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Actualización en biomarcadores moleculares
en esclerosis múltiple
Manuel Comabella
Outline
• Introduction: biomarkers
• Examples of biomarkers
CSF biomarkers in CIS patients
Treatment response biomarkers: pharmacogenomics
• Future directions
• “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
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
inflammation
demyelination
oxidative stress
glial activation / dysfunction
remyelination / repair
neuroaxonal
damage
MS pathophysiological processes
inflammation
demyelination
oxidative stress
glial activation / dysfunction
remyelination / repair
neuroaxonal
damage
MS pathophysiological processes
inflam
mation
neuro
- degenera
tion
Introduction Molecular biomarkers in MS
Comabella M, Montalban X. Lancet Neurol 2014;13:113
DISCOVERY VALIDATIONCLINICAL
APPLICATION
strength of evidence
candidate
biomarkers
validated
biomarkers
clinically useful
biomarkers
• 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
ole
cula
r bio
mark
ers
in M
S
Outline
• Introduction: biomarkers
• Examples of biomarkers
CSF biomarkers in CIS patients
Treatment response biomarkers: pharmacogenomics
• Future directions
• IgG oligoclonal bands
• IgM oligoclonal bands
• Neurofilaments
• Chitinase 3-like 1
CSF biomarkers in CIS patients
• 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
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
IgG oligoclonal bands
CSF biomarkers in CIS patients
Tintoré M et al. Brain 2015; 138: 1863-74
EDSS 3.0
IgG oligoclonal bands
CSF biomarkers in CIS patients
The presence of IgG OB was associated with a
higher risk of the accumulation of disability
(time to EDSS 3.0) independent of other
variables
EDSS 3.0
Tintoré M et al. Brain 2015; 138: 1863-74
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
IgM oligoclonal bands
CSF biomarkers in CIS patients
Magraner et al. Neuroradiology 2012;54:5-12
Brain atrophy was higher in CIS patients with IgM OB
Villar et al. J Clin Invest.
2005;115:187-94
Presence of IgM OB was associated with more aggressive disease course
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
NF-L
CSF biomarkers in CIS patients
Kuhle et al., Neurology 2015;84:1639-1643
Natalizumab Fingolimod
…biomarker to monitor response to therapies and neuroprotective effects of treatments?
Gunnarsson et al., Ann Neurol 2011;69:83-89
CSF NF-L levels are modified by MS therapies
Neurofilaments
• CHI3L1, also known as YKL40, is a member of the glycoside hydrolase 18
chitinase family that binds chitin but lacks chitinase activity
• It is mainly secreted by activated macrophages and its expression is
induced by proinflammatory cytokines
• Levels are increased in disorders characterized by chronic inflammation
• Functions: chemotactic factor? / tissue remodelling factor?
CSF biomarkers in CIS patients Chitinase 3-like 1 (CHI3L1)
CSF biomarkers in CIS patients
LC- MS/MSLC- MS/MS
i-TRAQ: isobaric tag for relative and absolute quantitation
CHI3L1
…to identify CSF biomarkers associated with the conversion to MS
CSF biomarkers in CIS patients
ELISA
CHI3L1
CSF biomarkers in CIS patients Validation of CSF CHI3L1 as a prognostic biomarker of conversion to MS*
813813
CIS
CSF samples
Universityof Ulm, Ulm
MS center ErasMS, Rotterdam
Medical Universityof Lublin, Poland
Karolinska UniversityHospital, Stockholm
Ospedale Maggiore Policlinico, Milan
Charles University, Prague
University Hospital Basel, Basel
Medical Universityof Graz, Graz
Innsbruck Medical University, Innsbruck
, Barcelona
Universityof Ulm, Ulm
MS center ErasMS, Rotterdam
Medical Universityof Lublin, Poland
Karolinska UniversityHospital, Stockholm
Ospedale Maggiore Policlinico, Milan
