BIOMARKERS FOR THE MANAGEMENT
OF COMPLEX DISEASES
Complex diseases are characterized by a
multifactorial pathogenesis that leads to
wide clinical variability (1). For this reason,
the course of the disease and the response
to therapy can vary largely between
patients, even if groups can be established
of similar clinical phenotypes. The unpre-
dictability of complex diseases represents a
significant challenge for healthcare systems
and is an important priority from a patient’s
perspective. For this reason biomarkers offer
a promising approach to identify disease
stages or subgroups based on the ongoing
pathogenic events, in order to improve our
predictive abilities. In addition, the develop-
ment of effective biomarkers is one of the
more basic steps in the path of developing
personalized medicine (2).
The definition of a biomarker has been clar-
ified recently for regulatory purposes. Thus,
biomarker is a characteristic that is objec-
tively measured and evaluated as an indica-
tor of normal biological processes, patho-
genic processes or pharmacological respons-
es to a therapeutic intervention (3). A surro-
gate endpoint is a biomarker that is intend-
ed to substitute for a clinical endpoint and is
expected to predict the effect of a therapeu-
tic intervention. A clinical endpoint is a char-
acteristic or variable that measures how a
patient feels, functions or survives (3).
Prentice’s criteria define two main proper-
ties of a biomarker: 1) the biomarker strong-
ly and significantly correlates with the clini-
cal endpoint; 2) the biomarker must fully
capture the net effect of the treatment on
the true clinical endpoint (4). Although the
first principle is included in the definition of
biomarkers, the second is often not consid-
ered and in general, it is very difficult to ful-
fill. Because the majority of chronic diseases
have a complex pathogenesis, an individual
biomarker is likely to reflect only one of the
many ongoing pathogenic processes. This
specificity of a biomarker for an individual
process is one reason why it does not serve
as a surrogate for the clinical outcome of a
complex disease. However, it also implies
that the clinical outcome, which reflects all
the pathophysiological processes that con-
tribute to the disease, is too insensitive to
capture the full effect of any therapeutic
application specific to an individual process.
False-positive and -negative biomarkers are
an important limitation to the discovery of
true biomarkers (5). False-positive biomark-
ers may arise if the changes in the biomark-
er reflect the effect of treatment, although
these effects are either irrelevant to the
pathophysiology of the disease or they are
clinically unimportant. False-negative bio-
markers reflect clinically relevant changes in
the pathophysiology but they do not capture
the mechanistic effects of the treatment
applied (e.g., T2 lesion load in magnetic res-
onance imaging [MRI] for neuroprotection
trials). In addition, they may arise if the bio-
marker is more sensitive than the clinical
marker or if the clinical marker is irrelevant
to a subset of the patients, to a novel mode
of action of the drug or to its new indication
FOCUS ON BIOMARKERS
BIOMARKERS FOR MULTIPLE
such as HLA or myelin
antibodies have not
but many other
biomarkers are in the
pipeline to fill this niche.
Copyright © 2010 Prous Science, S.A.U. or its licensors. All rights reserved. CCC: 0214-0934/2010. DOI: 10.1358/dnp.2010.23.9.1472300
Drug News & Perspectives 2010, 23(9): 585-595
The pursuit of personalized medicine
requires the development of biomarkers
to predict disease course, monitor dis-
ease evolution, stratify patient sub-
groups by disease activity and to predict
and monitor response to therapies.
Multiple sclerosis (MS) is a common neu-
rological disease in young adults with an
unpredictable course that may be associ-
ated with significant disability, diminish-
ing the patient's quality of life. Currently,
disease prognosis is based on clinical
information (relapse rate and disability
scales) and diagnostic tests (brain MRI or
the presence of oligoclonal bands in the
cerebrospinal fluid). However, the ability
of neurologists to make an accurate
prognosis is very limited based on such
information, a situation perceived by
patients as one of their biggest concerns.
Although many recent studies have iden-
tified different molecules and imaging
techniques associated with the course of
MS, in most cases the diagnostic accura-
cy of such technologies has not been
properly assessed. This shortcoming is
partly due to the failure to validate such
biomarkers, which impedes their appli-
cation in clinical practice. However, the
recent validation of anti-aquaporin-4
antibodies for Devic's disease and the
development of optic coherent tomogra-
phy for MS, are examples of the benefits
that the development of MS biomarkers
can offer. Indeed, it may currently be nec-
essary to redress the bias in research
towards clinical validation rather than
discovery in order to promote transla-
tional research and improve patient's
quality of life.
Correspondence: P. Villoslada, firstname.lastname@example.org
by Pablo Villoslada
Biomarkers can be divided into different cat-
egories: 1) prognostic markers, 2) predictive
markers and 3) pharmacodynamic biomark-
ers (Fig. 1A) (6). Prognostic biomarkers seek
to predict the natural course of the disease,
distinguishing between good and bad out-
comes. Predictive biomarkers or response
biomarkers aim to establish the probability
a given patient will respond to a specific
therapy. Finally, pharmacodynamic bio-
markers measure the near-term treatment
effects of a drug on the disease, in order to
guide the doses required. Thus, at the treat-
ment level, the prognostic biomarker
addresses the decision to “treat or not
treat”, the predictive biomarker “which
drug”, and the pharmacodynamic biomarker
the “dose” (6). In addition to this diagnosis-
therapeutic approach, an alternative
approach is to search for pathway-based
biomarkers in order to stratify patients into
subgroups based on the main pathogenic
processes responsible for the disease that
will drive their response to therapy. This
pathway-based approach is the main focus
of the systems biology drug and biomarker
discovery approach (7).
The Food & Drug Administration (FDA) cat-
egorizes biomarkers as 1) exploratory bio-
markers, 2) possible valid biomarkers, 3)
known valid biomarkers and 4) regulatory
biomarkers (Box 1) (8). The regulatory
process at the FDA is guided by the Clinical
Laboratory Improvement Amendments
(CLIA) and by the CE mark of the European
Medicines Agency (EMA). Discovering bio-
markers is a multistep process involving bio-
marker identification, assay development,
biomarker validation, regulatory approval
and translation to clinical practice (Fig. 1B).
In order to validate biomarkers, it is neces-
sary to perform well-controlled observation-
al studies using the Standards for Reporting
of Diagnostic Accuracy (STARD) criteria
ment.htm) (9). The STARD criteria are
equivalent to the CONSORT (Consolidated
Standards of Reporting Trials) criteria for
reporting clinical trials but they are aimed at
reporting the diagnostic accuracy of a test.
Calculating the diagnostic accuracy requires
establishing the number of patients that
must reach the clinical endpoint and calcu-
lating the sensitivity, specificity, positive and
negative predictive value, accuracy and the
area under the receiver operating character-
istic (ROC) curve (AUC). Although there are
many articles describing new exploratory
biomarkers every year, in most cases these
discoveries are not followed-up by system-
atic studies to validate the marker.
Moreover, new studies often fail to repro-
duce the original observation due to
methodological differences, poor study
design, the use of nonstandardized assays
or misleading statistical analysis based on
small sample sizes. This problem has been
particularly evident in the field of cancer,
where many biomarkers have been pro-
posed but few have been validated, a situa-
tion which has led to the release of a set of
standards (REMARK, REporting recommen-
dations for tumor MARKer prognostic stud-
ies) to report biomarker discovery (10).
