Pharmacogenomics of Interferon-ß Therapy in Multiple
Sclerosis: Baseline IFN Signature Determines
Pharmacological Differences between Patients
Lisa G. M. van Baarsen1,2., Saskia Vosslamber1,2., Marianne Tijssen2, Josefien M. C. Baggen1, Laura F.
van der Voort3, Joep Killestein3, Tineke C. T. M van der Pouw Kraan1, Chris H. Polman3, Cornelis L.
1Department of Molecular Cell Biology and Immunology, VU Medical Center, Amsterdam, The Netherlands, 2Department of Pathology, VU Medical Center, Amsterdam,
The Netherlands, 3Department of Neurology, VU Medical Center, Amsterdam, The Netherlands
Background: Multiple sclerosis (MS) is a heterogeneous disease. In order to understand the partial responsiveness to IFNß in
Relapsing Remitting MS (RRMS) we studied the pharmacological effects of IFNß therapy.
Methodology: Large scale gene expression profiling was performed on peripheral blood of 16 RRMS patients at baseline
and one month after the start of IFNß therapy. Differential gene expression was analyzed by Significance Analysis of
Microarrays. Subsequent expression analyses on specific genes were performed after three and six months of treatment.
Peripheral blood mononuclear cells (PBMC) were isolated and stimulated in vitro with IFNß. Genes of interest were
measured and validated by quantitative realtime PCR. An independent group of 30 RRMS patients was used for validation.
Principal Findings: Pharmacogenomics revealed a marked variation in the pharmacological response to IFNß between
patients. A total of 126 genes were upregulated in a subset of patients whereas in other patients these genes were
downregulated or unchanged after one month of IFNß therapy. Most interestingly, we observed that the extent of the
pharmacological response correlates negatively with the baseline expression of a specific set of 15 IFN response genes
(R=20.7208; p=0.0016). The negative correlation was maintained after three (R=20.7363; p=0.0027) and six
(R=20.8154; p=0.0004) months of treatment, as determined by gene expression levels of the most significant correlating
gene. Similar results were obtained in an independent group of patients (n=30; R=20.4719; p=0.0085). Moreover, the ex
vivo results could be confirmed by in vitro stimulation of purified PBMCs at baseline with IFNß indicating that differential
responsiveness to IFNß is an intrinsic feature of peripheral blood cells at baseline.
Conclusion: These data imply that the expression levels of IFN response genes in the peripheral blood of MS patients prior
to treatment could serve a role as biomarker for the differential clinical response to IFNß.
Citation: van Baarsen LGM, Vosslamber S, Tijssen M, Baggen JMC, van der Voort LF, et al. (2008) Pharmacogenomics of Interferon-ß Therapy in Multiple Sclerosis:
Baseline IFN Signature Determines Pharmacological Differences between Patients. PLoS ONE 3(4): e1927. doi:10.1371/journal.pone.0001927
Editor: Hans Lassmann, University of Vienna, Austria
Received January 24, 2008; Accepted February 26, 2008; Published April 2, 2008
Copyright: ? 2008 van Baarsen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded in part by the Dutch MS Research Foundation (Voorschoten, Netherlands) and by the Centre for Medical Systems Biology (a
center of excellence approved by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research). Furthermore, the Dutch MS Research
Foundation also supports S. Vosslamber (grant 04-549 MS). The funding sources of this study had no involvement in study design, in the collection, analyses and
interpretation of data, in the writing of the report, and in the decision to submit the paper for publication.
Competing Interests: Prof. Polman reports having received the following: consulting fees from Biogen Idec, Schering AG, Teva, Serono, Novartis,
GlaxoSmithKline, UCB, Astra Zeneca, Roche and Antisense Therapeutics, lecture fees from Biogen Idec, Schering AG, Novartis and Teva, and grant support from
Biogen Idec, Schering AG , GlaxoSmithKline, Novartis, Serono and Teva. Joep Killestein and Laura F. van der Voort worked with companies that market drugs for
MS (Schering, Biogen Idec, Serono, Teva) and with some companies that have development programmes for future drugs in MS. Both authors are partially funded
by NABINMS, a specific targeted research project on neutralising antibodies to interferon beta in MS, established by the European Commission under its 6th
Framework Programme. The VU University Medical Center has filed a provisional patent application entitled ‘‘Means and methods for classifying samples of
multiple sclerosis patients.’’ that is based on the present work. LB, CP and CV are listed as co-inventors on that provisional patent application.
* E-mail: firstname.lastname@example.org
. These authors contributed equally to this work.
Multiple sclerosis (MS) is a common inflammatory disease of
the central nervous system characterized by progressive neurolog-
ical dysfunction. The disease has a heterogeneous nature, which
is reflected in the clinical presentation, ranging from mild to
severe demyelinating disease. No curative therapy is currently
available, and the majority of affected individuals are ultimately
IFNs were the first agents to show clinical efficacy in RRMS.
Interferon beta (IFNß) decreases clinical relapses, reduces brain
disease activity, and possibly slows down progression of disability.
However, therapy is associated with a number of adverse
reactions, including flu-like symptoms and transient laboratory
PLoS ONE | www.plosone.org1 April 2008 | Volume 3 | Issue 4 | e1927
abnormalities. Moreover, the response to IFNß is partial, i.e.
disease activity is suppressed by only about one third. Clinical
experience suggests that there are IFN ‘responders’ as well as ‘non
responders’, however clear criteria for such classification are still
lacking. In the absence of predictive biomarkers the question
remains who will respond to therapy and who to treat when
inconvenience and costs are significant.