Charles University, Prague
University Hospital Basel, Basel
Medical Universityof Graz, Graz
Innsbruck Medical University, Innsbruck
Cemcat , Barcelona
Universityof Ulm, Ulm
MS center ErasMS, Rotterdam
Medical Universityof Lublin, Poland
Karolinska UniversityHospital, Stockholm
Ospedale Maggiore Policlinico, Milan
Charles University, Prague
University Hospital Basel, Basel
Medical Universityof Graz, Graz
Innsbruck Medical University, Innsbruck
, Barcelona
Universityof Ulm, Ulm
MS center ErasMS, Rotterdam
Medical Universityof Lublin, Poland
Karolinska UniversityHospital, Stockholm
Ospedale Maggiore Policlinico, Milan
Charles University, Prague
University Hospital Basel, Basel
Medical Universityof Graz, Graz
Innsbruck Medical University, Innsbruck
Cemcat , Barcelona
BioMS
Hospital Clinic, Barcelona
Hospital Gregorio Marañón, Madrid
Hospital Puerta del Hierro, Madrid
Hospital Universitario Ramón y Cajal, Madrid
Hospital Clinic, Barcelona
Hospital Gregorio Marañón, Madrid
Hospital Puerta del Hierro, Madrid
Hospital Universitario Ramón y Cajal, Madrid
Hospital Clinic, Barcelona
Hospital Gregorio Marañón, Madrid
Hospital Puerta del Hierro, Madrid
Hospital Universitario Ramón y Cajal, Madrid
Hospital Clinic, Barcelona
Hospital Gregorio Marañón, Madrid
Hospital Puerta del Hierro, Madrid
Hospital Universitario Ramón y Cajal, Madrid
REEM
Others Université de Toulouse - Hopital Purpan, ToulouseUniversité de Toulouse - Hopital Purpan, ToulouseUniversité de Toulouse - Hopital Purpan, ToulouseUniversité de Toulouse - Hopital Purpan, Toulouse
15 European MS centers…
Quantification ofCHI3L1 levels: ELISA
*inclusion of all CIS patients
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
Universit é de Toulouse - Universit é de Toulouse - H. Purpan, Toulouse
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
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
Universit é de Toulouse - Universit é de Toulouse - H. Purpan, Toulouse
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
CSF biomarkers in CIS patients
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
CSF biomarkers in CIS patients
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
CSF biomarkers in CIS patients
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
CSF biomarkers in CIS patients
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%)
CSF biomarkers in CIS patients
Cut-off: 189 ng/ml
CSF biomarkers in CIS patients CHI3L1
High CSF CHI3L1 levels were associated with shorter time to MS (McDonald criteria)
Proteomic approach Verification by ELISA
Hinsinger et al., Mult Scler 2015; 21:1251-1261
CSF biomarkers in CIS patients CHI3L1
CIS patients (optic neuritis)
CHI3L1 in combination with MRI and age were the best predictors of MS risk
CHI3L1 predicted long-term (>10 years) cognitive
impairment
Modvig et al., Mult Scler 2015; 21: 1761-1770
CHI3L1
Martínez et al., Mult Scler 2015;21:550-561
CSF biomarkers in CIS patients
CIS + RRMS
High CHI3L1 levels were associated with earlier progression to EDSS 3 and EDSS 6
Next steps? biomarker combination
CSF biomarkers in CIS patients
CNDP1: Ala-beta-his dipeptidase
SEMA7A: semaphorin 7A
CISCIS CISCDMS
CHI3L1 CNDP1 SEMA7A
CHI3L1
+
CNDP1
+
SEMA7A
Cantó E et al., J Neuroinflam 2014;11:181 Borràs E et al., Mol Cell Proteomics 2016;15:318
Next steps? how is CHI3L1 exerting its action?
CSF biomarkers in CIS patients
CHI3L1 (ng/ml)
Outline
• Introduction: biomarkers
• Examples of biomarkers
CSF biomarkers in CIS patients
Treatment response biomarkers: pharmacogenomics
• Future directions
How to identify markers associated with response to treatment in MS?
Pharmacogenomics
How to identify markers associated with response to treatment in MS?