THE NEED FOR BIOMARKERS IN
Biomarkers in multiple sclerosis (MS) can
play several different roles. They can be
used to improve our diagnostic capacity
when screening individuals at risk of suffer-
ing the disease, allowing early diagnosis
and preventive therapy. Although the sensi-
tivity and specificity of diagnosing MS based
on current criteria is high (11, 12), identifica-
tion of new biomarkers of the disease may
improve our capacity to diagnose more
complex cases or those where confounding
diseases imply the need for a differential
diagnosis. Even if the number of individuals
requiring such differential diagnosis is small
compared with the overall population of
patients, augmenting the accurate diagno-
sis of the disease will improve how the dis-
ease is managed as a whole.
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
Figure 1. Role of biomarkers in patient management and biomarker development. A) Biomarkers are useful tools to address specific questions regarding
patient management and therapy. B) Development of a biomarker follows a process to some degree similar to drug development, until reaching clinical appli-
The most important unmet need in MS is
the identification of biomarkers for the prog-
nosis of the disease (prognostic biomark-
ers). MS is a very variable and unpredictable
disease, this situation being one of the most
disturbing aspects referred to by patients
(13). The uncertainty about the short- and
long-term disease course poses significant
difficulties to reach the appropriate balance
between risk and benefits for therapies, as
well as when making decisions about per-
sonal life. In addition, obtaining predictive
biomarkers—those providing information
about response to therapy—is also a priority
since decisions about drug prescription are
currently based on population information
(efficacy of the therapy in cohorts of patients
with a similar phenotype). By identifying
which patients are more likely to respond
well to a given therapy, or which are at risk
of developing serious side effects (e.g., pro-
gressive multifocal leukoencephalopathy
[PML]), we can maximize the patient’s ben-
efits for a given therapy, significantly
improving the patient’s quality of life and
healthcare management. Moreover, devel-
opment of pharmacodynamic biomarkers
will permit the most convenient dosage to
be selected and adjusted over time.
Finally, biomarkers can be specific and
informative of the pathogenic processes
active in a given patient. As explained above,
in complex diseases like MS there may be
many pathogenic processes ongoing at dif-
ferent levels and in different individuals.
Hence, more knowledge of the active path-
ways would allow physicians to make a more
precise diagnosis at the physiopathological
level, which will drive disease management.
Indeed, different biomarkers have been pro-
posed for the inflammatory process (both
innate and immune system response), infec-
tions, demyelination, axonal damage and
neurodegeneration, although these markers
still remain to be validated (14, 15).
PROPOSED BIOMARKERS FOR MS
In a PubMed search using the key words
“multiple sclerosis” and “biomarker”, we
obtained 2,980 entries reporting the identi-
fication of new molecular, cellular, imaging
or clinical variables associated with the
course of MS or its response to therapy.
However, a search using the same terms
plus the key words suggested by the STARD
criteria (“diagnostic accuracy”, “sensitivity”,
specificity” or “AUC”) produced only 308
hits. This indicates the bias in research (and
publishing) towards the screening for new
biomarkers rather than validating previously
reported markers. This could reflect the fact
that exploratory studies can be done with a
small cohort of patients, whereas validation
requires prospective multicenter studies,
which are significantly more complex and
The Biomarkers Module of Thomson Reuters
ty.com) is a specialized, manually curated
text-mining database that contains bio-
marker information published in scientific
papers and patents. A search of this data-
base using the condition “Multiple
Sclerosis” identified 319 entries, and it was
striking that the majority of these were in
the exploratory stage (including emerging,
experimental, early and late studies in
humans), and none had reached the known
or regulatory level. Moreover, the majority of
the biomarkers were related with new high
throughput techniques (genomics and pro-
teomics; Fig. 2A) (16). In terms of their med-
ical application, they were mainly focused
on the diagnosis of MS, including differen-
tial diagnosis (Fig. 2B), and only a few
addressed the unmet need in MS for prog-
nostic and predictive biomarkers.
A list of proposed biomarkers (molecular
and imaging) for MS is shown in Tables I and
II, which may not be complete if any mole-
cules or imaging techniques exist that have
not been described as biomarkers for MS.
The best known biomarkers for MS at the
molecular level is the presence of oligoclon-
al bands (OCB) in cerebrospinal fluid (CSF)
or an increase in the IgG index (11). OCB
have a known biomarker status, although
they may never reach the regulatory status
due to the lack of current commercial inter-
est. OCB have a high sensitivity and speci-
ficity for the diagnosis of MS and in addition,
the presence of OCB is a prognostic bio-
marker for the conversion from clinically iso-
lated syndrome to relapsing-remitting MS
(17, 18). Also, the presence of IgM OCB
seems to be associated with a more aggres-
sive disease (19, 20).
The field of genomics, including genetic
studies (genetic association studies and
linkage studies) and gene expression stud-
ies (DNA arrays, real time PCR), has been
particularly fruitful in providing new candi-
date markers, although outside of the
human leukocyte antigen (HLA) class II it
has been quite poor in validating them (21,
22). However, recent genome-wide associa-
tion studies have started to provide valida-
tions for some such markers (Table I),
although the low odds ratio of such genes
might prevent their usefulness as biomark-
ers of MS. The case of HLA is a particular
example of the difficulties in biomarker dis-
covery since even if the association between
HLA-DRB1*1501 exists and it is associated
with a significant risk of suffering the dis-
ease (23), its utility is significantly impaired
by the fact that it is also a common allele in
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
Box 1. Biomarker definition (FDA)
• Valid (regulatory) biomarker: A biomarker that is measured in an analytical test system with well-established performance characteristics and for which
there is an established scientific framework or body of evidence that elucidates the physiologic, toxicologic, pharmacologic or clinical significance of the
• Known valid biomarker: A biomarker that is measured in an analytical test system with well-established performance characteristics and for which there
is widespread agreement in the medical or scientific community about the physiologic, toxicologic, pharmacologic or clinical significance of the results.
• Probable valid biomarker: A biomarker that is measured in an analytical test system with well-established performance characteristics and for which
there is a scientific framework or body of evidence that appears to elucidate the physiologic, toxicologic, pharmacologic or clinical significance of the
Source: U.S. FDA. Guidance for Industry: Pharmacogenomic Data Submissions.
the general healthy population (30–60% of
the Caucasian population).
A new field in the search of biomarkers for
MS is pharmacogenomics and in a wider
perspective, biomarkers of the response to
therapy (predictive and pharmacodynamics
biomarkers) (24, 25). The first studies con-
ducted in this field have provided a list of
candidate biomarkers related with the
response to the most common therapies for
MS, such as interferon β or glatiramer
acetate (Table I) (26-29). Again, the com-
plexity of the disease and the mode of
action of these drugs make identifying
markers difficult, as does the lack of a good
definition of the response to therapy, the
pleiotropic activity of the drug and the het-
erogeneity of the disease.
