md: median time. 95% CI: 95% confidence intervals

high CHI3L1

low CHI3L1

high CHI3L1

low CHI3L1

Study #2

High CSF CHI3L1 levels are associated with more rapid development of disability

Time to EDSS 3.0


Md time

(95% CI)

156.0 months

(140.7 to 171.3)

215.0 months

(-)

p=1.8x10-10

Md: median time. 95% CI: 95% confidence intervals

high CHI3L1

low CHI3L1

Best cut-off to classify CHI3L1 levels into LOW / HIGH: 170 ng/ml (44%)

Study #2

0 lesions(N=356)

≥1 lesions(N=187)

p=3.8 x 10-6

0 lesions(N=356)

≥1 lesions(N=187)

p=3.8 x 10-6

0 lesions(N=116)

1-8 lesions(N=238)

≥9 lesions(N=299)

p=0.06

p=5.2 x 10-9

p=6.1 x 10-6

0 lesions(N=116)

1-8 lesions(N=238)

≥9 lesions(N=299)

p=0.06

p=5.2 x 10-9

p=6.1 x 10-6

Number of Gd enhancing lesions at baseline MRI

Number of T2 lesions at baseline MRI

CSF CHI3L1 levels are associated with brain MRI abnormalities at baseline

Study #2

r (p): partial correlation coefficient (p value)

CHI3L1 levels in CSF correlate weakly with inflammatory CSF parameters

Age-adjusted

correlations

CSF cells (cells/μl)

r (p)

CSF proteins (g/L)

r (p)

IgG index

r (p)

CHI3L1 0.11 (0.008) 0.13 (0.002) 0.20 (5.8x10-6)

• Paraffin-embedded brain tissue samples from MS patients (N=15) and non-neurological controls (n=10)*

*Samples were provided by the UK Multiple Sclerosis Tissue Bank

haematoxylin-eosin (HE) Klüver-Barrera (KB)

Staining

high inflammatory activity (n=5) low inflammatory activity (n=10)

MS lesions chronic active

GFAP (Astrocytes) CD68 (microglia/macrophages) CD3 (T lymphocytes)

• Double immunostainings

CHI3L1 expression

Study #2

CD68 CHI3L1

CHI3L1 is expressed by reactive astrocytes and macrophages/microglial cells

CD3 CHI3L1

GFAP CHI3L1

HE: haematoxylin-eosin. KB: Klüver-Barrera

Study #2

CD3 CHI3L1

CHI3L1 is expressed by macrophages/microglial cells

GFAP CHI3L1

CD68 CHI3L1

HE: haematoxylin-eosin. KB: Klüver-Barrera

Study #2

• CHI3L1 expression was determined by flow cytometry in fresh CSF samples from MS patients (N=5) and NINDC (N=5)*

*CSF samples were centrifuged and cellular pellets stained with the corresponding antibodies. NINDC: non-inflammatory neurological disease controls

CD3 (T lymphocytes) CD14 (monocytes)

Intracellular staining

CHI3L1 expression

Study #2

CCCHI3L1 expression is restricted

to CD14+ monocytes

CD

3 C

D14

CHI3L1 CHI3L1

Study #2

p=0.0079p=0.0079

Cp=0.0079

p=0.0079

C

CC

CD

3 C

D14

CHI3L1 CHI3L1

CD14+ monocytes

Study #2

Conclusions

• These findings validate CSF CHI3L1 as a biomarker associated with the conversion to MS and development of disability and reinforce the prognostic role of CHI3L1 in CIS patients

• CSF CHI3L1 levels reflect the degree of astrocyte activation secondary to inflammation

Validation of additional CSF proteins

aDirection of protein expression in CISRR vs. CISCIS. bNumber of pools in which differences were detected

List of differentially expressed proteins identified in the screening phase

Validation of these proteins as potential biomarkers in CIS

ELISA first validation cohort (36% overlapping)

Variables N CIS converters* 29

CIS non-converters* 27

Patients with other neurological disorders

26

*Criteria similar to the original discovery proteomic study

Apolipoprotein AI Apolipoprotein AIV Vitronectin Plasminogen



Selected reaction monitoring first validation cohort (28% overlapping)




20

Semaphorin 7A

Ala-beta-his dipeptidase


ELISA first validation cohort (36% overlapping)


ala-beta-his dipeptidase semaphorin 7A

Nor

mal

ized

prot

ein

inte

nsity

Nor

mal

ized

prot

ein

inte

nsityp=0.033

p=4.4x10-10

* *

ala-beta-his dipeptidase semaphorin 7A

Nor

mal

ized

prot

ein

inte

nsity

Nor

mal

ized

prot

ein

inte

nsityp=0.033

p=4.4x10-10

* *

Selected reaction monitoring first validation cohort (28% overlapping)


Second validation cohort: totally independent




50


Apolipoprotein AI




23

Semaphorin 7A

Ala-beta-his dipeptidase

Selected reaction monitoring

ELISA


Second validation cohort: totally independent

semaphorin 7A ala-beta-his-dipeptidase apolipoprotein AI

p=0.187

CSF

apo

l AI l

evel

s (n

g/m

l)

Nor

mal

ized

pro

tein

inte

nsity

Nor

mal

ized

pro

tein

inte

nsity

p=3.7x10-8 p=1.2x10-4

*


• These results validate previous findings for semaphorin 7A and ala-beta-his-dipeptidase, and point to a role of these proteins as CSF biomarkers associated with the conversion to MS in patients with CIS.

Conclusions

use of biomarkers to anticipate ms severity...csf biomarkers in cis patients . teunissen et al.,...

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