Pharmacogenomics: “Application of genome technologies such as gene expression
profiling, single nucleotide polymorphisms (SNP) screens,
high-throughput DNA/RNA sequencing to predict patient
response and toxicity to drugs”
Pharmacogenomics
How to identify markers associated with response to treatment in MS?
Pharmacogenomics: “Application of genome technologies such as gene expression
profiling, single nucleotide polymorphisms (SNP) screens,
high-throughput DNA/RNA sequencing to predict patient
response and toxicity to drugs”
“To facilitate individualization
of patient treatment...”
Ultimate goal...
Pharmacogenomics
Interferon-beta
Glatiramer acetate
Mitoxantrone
Natalizumab
DMT
Fingolimod
Laquinimod
Teriflunomide
BG12
Oral therapies
Alemtuzumab
Daclizumab
Rituximab
Ocrelizumab
Ofatumumab
Monoclonal antibodies
Interferon-beta
Glatiramer acetate
Mitoxantrone
Natalizumab
DMT
Fingolimod
Laquinimod
Teriflunomide
BG12
Oral therapies
Alemtuzumab
Daclizumab
Rituximab
Ocrelizumab
Ofatumumab
Monoclonal antibodies
Potential risk for treatment failure
Adverse reactions
Individualized
therapy
Tx. A Tx. B Tx. C
“Administration of treatment
to those patients who are
most likely to respond to it”
Pharmacogenomics in MS: The future A Clinical and radiological
characteristics
Patients
Variables
1
2
N
A B C D
A1........An B1........Bn C1........Cn D1........Dn
% R to treament 1
…………………………….
% R to treament N
% R to treament 1
…………………………….
% R to treament N
% R to treament 1
…………………………….
% R to treament NB Transcriptomics
Genes
ResponderNon-
responder
D Genetic polymorphisms
SNP a SNP d
SNP f
C Others
ProteomicsMetabolomics
..............
Predictionof response
• Interferon beta
• Fingolimod
• Natalizumab
Treatment response biomarkers Recent studies…
Genetic studies Whole genome SNP screens
Interferon-beta
4
Discovery cohort: rs9828519 (SLC9A9) exceeded the threshold of genome-wide significance (p-value <5x10-8). Association replicated in three independent validation cohorts
G allele associated with increased risk of NR
Gene expression is down-regulated in MS patient
who are more likely to have relapses role also
in MS disease activity
Esposito F et al. A pharmacogenetic study implicates SLC9A9 in multiple sclerosis disease activity. Ann Neurol 2015; 78:115.
SLC9A9: solute carrier family 9, subfamily A (NHE9, cation proton antiporter 9), member 9 (codes for a sodium/hydrogen exchanger found in lisosomes)
AA AG GG
Inflammasomes are multi-oligomeric subunits that
regulate maturation of pro-inflammatory cytokines such as
IL-1β and IL-18
At least four major inflammasomes have been
identified: absent in melanoma 2 (AIM2), Nod-like
receptor (NLR) family - CARD domain containing 4
(NLRC4), and NLR family pyrin domain containing 1 and
3 (NLRP1 and NLRP3)
Transcriptomics Inflammasome
Malhotra et al. Brain 2015; 138:644
Interferon-beta
There is increasing evidence of a role of inflammasomes
in EAE and MS
Gris et al. J Immunol 2010; 185:974
Guarda et al. Immunity 2011;34:213