Another hot topic in MS is the discovery of
self-antibodies in the blood. Antibodies are
ideal candidates as biomarkers because
they are very stable, specific for the antigen
and related to the pathogenesis of the dis-
ease. In MS, the search for antibodies has
been extensive, reporting many candidate
auto-antibodies (15, 30). However, none of
these have been validated to date, even
despite some extensive work as in the case
of anti-myelin-oligodendrocyte glycoprotein
antibodies (including the standardization of
many assays and well-conducted prospec-
tive studies) (31, 32). However, in the case of
neuromyelitis optica (NMO), the discovery of
the anti-aquaporin-4 antibody (anti-AQP4)
was further validated as having good sensi-
tivity and high specificity for the diagnosis of
the disease (33).
At the protein level, many of the candidate
markers reported are related to the study of
the pathogenesis of the disease, mainly at
the immunological level. They include
molecular mediators of the immune system
such as cytokines, chemokines, activation
markers or adhesion molecules (Table I) (34,
35). However, even after decades of
research and the confirmation that a few of
these molecules are involved in the patho-
genesis of the disease, none have been con-
firmed as valid biomarkers (known or regu-
latory biomarker). The field of proteomics is
now starting to generate a long list of candi-
dates that will ultimately require further val-
Among the other biomarkers explored are
metabolites, lipids, self-antigens, cell phe-
notypes or viruses. Metabolomics is a grow-
ing area of interest due to the recent tech-
nological advances enabling thousands of
metabolites to be screened. Due to the crit-
ical role of intermediate metabolism in the
immune response and the response of brain
tissue to damage, such molecules are
actively screened in other fields like cancer
and a few of them have been reported in MS
(Table I) (41). More recently, the role of vita-
min D in the susceptibility of MS has been
strengthened and it is actively pursued as a
marker of the disease (42). Although lipids
have been a topic of interest in MS research
given their prominent role in demyelination-
remyelination, technical limitations in the
study of lipids have hampered significant
advances in this field. However, new tech-
nologies may open the door to the efficient
analysis of lipids and to the new field of gly-
comics (43-45). Antigens represent a specif-
ic set of molecules with a distinct chemical
nature (and for this reason with different
technological needs) that are recognized by
the adaptive immune response. Identifying
the antigen targeted by the immune
response will allow the immune response
and the pathogenetic process to be moni-
tored. Since the explosion of molecular
immunology in the 1980s and the interest in
autoimmune diseases, a prominent search
for antigens involved in the pathogenesis of
MS has been carried out (34). However, we
have failed to definitely probe the main anti-
gen in MS, despite significant advances in
NMO with the discovery of AQP4. New tech-
nologies to screen hundreds of antigens in a
chip open new avenues to promote such dis-
Cell phenotyping has been another area of
intensive research due to the development
of cellular immunology. Although numerous
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
Figure 2. Current biomarkers in multiple sclerosis. A) Distribution of proposed multiple sclerosis bio-
markers (excluding imaging markers) available in the Biomarker Module of Thomson Reuters IntegritySM
database based on the technology and tissue and B) their role in clinical practice.
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
Table I. Candidate molecular biomarkers for multiple sclerosis.
HLA-DRB1 (1501, 1503, 0801, 0301, 0401, 1401), DRB5, HLA-DQA, HLA-DQB (0603), HLA-C
IL7R, IL2RA, CLEC16A, CD68, CD226, RPL5, DBC1, ALK, FAM69A, TYK2, CD6, IRF8, TNFRSF1A, SCIN, IL12A,
MPHOSPH9, RGS1, KIF21B, TMEM39A
ADAMTS14, AGER, ALS2, ALOX5, BANK, CD226, CCDC97, CYP2S1, CTLA4, FAM5A, LECAM2, GCCR, GSK3B, Exploratory
GPC5, AFGF, E1BAP5, ITGA4, ICAM1, IRF1, IFNGR1, IFNGR2, IL10, IL12, IL13, IL2RA, IL23R, IL3, IL4, IL4R, IL5,
IL6, IL7, IL7R, IL9, CMT2A, GLOD1, PTPRC, FDC, LFA3, MMP7, MMP9, TIMP3, MICB, MAPT, SLC25A8, MBP,
MAG, MPO, CMT1F, NPAS3, NPTXR, NT3, CARD15, OPN, CMT1A, PAI1, PECAM1, PLA2G7, PRR2, POU2AF1,
GGF2, NKNA, JAG1, PKCA, HIP, PON1, STAT1, FLJ22950, LAP18, MMP3, SOD1, SYN3, PLAT, TCF7, TGFB1,
TGFB2, TNFA, NGFR, GITR, TNFR2, TNFRSF5, 4-1BB, AXL, VEGF, VAMP
CASP3, TRAIL, FLIP, COL25, GPC5, HAPLN1, CAST, STAT1, IFNAR1, IFNAR2, MX1, IFNG, IL10, GRIA3, CIT,
ADAR, ZFAT, STARD13, ZFHX4, FADS1, MARCKS, IRF2, IRF4, IL4R, CASP10, CASP7, IL8, IFIT3, RASGEF1B,
IFIT1, OASL, IFI44, IFIT2, HLA-DRB1*1501, TCRB, CTSS
PDGFRA, BAX, BCL2, APAF1, API1, CASP1, CASP2, CASP6, CASP8, CASP10, P53, COL3A1, DOCK10, ADAM17,
EGR2, EPHX2, EAAT1, G3PD, C11, HBB, HAVCR, IFI6, IFITM1, IFITM3, IFNAR1, IFNAR2, ISG15, MX1, G10P1,
G10P2, IL1B, IL1A, IL10, IL12, IL4, IL5, CLEC5B, LY6E, LT, LAPTM5, MIF, MBP, MYD88, SIR2L1, NOTCH2,
FLJ00340, EBP-1, RIP15, PRDX5, PLSCR1, PSEN2, PDCD2, PDCD4, PARK7, JAG1, PKB, RSAD2, EB9, HIP,
NOGO, STK17A, TLR4, TLR6, NFKB3, TGFB1, TRIB1, TNFA, TRAIL, TNFSF12, APRIL, FASL, TNFRSF12A,
UBE4B, XIAPAF1, RASGEF1B, OASL, MARKS
Oligoclonal bands (OCB)IgG index, IgG OCB, IgM OCB, light chains Known
AntibodiesAnti-MBP, anti-MOG, anti-GalC, anti-PLP, anti-OSP, anti-CNPase, anti-transaldolase, anti-proteasome,
anti-β-arrestin, anti-Gangliosides, anti-CRYAB, anti-HSP60, anti-HSP70, anti-HSP90, anti-ATP2C1,
anti-KIAA1279, anti-PACSIN2, anti-SPAG16, anti-hnRNP B1, anti-Alu repeats, anti-NG2, anti-phosphatidyl-
choline, anti-NF, anti-NogoA, anti-tubulin, anti-enolase, anti-glycan, anti-triosephosphate isomerase (TPI),
Neutralizing antibodies of interferon β or natalizumab
Cytokines IL-1, IL-2, IL-3, IL-4, IL-5, IL-6, IL-10, IL-12, IL-13, IL-15, IL-17A, IL-18, IL-23, TNF-α, TGF-β, interferon β,
ChemokinesCCR2, CCR5, CCR7, CCL1, CCL2, CCL3, CCL4, CCL5, CCL8, CCL17, CCL21, CCL22, CXCR3, CXCR4,
CXCL5, CXCL10, CXCL12, CXCL13
ComplementC3, C3d, C4, C7 neoC9 Exploratory
Adhesion moleculesICAM-1, VCAM-I, E-selectin, L-selectin, LFA-1, VLA-4Exploratory
Activation markers CD25, CD40, CD80, CD86, CD26, CD30, OX40, Fas, TRAIL, OPN, CD127, CD45, CD47, CD16, CD279,
CD163, T-bet, CD1d, CD266, GITR, TNFR2
αβ-Crystallin, neurofilaments (light-chain), tau, actin, tubulin, 14-3-3, neuronal enolase
