use of biomarkers to anticipate ms severity...csf biomarkers in cis patients . teunissen et al.,...
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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
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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
• 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
CSF biomarkers in CIS patients
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
CSF biomarkers in CIS patients
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
CSF biomarkers in CIS patients
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
CSF biomarkers in CIS patients
P<0.001
CSF levels are increased in CIS patients who convert to CDMS
ELISA
CSF biomarkers in CIS patients
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)
CSF biomarkers in CIS patients
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
CSF biomarkers in CIS patients
• 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…
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
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
Oligoclonal bands 1.68 1.30 – 2.18 7.7 x 10-5
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
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
Oligoclonal bands 1.68 1.30 – 2.18 7.7 x 10-5
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)
Variables High CHI3L1 Low CHI3L1
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
Variables High CHI3L1 Low CHI3L1
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
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
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
Validation of additional CSF proteins
*Criteria similar to the original discovery proteomic study
Selected reaction monitoring first validation cohort (28% overlapping)
Variables N CIS converters* 18
CIS non-converters* 18
Patients with other neurological disorders
20
Semaphorin 7A
Ala-beta-his dipeptidase
Validation of additional CSF proteins
ELISA first validation cohort (36% overlapping)
Validation of additional CSF proteins
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)
Validation of additional CSF proteins
Second validation cohort: totally independent
Variables N CIS converters* 47
CIS non-converters* 27
Patients with other neurological disorders
50
*Criteria similar to the original discovery proteomic study
Apolipoprotein AI
Variables N CIS converters* 25
CIS non-converters* 25
Patients with other neurological disorders
23
Semaphorin 7A
Ala-beta-his dipeptidase
Selected reaction monitoring
ELISA
Validation of additional CSF proteins
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
*
Validation of additional CSF proteins
• 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