Inoue et al. PNAS 2012;109:10480
Jha et al. J Neurosci 2010;30:15811
We aimed to investigate the role of inflammasomes (NLRP3,
NLRP1, NLRC4, and AIM2) and related cytokines (IL-1β, IL-
10, IL-18) in the response to IFN-β in MS patients
Inflammasome
Malhotra et al. Brain 2015; 138:644
Interferon-beta
Response to IFNb (after 2 years of treatment)
Responders Non-
responders Intermediate responders
Relapses None 1 None / 1
and and and
EDSS * No Yes Yes / No
Clinical criteria of response
* 1 point sustained for at least 6 months
Malhotra et al. Brain 2015; 138:644
Interferon-beta
Clinical criteria of response (N= 97)
Demographic and baseline clinical and radiological characteristics
Malhotra et al. Brain 2015; 138:644
Interferon-beta
PBMC
N2
Real time PCR
RNA
ABI PRISM® 7900HT system (Applied Biosystems)
Endogenous control: GAPDH
AIM2
NLRC4
NLRP1
NLRP3
Inflammasomes
IL1B
IL18
IL10
Cytokines
Malhotra et al. Brain 2015; 138:644
Interferon-beta
NLRC4
R NR IR HC
AIM2
NLRP3 NLRP1
R NR IR HC
R NR IR HC R NR IR HC
INFLA
MM
AS
OM
ES
-
ba
se
lin
e le
ve
ls
Interferon-beta
NLRC4
R NR IR HC
AIM2
P=0.963
NLRP3 NLRP1
R NR IR HC
P=0.164
R NR IR HC R NR IR HC
P=0.194
P=0.034
INFLA
MM
AS
OM
ES
-
ba
se
lin
e le
ve
ls
Interferon-beta
R NR IR HC R NR IR HC
R NR IR HC
CY
TO
KIN
ES
-
ba
se
lin
e le
ve
ls
IL-1B
IL-10 IL-18
Interferon-beta
R NR IR HC R NR IR HC
R NR IR HC
CY
TO
KIN
ES
-
ba
se
lin
e le
ve
ls
IL-10 IL-18
P=0.006 P=0.007
IL-1B
Interferon-beta
R NR IR HC
0.0
0.5
1.0
1.5
2.0
Rela
tive N
LR
P3 e
xp
ressio
n
R NR IR HC
0.0
2.0
4.0
6.0
Re
lati
ve
IL
-1b
ex
pre
ss
ion
R NR HC
0.0
0.5
1.0
1.5
2.0
Rela
tive N
LR
P3 e
xp
ressio
n
R NR HC
0.0
1.0
2.0
3.0
4.0
Rela
tive IL
-1b
exp
ressio
n
P=0.022
Clinical classification of IFN-β response at 24 months
Clinical - radiological classification of IFN-β response at 12 months
P=0.0076
P=0.001 P=0.024
P=0.022
Clinical – radiological classification of IFNβ response at 1 year (Río score)
Relapse
MRI Progression
IL-1B NLRP3
R+/P+/MRI+
R+/P-/MRI+
R-/P+/MRI+
R+/P+/MRI-
R-/P+/MRI-
R+/P-/MRI-
R-/P-/MRI+
R-/P-/MRI-
Interferon-beta
R NR IR HC
0.0
0.5
1.0
1.5
2.0
Rela
tive N
LR
P3 e
xp
ressio
n
R NR IR HC
0.0
2.0
4.0
6.0
Re
lati
ve
IL
-1b
ex
pre
ss
ion
R NR HC
0.0
0.5
1.0
1.5
2.0
Rela
tive N
LR
P3 e
xp
ressio
n
R NR HC
0.0
1.0
2.0
3.0
4.0
Rela
tive IL
-1b
exp
ressio
n
P=0.022
Clinical classification of IFN-β response at 24 months
Clinical - radiological classification of IFN-β response at 12 months
P=0.0076
P=0.001 P=0.024
P=0.022
Clinical – radiological classification of IFNβ response at 1 year (Río score)
Relapse
MRI Progression
IL-1B NLRP3
R+/P+/MRI+
R+/P-/MRI+
R-/P+/MRI+
R+/P+/MRI-
R-/P+/MRI-
R+/P-/MRI-
R-/P-/MRI+
R-/P-/MRI-
Interferon-beta
Functional polymorphisms of the NLRP3: rs35829419
trend for association between the NLRP3 rs35829419 and the response to IFN-β
789 MS patients
(clinical criteria)
Malhotra et al. Brain 2015; 138:644
Interferon-beta
Inflammasome
Genotyping additional functional
