Actualización en biomarcadores moleculares en esclerosis ... · biomarcadores moleculares en...

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


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


Introduction M

ole

cula

r bio

mark

ers

in M

S

Outline






• IgG oligoclonal bands

• IgM oligoclonal bands

• Neurofilaments

• Chitinase 3-like 1


• 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


IgG oligoclonal bands


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



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



Tintoré M et al. Brain 2015; 138: 1863-74

EDSS 3.0



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


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


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


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


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)


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


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










Cemcat , Barcelona










, Barcelona










Cemcat , Barcelona

BioMS

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


Medical University of Lublin , Poland

Karolinska University Hospital, Stockholm

Ospedale Maggiore Policlinico , Milan

Charles University , Prague


Medical University of Graz, Graz


, Barcelona










Cemcat , Barcelona

Universit é de Toulouse - Universit é de Toulouse - H. Purpan, Toulouse

BioMS


Hospital Gregorio Mara ñó n, Madrid


Hospital Universitario Ram ó n y Cajal, Madrid


Hospital Gregorio Mara ñó n, Madrid


Hospital Universitario Ram ó n y Cajal, Madrid

REEM










, Barcelona










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


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


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


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


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%)


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


CIS + RRMS

High CHI3L1 levels were associated with earlier progression to EDSS 3 and EDSS 6

Next steps? biomarker combination


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?


CHI3L1 (ng/ml)

Outline






How to identify markers associated with response to treatment in MS?

Pharmacogenomics


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


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


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


Interferon-beta

Clinical criteria of response (N= 97)

Demographic and baseline clinical and radiological characteristics


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


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


P=0.194

P=0.034

INFLA

MM

AS

OM

ES

-

ba

se

lin

e le

ve

ls

Interferon-beta


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

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

Functional polymorphisms of the NLRP3: rs35829419

trend for association between the NLRP3 rs35829419 and the response to IFN-β

789 MS patients

(clinical criteria)


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


exonic,regulatory 1 247587343 rs121908147 G A A 0.00588235


regulatory 1 247597139 rs28681541 A G G 0.170588


exonic,regulatory 1 247587531 rs4925543 A G A 0.0352941

regulatory 1 247612435 rs4925547 A T T 0.447059




regulatory 1 247614386 rs12070953 T C C 0.123529


exonic,regulatory 1 247587783 rs180177471 G A A 0.00588235




regulatory 1 247597149 rs79881253 T G G 0.0470588


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


1

2

3



1

2

3


1

2

3

Clinically useful biomarker!!


% 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


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?


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






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

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...

Actualización en biomarcadores moleculares en esclerosis ... · biomarcadores moleculares en...

Documents

Transcript of Actualización en biomarcadores moleculares en esclerosis ... · biomarcadores moleculares en...