Nogo-A, Lingo, ALDH, α1B glycoprotein, α2-HS-glycoprotein, α-synuclein, Aβ, ANX1-5, ApoA (I, IV, B, D),
API1, βADRBK1, Arrestin, beta 1, beta-End, NGF, BDNF, CNTF, BRCA1, CRP, CB2, CD276, CD44,
chitotriosidase-1, chromogranin A, clusterin, contactin1, cystatin C, CD26, Mac-2 BP, gelsolin, GFAP,
haptoglobin, iNOS, IGFBP-3, interferon α, interferon γ, MxA, IL-1ra, kallikrein-1, kallikrein-6, Manan-binding
lectin serine protease-1, MMP-9, TIMP-3, MICB, MBP, MAG, NT3, OLIG2, P2X7R, PDGFB, PD-L1, PD-L2,
IGFBP3R, COX-2, DJ-1, PACSIN2, protein C inhibitor, S100A, S100B, RBP4, secretogranin I, transferrin,
serum paraoxonase/arylesterase 1, Stat-1, SCN2A, Sox-9, Sox-10, SPAG16, MMP-3, SOD1, tetranectin, tPA,
transferrin receptor, TGF-β, peripheral benzodiazepine receptor, transthyretin, TNFSF12, tissue factor, Fas,
vitamin D-binding protein, VDAC1, AZGP1
MetabolitesFolic acid, homocysteine, prostaglandin E2, vitamin D, vitamin B12, vitamin B6, hydroxyindoleacetic acid,
iron, malonaldehyde, N-acetylaspartate, neopterin, nitrates, orosomucoid, sorbitol, thiobarbituric acid
reactive species, cholesterol, 24S-hydroxycholesterol
LipidsGalactocerebroside, gangliosides, sphingolipids, phosphatidyl-serine, oxidized cholesterol derivativesExploratory
cell surface markers and cell populations
have been proposed to be associated with
MS (47), difficulties in the standardization of
the techniques and the dynamic nature of
the process have hampered the validation of
a given cell phenotype as a biomarker of the
disease. Nevertheless, the identification of
regulatory T cells that are impaired in MS
and that are modified by disease-modifying
drugs is currently a very active area of
Finally, measuring pathogen levels is also
another area in which there is an intense
search for biomarkers, particularly focusing
on viruses. The search for a pathogen asso-
ciated with MS has been constant and
although a role for environmental agents
has been proposed, along with associations
with different viruses and bacteria, to date
no such agent has been fully validated as a
biomarker (51, 52). This could be due to the
complex interaction between pathogens
and the immune system in the pathogenesis
of MS, which might prevent a single
pathogen from being detected in samples
from patients with MS. Nevertheless, in
recent years an association between
Epstein-Barr virus and MS has been clearly
established, although currently quantifying
viral load and Epstein-Barr virus antibody
titers is not sufficiently sensitive for it to
become a biomarker for the disease (53, 54).
Similarly, the association between human
herpes virus 6 or the MS-associated retro-
virus is promising, even though they have
not yet achieved the status as biomarkers for
the disease (55, 56).
BIOMARKERS BASED ON SAMPLE TYPE
OR PATHOGENIC PROCESS
Identification of biomarkers is most often
closely related with the samples available
for study. By contrast to oncology, where the
main focus is the target tissue, in MS the
study of the brain is extremely unusual (only
a few cases are study by biopsy), excluding
post-mortem studies. For this reason, the
main focus is the analysis of blood and CSF,
and to a lesser extent urine. Biomarkers
determined in blood will be the most con-
venient ones due to its availability and
because it is a tissue that can reflect the
activity of the immune system. Indeed, its
relationship with the target tissue, it seems
an ideal place for looking for pathogenic
markers of the disease. For this reason,
most of the studies performed to date have
been performed on serum, plasma or blood
cells, screening for DNA, messenger RNA,
proteins, metabolites, antibodies, cytokines,
viruses, etc. (Fig. 2B). Indeed, the CSF is cur-
rently the only tissue that has provided a
known biomarker, namely the OCB (if we
exclude brain MRI). Moreover, several bio-
markers of axonal damage and neurode-
generation have been proposed, including
light-chain neurofilaments, tau, β-amyloid,
protein S100-B, 14-3-3 protein, glial fibril-
lary acidic protein (GFAP) or neuronal eno-
lase (57-59). Recently, a new standard in the
collection and biobanking of CSF has been
proposed which is going to ultimately
improve the ability of detecting biomarkers
in this fluid (60). Urine has also been stud-
ied, which has the advantage of collecting
molecules produced by metabolism but the
disadvantage of being quite far from the two
systems of interest, the brain and the
immune system. Several biomarkers in urine
have been proposed, including neopterin,
nitrate, γ-globulins, myelin basic protein,
interleukins, prostaglandin or β-microglob-
ulin, but to date none of these have been
Another important area of research is the
discovery of biomarkers related to patholog-
ical processes, and several markers of
inflammation have been proposed, includ-
ing cytokines, chemokines, antibodies, com-
plement, adhesion molecules, antigen pres-
entation, cell cycle/apoptosis, etc. The
proposed markers of demyelination are
myelin basic protein-derived peptides or
myelin antibodies, while the markers of
axonal damage include neurofilaments, tau,
β-amyloid, protein S100-B, GFAP or neu-
ronal enolase (14, 59).
IMAGING AS A BIOMARKER FOR MS
Magnetic resonance imaging
Imaging is one of the most rapidly growing
areas of interest in the development of bio-
markers for complex diseases (65). In the
case of MS, MRI has completely changed
the diagnosis of the disease due its sensitiv-
ity to identify brain lesions and given that
the MS MRI criteria (Barkoff-Tintore criteria)
are also very specific (11, 12, 66). Several
imaging markers have been described as
prognostic biomarkers for MS, including the
presence of black hole, as well as the num-
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
Table I. Cont. Candidate molecular biomarkers for multiple sclerosis.
Antigens MOG, MBP, PLP, β-arrestin, contactin 2,
Cell phenotypesTreg (Foxp3+, Tr1, CD8reg)
Breg, NK cells, CD4, CD8, B cells (CD5+), macrophages, DC (myeloid and plasmacytoid)
VirusesEBV, HHV-6, MSRV, VZV Exploratory
1Status: Exploratory biomarker (including emerging, experimental, early and late studies in humans according to the Biomarkers Module of Thomson Reuters
IntegritySM); possible biomarker, known biomarker, regulatory biomarker (as per FDA definitions; see text).
2Genes discovered in genome-wide association studies and validated in subsequent studies.
3Genes reported as biomarkers in the Biomarkers Module of Thomson Reuters IntegritySM.