SNPs (NLRP3) - Open array -
960 DNA samples Clinical – radiological criteria (Rio score)
VariantType chr position rs ref alt Minor FrqMinor
regulatory 1 247597180 rs76911796 T C C 0.0176471
regulatory 1 247614617 rs4925663 C T T 0.423529
exonic,regulatory 1 247581542 rs72771992 T G G 0.0941176
regulatory 1 247597150 rs200373688 G GA GA 0.0176471
regulatory 1 247614407 rs12065526 G A A 0.123529
exonic,regulatory 1 247608010 rs201229629 C T T 0.00588235
regulatory 1 247614896 rs11583410 A C C 0.5
regulatory 1 247597050 rs189440683 A G G 0.105882
regulatory 1 247596993 rs138958966 C T T 0.147059
exonic,regulatory 1 247587343 rs121908147 G A A 0.00588235
regulatory 1 247614553 rs12086048 C T T 0.123529
regulatory 1 247597139 rs28681541 A G G 0.170588
regulatory 1 247597054 rs181487854 C T T 0.117647
exonic,regulatory 1 247587531 rs4925543 A G A 0.0352941
regulatory 1 247612435 rs4925547 A T T 0.447059
exonic,regulatory 1 247587408 rs7525979 C T T 0.0470588
exonic,regulatory 1 247607973 rs139814109 C T T 0.00588235
regulatory 1 247596954 rs61841185 G A A 0.0764706
regulatory 1 247614386 rs12070953 T C C 0.123529
exonic,regulatory 1 247587982 rs148478875 C T T 0.00588235
exonic,regulatory 1 247587783 rs180177471 G A A 0.00588235
exonic,regulatory 1 247588053 rs34298354 C T T 0.105882
regulatory 1 247596951 rs12068914 C T T 0.00588235
regulatory 1 247597035 rs148040387 C T T 0.264706
regulatory 1 247597149 rs79881253 T G G 0.0470588
regulatory 1 247597181 rs148560736 G A A 0.0176471
exonic,regulatory 1 247588858 rs35829419 C A A 0.0411765
Interferon-beta (on-going study)
Polman et al., 2010; Lancet Neurol 9:740
Neutralizing antibodies (NABs) to interferon-beta
High-titre NAb positivity negative impact
biomarkers of IFNb clinical efficacy
Interferon-beta
Neutralizing antibodies (NABs) to interferon-beta
Interferon-beta
- Genetic predisposition -
HLA-DRB1*0401 HLA-DRB1*0408
rs9272105 (intergenic between HLA-DRB1 and -DQA1)
HLA class I and class II genes
1
3
HLA-DRB1*16:01 2
HLA-DRB1*15 haplotype 4
Combined presence of DRB1*07/DQA1*02 with A*26 or B*14 5
1. Hoffmann et al. Am J Hum Genet. 2008;83:219 /
3. Weber et al. Pharmacogenomics J. 2012;12:238 / 4. Link et al. PLoS ONE. 2014; 9:e90479 5. Nuñez et al. J Med Genet. 2014 ;51:395
2. Buck et al. Arch Neurol. 2011;68:480
Fingolimod
Kuhle et al., Neurology 2015;84:1639-1643
CSF NF-L levels are modified by fingolimod
post hoc analysis (FREEDOMS)
treatment effect on CSF NF-L levels was associated with improved clinical and
MRI outcomes
potential use of NF-L as a biomarker to monitor response to fingolimod
Natalizumab Progressive multifocal leucoencephalopathy...
Bloomgren et al. N Eng J Med. 2012;366:1870
1
2
3
Bloomgren et al. N Eng J Med. 2012;366:1870
1
2
3
Natalizumab Progressive multifocal leucoencephalopathy...
Bloomgren et al. N Eng J Med. 2012;366:1870
1
2
3
Bloomgren et al. N Eng J Med. 2012;366:1870
1
2
3
Clinically useful biomarker!!
Natalizumab Progressive multifocal leucoencephalopathy...