4Genes identified in pharmacogenomics studies for interferon-β therapy.
5Biomarker for neuromyelitis optica.
6Proteins identified by molecular biology methods (ELISA, Western blot), histology (immunohistochemistry or immunofluorescence) or proteomic methods in
different tissues (CSF, serum, brain, etc.).
ber and volume of T1, T2 and gadolinium-
enhancing lesions (Table II) (11, 67). These
phenomena have been validated in previous
studies, becoming considered as known bio-
markers, and they are used as surrogate
endpoints in phase II clinical trials. However,
they have still not reached the status of reg-
ulatory biomarkers approved by the FDA or
EMA as valid surrogate endpoints in phase
III clinical trials.
At the pathogenic level, MRI markers are
partially informative of the disease process.
Indeed, there is a close relationship between
the breakdown of the brain–blood barrier
and presence of gadolinium enhancement,
although we know that not all brain–blood
barrier damage is captured by this marker.
T1 lesions are more closely associated with
axonal loss and tissue damage, although
such an association is far from perfect.
However, the pathogenic substrate of T2
lesions is more complex, including the pres-
ence of edema, demyelination, inflamma-
tion, astrogliosis or axonal loss. Hence, T2
lesions would appear to lack specificity as a
biomarker of pathogenic processes (67, 68).
Brain atrophy is the imaging counterpart of
tissue destruction and tissue loss is current-
ly captured with brain volume measure-
ments using voxel-based morphometry.
Several markers have been described along
such lines, including the brain parenchymal
fraction, the volume of grey and white mat-
ter, cervical cord or regional volumes (e.g.,
thalamus volume) (68). Such measure-
ments are associated with disease pheno-
type and disability, although they remain to
be validated as prognostic markers of the
Other MRI techniques that offer more infor-
mation about the disease and that may
serve as biomarkers of MS are magnetic
transfer imaging to calculate the magnetic
transfer ratio, MR spectroscopy, diffusion
weighted imaging, functional MRI, fractal
dimension analysis or texture analysis (69).
Magnetic transfer ratio provides a quantita-
tive measurement that is related to the
extent of demyelination, although difficul-
ties in standardizing the technique have
prevented it from entering multicenter stud-
ies (70). MR spectroscopy provides quanti-
tative information about several brain
metabolites (Table II) and although it is cur-
rently used in the differential diagnosis of
several brain diseases (71), it has not been
validated as a marker for MS. Diffusion
weighted imaging is a rapidly advancing
technology that has provided several mark-
ers of brain damage related to the restricted
movement of molecules, yet validation stud-
ies are still necessary before it can produce
biomarkers for MS (72). Functional MRI pro-
vides information about brain activation by
measuring changes in blood flow. While it is
extensively used in cognitive sciences and it
has revealed how brain damage impairs
cognition in MS, it is still not a validated
marker for MS (68). Our group developed
fractal dimension analysis of the grey and
white matter as a marker for MS (73, 74).
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
Table II. Imaging markers for multiple sclerosis.
MS MRI criteria (Barkoff–Tintore) 3–4 criteria: periventricular (3), juxtacortical, gad+, infratentorial, spinal cord lesionsKnown
T1 Number and volume of T1 lesions, “black-holes” Known
T2 Number and volume of T2 lesionsKnown
Gadolinium (gad+) Number and volume of gad+ lesionsKnown
Ion particles contrast Number and volume of iron+ lesionsExploratory
Double inversion recoveryCortical lesionsExploratory
Brain volumeBrain parenchymal fraction, grey matter volume, white matter volume, spinal cord (cervical) volume,
Magnetization transfer MRIMagnetization transfer ratioPossible
SpectroscopyNAA, glutamate, glutamine, GABA, choline, creatinine, myoinositol
glutathione, ascorbic acid
Diffusion MRIMean diffusivity, diffusion tensor (tractography and voxelwise analysis) Possible
Fractal dimension (FD) White matter FD, grey matter FD Exploratory
Texture analysisGradient matrix, run-length matrix, grey level co-occurrence matrix, autoregressive model, wavelet
fMRI Regional activation (BOLD)Known
Optic coherent tomographyThickness retinal nerve fiber layer and quadrants
Positron emission tomographyPeripheral benzodiazepine receptor ligand (PK-11195, [11C]-vinpocetine)Exploratory
EchographyHypoechogenicity/hyperechogenicity Deep nuclei, third ventricleExploratory
1See explanation in Table I. MS, multiple sclerosis; MRI, magnetic resonance imaging.
Fractal dimension captures the topological
complexity of an object like the brain, and it
is sensitive to the brain atrophy and
tissue changes related to MS, even at the
early stages of the disease. Indeed, frac-
tal dimension by MRI is currently in the
process of being validated as a biomarker
Optic coherence tomography
Optic coherence tomography (OCT) provides
high-resolution measurements of the thick-
ness of the retinal nerve fiber layer (RNFL) or
the macula (75). The visual system is fre-
quently affected by the disease, leading to
axonal loss, which is captured by the RNFL
measurement at the head of the optic nerve.
We performed a study to validate the RNFL
thickness measured with OCT as a prognos-
tic biomarker of MS, detecting high specifici-
ty and intermediate sensitivity for predicting
future relapses and increases in the
Expanded Disability Status Scale 2 years
later (76). OCT holds promise as a biomarker
of the neurodegenerative processes in MS
and to become a surrogate marker for neuro-
protective therapies (68). The technological
advance represented by spectral-domain
OCT, with a very high spatio- temporal reso-
lution will significantly help make OCT a use-
ful biomarker for MS (77).
COMBINING BIOMARKERS WITH THE
CLINICAL MARKERS OF MS
Decades of research in the natural history of
MS have identified several clinical variables
as markers of MS (Table III). By contrast to the
biomarker field, the search for clinical mark-
ers of MS has led to the validation of many
known markers, such as those for the time to
the second relapse, relapse rate, progressive
subtype or reaching several disability steps
(78). Recently, low contrast visual acuity was
accepted as another known marker of the dis-
ease, which is also used as an endpoint in
clinical trials (79). Neurophysiological stud-
ies, including the measurement of evoked
potentials, can identify subclinical damage of
brain pathways and they are useful in the
diagnosis of the disease. Visual evoked
potentials and motor evoked potentials have
been explored as prognostic markers of the
disease, although more validation studies will
be necessary for them to become useful
The challenge in biomarker and clinical
research for MS is now how to combine all the
information and measurements these tech-
niques have made available in order to better
predict disease evolution and response to
therapy. As explained before, the complexity
and multifactorial nature of MS makes it diffi-
cult that a single biomarker will capture all
the dimensions of the disease, proving accu-
rate as a prognostic marker. For this reason,
developing algorithms or scores that com-
bine clinical, imaging and biological informa-
tion is a promising approach to the problem.