% CD4+ T cells expressing CD62L is
in pre-PML samples
Schwab et al., 2013; Neurology 81:865
CD62L: biomarker for individual
PML risk in patients treated with
natalizumab
Natalizumab Progressive multifocal leucoencephalopathy...
Schwab et al., 2015; MS Journal Oct 2
Findings validated in independent cohorts of patients
... in combination with the anti-JCV antibody index may improve the
identification of NTZ-treated patients at high risk for PML
Natalizumab
...utility of CD62L as PML risk biomarker?
Natalizumab Progressive multifocal leucoencephalopathy...
Villar et al., 2015; Ann Neurol 77; 447
367 NTZ-treated MS patients / 24 developed PML
Patients with lipid-specific IgM OB in CSF have lower risk of PML when treated with NTZ
…the high inflammatory status of
patients positive for IgM OB is PML-
protective?
Natalizumab Anaphylactic/anaphylactoid reactions...
HLA-DRB1*13 / HLA-DRB1*14 alleles
Risk
pM-H=3x10-7 / ORM-H (95% CI)= 8.9 (3.4-23.6) PPV: 82%
De la Hera et al. Neurol Neuroimmunol Neuroinflamm 2014;1(4):e47
Natalizumab Clinical response to treatment...
Signoriello et al., 2015; MS Journal Oct 9
mean % of lymphocytes higher in responders
than in partial responders to NTZ
NTZ-induced lymphocytosis (NIL) as biomarker of response
time to relapse is shorter in patients with low NIL
Natalizumab Clinical response to treatment...
Mattoscio et al., 2015; Neurology 84:1473
CD34+ cells increase in NTZ-treated patients and
HSPC mobilization response vary among
treated patients
HSPC mobilization as biomarker of response
HSPC nonmobilizer patients show persistent MRI activity (6 months)
HSPC: hematopoietic stem and progenitor cells
Outline
• Introduction: biomarkers
• Examples of biomarkers
CSF biomarkers in CIS patients
Treatment response biomarkers: pharmacogenomics
• Future directions
Future directions
Discovery Validation Clinicalpractice
Assaydevelopment
Regulatoryapproval
Exploratorybiomarker
Possiblebiomarker
Knownbiomarker
Regulatorybiomarker
Biomarker studies in MS
Discovery Validation Clinicalpractice
Assaydevelopment
Regulatoryapproval
Exploratorybiomarker
Possiblebiomarker
Knownbiomarker
Regulatorybiomarker
Biomarker studies in MS
Limited overlap between studies...
Inconsistent results...
Future directions
How to increase reproducibility of biomarker studies?
Adherence to a consensuson sampling, storage and
biobanking of samples
variations in samples
easier exchange of
samples to sample size
Teunissen et al., Neurology 2009;73:1914-1922
Bio-MS
Future directions
How to increase reproducibility of biomarker studies?
healthy controls
spinal anesthesia subjects
inflammatory neurological
disease controls
non-inflammatory
neurological disease controls
symptomatic controls
Better definition of control groups
Future directions
How to increase reproducibility of biomarker studies?
healthy controls
spinal anesthesia subjects
inflammatory neurological
disease controls
non-inflammatory
neurological disease controls
symptomatic controls
Better definition of control groups
Future directions
Future directions
Topics Comments
1. Biomarker validation More studies are needed to validate current exploratory biomarkers
2. Inclusion of pharmacogenomic studies in clinical trials
Clinical trials should incorporate large scale pharmacogenomic studies as part of their design
3. Increase in sample size of pharmacogenomic studies
Current pharmacogenomic studies have a lack of statistical power to detect reliable associations with the response
4. Definition of response criteria to therapies
Big efforts are needed to define the criteria of response and treatment failure to each particular MS therapy
5. Inclusion of a placebo group in pharmacogenomic studies
Current design of pharmacogenomic studies does not allow to discriminate between true response (or lack of response) to a particular therapy and natural evolution of the disease
...towards individualized therapy in MS...
MRI