Accordingly, the use of computational classi-
fiers developed in the field of medical infor-
matics (neuronal networks, decision trees,
Bayesian classifiers or regression classifiers)
could be a good way to select the most useful
information and maximize its predictive abili-
DEFINING THE RESPONSE TO THERAPY
Predictive biomarkers are also of critical
interest in MS due to the limited efficacy,
cost and side effects associated with current
therapies. Current efforts are focused on
identifying biomarkers to identify respon-
ders and nonresponders to first-line thera-
pies (interferon β and glatiramer acetate), or
to predict and identify individuals at risk of
developing severe side effects at early
stages (e.g., PML in natalizumab-treated
patients) (84, 85). Most of the main efforts
along these lines have been made in phar-
macogenomics and imaging for interferon
β therapy. Several genes have been associ-
ated with the response to interferon β (Table
I) and the persistence of gad+lesions, or the
increase in the T1 or T2 lesion load, are being
pursued as markers of failure to respond to
the therapy (27, 85, 86). This is a growing
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
Table III. Clinical markers for multiple sclerosis.
Clinical variableMarker Status1
Disease subtype CIS, RR, SP, PP, PR Known
DemographicsSex (male), age at onset (> 40 years)
Month of birth, familiar MS
Topography first relapse Motor, cerebellar, polyregional: bad outcome;
Sensitive or visual: good outcome
Disability after first relapseEDSS > 0: bad outcomeKnown
Time to second relapse< 1 year: bad outcome Known
Relapse frequency Number of relapses previous 2 yearsKnown
Disability stepsTime to EDSS 4.0
Time to EDSS 6.0
Time to EDSS 7.0
Low contrast visual acuity 2.5%, 1.25% contrast visual acuity Possible
Visual evoked potentialsLatency P100 Possible
Motor evoked potentialsLatency, amplitude Possible
1See explanation in Table I. CIS, clinically isolated syndrome; RR, relapsing-remitting; SP, secondary-progressive; PP, primary-progressive; PR, progressive-
relapsing; EDSS, Expanded Disability Status Scale.
area of interest that physicians, patients and
addressing under the paradigm of personal-
ized or stratified medicine, and it will clearly
pay dividends in the near future (2).
Research into biomarkers for MS is a very
active area that should move from the dis-
covery phase to the validation stage in order
to transfer the advances of biomedical
research to clinical practice, and to provide
benefits to patients and society. This process
is going to require an effort by clinicians,
researchers, funding agencies and journal
editors, placing emphasis on the validation
steps in order to provide more effective
markers. Validation requires conducting
prospective multicenter studies on large,
statistically relevant cohorts, the standardi-
zation of the techniques, the evaluation of
intercenter or intermachine variability,
reporting the diagnostic accuracy of the bio-
markers using accepted criteria such as
STARD, and applying for regulatory
approval when appropriate. In the process
of developing personalized medicine, we
must move from population data (statistics)
to individual predictions. Biomarkers must
be easy to apply in clinical practice, robust
to the center and platform differences, and
cost effective. In the field of demyelinating
diseases, we have seen dramatic changes in
the management of NMO through the dis-
covery of a biomarker such as anti-AQP4
antibody. However, in terms of MS, we have
seen that promising biomarkers such as
HLA or myelin antibodies have not fulfilled
expectations, although many other bio-
markers are in the pipeline to fill this niche.
This work was supported by the Instituto de
Salud Carlos III - RETICS program
The author has received consultancy fees
from Prous Science.
1. Rees, J. Complex disease and the new clinical
sciences.Science 2002, 296(5568): 698-700.
2. Trusheim, M.R., Berndt, E.R., Douglas, F.L.
Stratified medicine: strategic and economic
implications of combining drugs and clinical
biomarkers. Nat Rev Drug Discov 2007, 6(4):
3. Biomarkers and surrogate endpoints: preferred
definitions and conceptual framework. Clin
Pharmacol Ther 2001, 69(3): 89-95.
4. Prentice, R.L. Surrogate endpoints in clinical
trials: definition and operational criteria. Stat
Med 1989, 8(4): 431-40.
5. Bielekova, B., Martin, R. Development of bio-
markers in multiple sclerosis. Brain 2004,
127(Pt 7): 1463-78.
6. Sawyers, C.L. The cancer biomarker problem.
Nature 2008, 452(7187): 548-52.
7. Villoslada, P., Steinman, L., Baranzini, S.E.
Systems biology and its application to the
understanding of neurological diseases. Ann
Neurol 2009, 65(2): 124-39.
8. FDA, Guidance for Industry: Pharmaco-
genomic Data Submissions. U.S. FDA,
Washington, D.C., 2005.
9. Bossuyt, P.M., Reitsma, J.B., Bruns, D.E. et al.
The STARD statement for reporting studies of
diagnostic accuracy: explanation and elabora-
tion. Clin Chem 2003, 49(1): 7-18.
10. McShane, L.M., Altman, D.G., Sauerbrei, W.,
Taube, S.E., Gion, M., Clark, G.M. REporting
recommendations for tumor MARKer prognos-
tic studies (REMARK). Nat Clin Pract Oncol
2005, 2(8): 416-22.
11. Polman, C.H., Reingold, S.C., Edan, G. et al.
Diagnostic criteria for multiple sclerosis: 2005
revisions to the “McDonald Criteria”. Ann
Neurol 2005, 58(6): 840-6.
12. Montalban, X., Tintore, M., Swanton, J. et al.
MRI criteria for MS in patients with clinically
isolated syndromes. Neurology 2010, 74(5):
13. Janssens, A.C., van Doorn, P.A., de Boer, J.B.,
van der Meche, F.G., Passchier, J., Hintzen,
R.Q. Perception of prognostic risk in patients
with multiple sclerosis: the relationship with
anxiety, depression, and disease-related dis-
tress. J Clin Epidemiol 2004, 57(2): 180-6.
14. Tumani, H., Hartung, H.P., Hemmer, B.,
Teunissen, C., Deisenhammer, F., Giovannoni,
G., Zettl, U.K. Cerebrospinal fluid biomarkers
in multiple sclerosis. Neurobiol Dis 2009,
15. Reindl, M., Khalil, M., Berger, T. Antibodies as
biological markers for pathophysiological
processes in MS. J Neuroimmunol 2006,
16. Ibrahim, S.M., Gold, R. Genomics, proteomics,
metabolomics: what is in a word for multiple
sclerosis? Curr Opin Neurol 2005, 18(3): 231-
17. Villar, L.M., Garcia-Barragan, N., Sadaba,
M.C. et al. Accuracy of CSF and MRI criteria for
dissemination in space in the diagnosis of
multiple sclerosis. J Neurol Sci 2008, 266(1-
18. Tintore, M., Rovira, A., Rio, J. et al. Do oligo-
clonal bands add information to MRI in first
attacks of multiple sclerosis? Neurology 2007,
19. Villar, L.M., Sadaba, M.C., Roldan, E. et al.
Intrathecal synthesis of oligoclonal IgM
against myelin lipids predicts an aggressive
disease course in MS. J Clin Invest 2005,
20. Thangarajh, M., Gomez-Rial, J., Hedstrom,
A.K., Hillert, J., Alvarez-Cermeno, J.C.,
Masterman, T., Villar, L.M. Lipid-specific
immunoglobulin M in CSF predicts adverse
long-term outcome in multiple sclerosis. Mult
Scler 2008, 14(9): 1208-13.
21. Oksenberg, J.R., Baranzini, S.E., Sawcer, S.,
Hauser, S.L. The genetics of multiple sclerosis:
SNPs to pathways to pathogenesis. Nat Rev
Genet 2008, 9(7): 516-26.
22. Fugger, L., Friese, M.A., Bell, J.I. From genes
to function: the next challenge to understand-
ing multiple sclerosis. Nat Rev Immunol
2009, 9(6): 408-17.
23. Barcellos, L.F., Sawcer, S., Ramsay, P.P. et al.
Heterogeneity at the HLA-DRB1 locus and risk
for multiple sclerosis. Hum Mol Genet 2006,
24. Pappas, D.J., Oksenberg, J.R. Multiple sclero-
sis pharmacogenomics: maximizing efficacy of
therapy. Neurology, 74 Suppl 1: S62-9.
25. Martinez-Forero, I., Pelaez, A., Villoslada, P.
Pharmacogenomics of multiple sclerosis: in
search for a personalized therapy. Expert Opin
Pharmacother 2008, 9(17): 3053-67.
26. Baranzini, S.E., Mousavi, P., Rio, J. et al.
Transcription-based prediction of response to
IFNbeta using supervised computational
methods. PLoSBiol 2005, 3(1): e2.
27. Byun, E., Caillier, S.J., Montalban, X. et al.
Genome-wide pharmacogenomic analysis of
the response to interferon beta therapy in
multiple sclerosis. Arch Neurol 2008, 65(3):
28. O’Doherty, C., Favorov, A., Heggarty, S. et al.
Genetic polymorphisms, their allele combina-
tions and IFN-beta treatment response in Irish
multiple sclerosis patients.
genomics 2009, 10(7): 1177-86.
29. Grossman, I., Avidan, N., Singer, C. et al.
Pharmacogenetics of glatiramer acetate thera-
py for multiple sclerosis reveals drug-response
markers. Pharmacogenet Genomics 2007,
30. Berger, T., Reindl, M. Biomarkers in multiple
sclerosis: role of antibodies. Dis Markers 2006,
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
31. Lalive, P.H., Menge, T., Delarasse, C. et al.
Antibodies to native myelin oligodendrocyte
glycoprotein are serologic markers of early
inflammation in multiple sclerosis. Proc Natl
Acad Sci U S A 2006, 103(7): 2280-5.
32. Polman, C.H., Killestein, J. Anti-myelin anti-
bodies in multiple sclerosis: clinically useful? J
Neurol Neurosurg Psychiatry 2006, 77(6):
33. McKeon, A., Fryer, J.P., Apiwattanakul, M. et
al. Diagnosis of neuromyelitis spectrum disor-
ders: comparative sensitivities and specificities
of immunohistochemical and immunoprecipi-
tation assays. Arch Neurol 2009, 66(9): 1134-
34. Sospedra, M., Martin, R. Immunology of mul-
tiple sclerosis. Annu Rev Immunol 2005, 23:
35. Hauser, S.L., Oksenberg, J.R. The neurobiolo-
gy of multiple sclerosis: genes, inflammation,
and neurodegeneration. Neuron 2006, 52(1):
36. Han, M.H., Hwang, S., Roy, D.B. et al.
Proteomic analysis of active multiple sclerosis
lesions reveals therapeutic targets in the
coagulation cascade. Nature 2007, 451: 1076-
37. Berven, F.S., Flikka, K., Berle, M., Vedeler, C.,
Ulvik, R.J. Proteomic-based biomarker discov-
ery with emphasis on cerebrospinal fluid and
multiple sclerosis. Curr Pharm Biotechnol
2006, 7(3): 147-58.
38. Ottervald, J., Franzen, B., Nilsson, K. et al.
Multiple sclerosis: Identification and clinical
evaluation of novel CSF biomarkers. J
Proteomics 2010, 73(6): 1117-32.
39. Satoh, J.I., Tabunoki, H., Yamamura, T.
Molecular network of the comprehensive multi-
ple sclerosis brain-lesion proteome. Mult Scler
2009, 15(5): 531-41.
40. Stoop, M.P., Dekker, L.J., Titulaer, M.K. et al.
Quantitative matrix-assisted laser desorption
ionization-fourier transform ion cyclotron reso-
nance (MALDI-FT-ICR) peptide profiling and
identification of multiple-sclerosis-related pro-
teins. J Proteome Res 2009, 8(3): 1404-14.
41. Sinclair, A.J., Viant, M.R., Ball, A.K. et al.
NMR-based metabolomic analysis of cere-
brospinal fluid and serum in neurological dis-
eases - a diagnostic tool? NMR Biomed 2010,
42. Ascherio, A., Munger, K.L. Environmental risk
factors for multiple sclerosis. Part II:
Noninfectious factors.Ann Neurol 2007, 61(6):
43. Kanter, J.L., Narayana, S., Ho, P.P. et al. Lipid
microarrays identify key mediators of autoim-
mune brain inflammation. Nat Med 2006,
44. Podbielska, M., Hogan, E.L. Molecular and
immunogenic features of myelin lipids: inci-
tants or modulators of multiple sclerosis? Mult
Scler 2009, 15(9): 1011-29.
45. An, H.J., Kronewitter, S.R., de Leoz, M.L.,
Lebrilla, C.B. Glycomics and disease markers.
Curr Opin Chem Biol 2009, 13(5-6): 601-7.
46. Quintana, F.J., Farez, M.F., Viglietta, V. et al.
Antigen microarrays identify unique serum
autoantibody signatures in clinical and patho-
logic subtypes of multiple sclerosis. Proc Natl
Acad Sci U S A 2008, 105(48): 18889-94.
47. Kasper, L.H., Shoemaker, J. Multiple sclerosis
immunology: The healthy immune system vs
the MS immune system. Neurology 74(Suppl.
48. Venken, K., Hellings, N., Liblau, R., Stinissen,
P. Disturbed regulatory T cell homeostasis in
multiple sclerosis. Trends Mol Med, 16(2): 58-
49. Martinez-Forero I,
Martinez-Pasamar A et al. IL-10 suppressor
activity and ex vivo Tr1 cell function are
impaired in Multiple Sclerosis. Eur J Immunol
2008, 38(2): 576-86.
50. Zozulya, A.L., Wiendl, H. The role of regulato-
ry T cells in multiple sclerosis. Nat Clin Pract
Neurol 2008, 4(7): 384-98.
51. Ascherio, A., Munger, K.L. Environmental risk
factors for multiple sclerosis. Part I: the role of
infection. Ann Neurol 2007, 61(4): 288-99.
52. Fotheringham, J., Jacobson, S. Human her-
pesvirus 6 and multiple sclerosis: potential
mechanisms for virus-induced disease. Herpes
2005, 12(1): 4-9.
53. Villoslada, P., Juste, C., Tintore, M., Llorenc, V.,
Codina, G., Pozo-Rosich, P., Montalban, X.
The immune response against herpesvirus is
more prominent in the early stages of MS.
Neurology 2003, 60(12): 1944-8.
54. Lunemann, J.D., Ascherio, A. Immune
responses to EBNA1: biomarkers in MS?
Neurology 2009, 73(1): 13-4.
55. Alvarez-Lafuente, R., Garcia-Montojo, M., De
Las Heras, V., Dominguez-Mozo, M.I.,
Bartolome, M., Benito-Martin, M.S., Arroyo,
R. Herpesviruses and human endogenous
retroviral sequences in the cerebrospinal fluid
of multiple sclerosis patients.Mult Scler 2008,
56. Alvarez-Lafuente, R., Garcia-Montojo, M., De
las Heras, V., Bartolome, M., Arroyo, R.
Clinical parameters and HHV-6 active replica-
tion in relapsing-remitting multiple sclerosis
patients.J Clin Virol 2006, 37(Suppl. 1): S24-6.
57. Brettschneider, J., Petzold, A., Junker, A.,
Tumani, H. Axonal damage markers in the
cerebrospinal fluid of patients with clinically
isolated syndrome improve predicting conver-
sion to definite multiple sclerosis. Mult Scler
2006, 12(2): 143-8.
58. Hein Nee Maier, K., Kohler, A., Diem, R. et al.
Biological markers for axonal degeneration in
CSF and blood of patients with the first event
indicative for multiple sclerosis. Neurosci Lett
2008, 436(1): 72-6.
59. Teunissen, C., Dijkstra, C.D., Polman, C.
Biological markers in CSF and blood for axon-
al degeneration in multiple sclerosis. Lancet
Neurol 2005, 4(1): 32-41.
60. Teunissen, C.E., Petzold, A., Bennett, J.L. et
al. A consensus protocol for the standardiza-
tion of cerebrospinal fluid collection and
biobanking. Neurology 2009, 73(22): 1914-
61. Giovannoni, G., Thompson, E.J. Urinary mark-
ers of disease activity in multiple sclerosis.Mult
Scler 1998, 4(3): 247-53.
62. Malcus-Vocanson, C., Giraud, P., Broussolle,
E., Perron, H., Mandrand, B., Chazot, G. A uri-
nary marker for multiple sclerosis. Lancet
1998, 351(9112): 1330.
63. Dobson, R., Miller, R.F., Palmer, H.E. et al.
Increased urinary free immunoglobulin light
chain excretion in patients with multiple sclero-
sis. J Neuroimmunol 2010, 220(1-2): 99-103.
64. ‘t Hart, B.A., Vogels, J.T., Spijksma, G., Brok,
H.P., Polman, C., van der Greef, J. 1H-NMR
spectroscopy combined with pattern recogni-
tion analysis reveals characteristic chemical
patterns in urines of MS patients and non-
human primates with MS-like disease. J
Neurol Sci 2003, 212(1-2): 21-30.
65. Barkhof, F., Filippi, M. MRI—the perfect surro-
gate marker for multiple sclerosis? Nat Rev
Neurol 2009, 5(4): 182-3.
66. Tintore, M., Rovira, A., Martinez, M.J. et al.
Isolated demyelinating syndromes: compari-
son of different MR imaging criteria to predict
conversion to clinically definite multiple sclero-
sis. AJNR 2000, 21(4): 702-6.
67. Miller, D.H. Biomarkers and surrogate out-
comes in neurodegenerative disease: lessons
from multiple sclerosis. NeuroRx 2004, 1(2):
68. Barkhof, F., Calabresi, P.A., Miller, D.H.,
Reingold, S.C. Imaging outcomes for neuro-
protection and repair in multiple sclerosis tri-
als. Nat Rev Neurol 2009, 5(5): 256-66.
69. Zhang, Y., Zhu, H., Mitchell, J.R., Costello, F.,
Metz, L.M. T2 MRI texture analysis is a sensi-
tive measure of tissue injury and recovery
resulting from acute inflammatory lesions in
multiple sclerosis. Neuroimage 2009, 47(1):
70. Filippi, M., Agosta, F. Magnetization transfer
MRI in multiple sclerosis. J Neuroimaging
2007, 17(Suppl. 1): 22S-6S.
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
71. De Stefano, N., Filippi, M. MR spectroscopy in
multiple sclerosis. J Neuroimaging 2007,
17(Suppl. 1): 31S-5S.
72. Rovaris, M., Agosta, F., Pagani, E., Filippi, M.
Diffusion tensor MR imaging. Neuroimaging
Clin N Am 2009, 19(1): 37-43.
73. Esteban, F., Sepulcre, J., Ruiz de Miras, J. et al.
Fractal dimension analysis of grey matter in mul-
tiple sclerosis J Neurol Sci 2009, 282(1-2): 67-71.
74. Esteban, F.J., Sepulcre, J., de Mendizabal,
N.V. et al. Fractal dimension and white matter
changes in multiple sclerosis. Neuroimage
2007, 36(3): 543-9.
75. Frohman, E.M., Fujimoto, J.G., Frohman, T.C.,
Calabresi, P.A., Cutter, G., Balcer, L.J. Optical
coherence tomography: a window into the
mechanisms of multiple sclerosis. Nat Clin
Pract Neurol 2008, 4(12): 664-75.
76. Sepulcre, J., Murie-Fernandez, M., Salinas-
Alaman, A., Garcia-Layana, A., Bejarano, B.,
Villoslada, P. Diagnostic accuracy of retinal
abnormalities in predicting disease activity in
MS. Neurology 2007, 68(18): 1488-94.
77. Menke, M.N., Dabov, S., Knecht, P., Sturm, V.
Reproducibility of retinal thickness measure-
ments in healthy subjects using spectralis opti-
cal coherence tomography. Am J Ophthalmol
2009, 147(3): 467-72.
78. Confavreux, C., Vukusic, S. Natural history of
multiple sclerosis: a unifying concept. Brain
2006, 129(Pt 3): 606-16.
79. Baier, M.L., Cutter, G.R., Rudick, R.A. et al.
Low-contrast letter acuity testing captures
visual dysfunction in patients with multiple
sclerosis. Neurology 2005, 64(6): 992-5.
80. Leocani, L., Comi, G. Neurophysiological
markers. Neurol Sci 2008, 29(Suppl. 2):
81. Villoslada, P., Oksenberg, J. Neuroinformatics
in clinical practice: are computers going to help
neurological patients and their physicians?
Future Neurology 2006, 1(2): 1-12.
82. Bates, D.W., Gawande, A.A. Improving safety
with information technology. N Engl J Med
2003, 348(25): 2526-34.
83. Tegner, J.N., Compte, A., Auffray, C. et al.
Computational disease modeling - fact or fic-
tion? BMC Syst Biol 2009, 3: 56.
84. Villoslada, P., Oksenberg, J.R., Rio, J.,
Montalban, X. Clinical characteristics of
responders to interferon therapy for relapsing
MS. Neurology 2004, 62(9): 1653.
85. Rio, J., Comabella, M., Montalban, X. Predic-
ting responders to therapies for multiple scle-
rosis. Nat Rev Neurol 2009, 5(10): 553-60.
86. O’Doherty, C., Villoslada, P., Vandenbroeck, K.
Pharmacogenomics of Type I interferon thera-
py: a survey of response-modifying genes.
Cytokine Growth Factor Rev 2007, 18(3-4):
Pablo Villoslada, Department of Neurosciences, Institute
of Biomedical Research August Pi Sunyer (IDIBAPS) –
Hospital Clinic of Barcelona, Spain. Correspondence:
Pablo Villoslada, M.D., Department of Neurology,
Hospital Clinic, Villarroel 170, 08017 Barcelona, Spain. E-
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)
MS BIOMARKERS Download full-text
THOMSON REUTERS – Drug News & Perspectives 2010, 23(9)