Part of the unresponsiveness to IFNß can be explained by
immunogenicity. However, since not all unresponsive patients
develop neutralizing antibodies (Nabs), and Nabs can disappear
over time,[4–7] other mechanisms have to be involved to explain
unresponsiveness. Hence, there have to be biological disease
mechanisms in a subpopulation of patients that results in
insensitivity or resistance to the effects of IFNs. This implies that
pharmacological responses may differ between patients, leading to
inter-individual differences in clinical efficacy. We hypothesize
that an in depth understanding of the pharmacological factors
underlying the therapeutic mechanisms and therapy unrespon-
siveness is the key for the identification of predictive markers.
In normal physiology type I IFNs achieve their biological effects
by binding to multi-subunit receptors IFNAR-1 and -2 on the cell
surface, thereby initiating a complex cascade of intracellular
secondary messengers that emerge in two divergent pathways.
One pathway, leads to activation of the transcription factor IFN-
stimulated gene factor 3 (ISGF3), a complex of phosphorylated
Signal Transducer and Activator of Transcription (STAT) 2 with
STAT1 and IFN regulatory factor 9 (IRF-9; p48) that binds to the
IFN-stimulated response element (ISRE) present in multiple
genes.[8,9] The other pathway involves STAT2/1 and STAT2/
3 heterodimers and STAT1 homodimer (IFN-a-activated factor,
AAF), which bind to the IFN gamma-activated sequence (GAS)
response element.[9–12] Ultimately, the IFN-induced activation of
ISRE and GAS enhancer elements switch on a wide variety of
genes leading to specific transcriptional changes.
With the aid of genomics technology, we are now in a position
to provide sufficient knowledge to determine pharmacological
outcomes that will allow us to search for predictors of therapeutic
outcomes. Previously we demonstrated that gene expression
signatures in MS may differ significantly between patients.
We found that a subgroup of MS was characterized by an
increased expression of an immune defense response gene set,
including a type I IFN response signature. Here we generated and
analyzed pre- and post- IFNß treatment gene expression patterns
of RRMS patients with the aim of identifying pre-existing and/or
drug-induced signatures that will allow us to make predictions on
the expected pharmacological effects of IFNß treatment. We show
that the expression level of IFN response genes prior to treatment,
could serve a role as biomarker for the pharmacological
differences between patients with MS at the molecular level.
A first group of 16 Dutch patients (10 females and 6 males) and
a second group of 30 Dutch patients (17 females and 13 males)
with clinically definite relapsing-remitting MS was recruited from
the outpatient clinic of the MS Centre Amsterdam. Mean age at
start of IFNß therapy for the test group is 40.667.7, mean EDSS is
2.361.3 (range 1–6). Blood samples were obtained at a fixed time
during the day just before treatment and 1, 3 and 6 months after
start of the therapy. Patients received either Avonex (n=4),
Betaferon (n=7), Rebif 22 (=2) or Rebif 44 (n=3). For the
validation group, mean age at start of IFNß therapy is 34.069.9,
mean EDSS 2.361.1 (range 0–4.5). Patients received either
Avonex (n=7), Betaferon (n=8), Rebif 22 (n=4) or Rebif 44
The study was approved by the ethics committee of the VUmc
and all patients provided written informed consent.
From each patient blood was drawn into one PAXgene tube
(PreAnalytix, GmbH, Germany) and three heparin tubes (Beckton
Dickinson, Alphen a/d Rijn, Netherlands). After blood collection,
tubes were transferred from the clinic to the lab within one hour in
order to isolate fresh peripheral blood mononuclear cells (PBMCs)
from heparinized blood using lymphoprep (Axis-Shield, Lucron)
density gradient centrifugation. PAXgene tubes were stored at
room temperature (RT) for two hours to ensure complete lyses of
all blood cells after which tubes were stored at 220 until RNA
isolation. Total RNA was isolated within 7 months after storage.
Tubes were thawed 2 hours at RT prior to RNA isolation. Next,
RNA was isolated using the PreAnalytix RNA isolation kit
according to the manufacturers’ instructions, including a DNAse
(Qiagen, Venlo, Netherlands) step to remove genomic DNA.
Quantity and purity of the RNA was tested using the Nanodrop
spectrophotometer (Nanodrop Technologies, Wilmington, Dela-
We used 43K cDNA microarrays from the Stanford Functional
Genomics Facility (http://microarray.org/sfgf/) printed on ami-
nosilane-coated slides containing ,20.000 unique genes. First
DNA spots were UV-crosslinked to the slide using 150–300
mJoules. Prior to sample hybridisation, slides were prehybridized
at 42 degrees Celsius for 15 minutes in a solution containing 40%
ultra pure formamide (Invitrogen, Breda, Netherlands), 5% SSC
(Biochemika, Sigma), 0.1% SDS (Fluka Chemie, GmbH, Switser-
land) and 50 mg/ml BSA (Panvera, Madison, USA). After
prehybridization slides were briefly rinsed in MilliQ water,
thoroughly washed in boiling water and 95% ethanol and air-
dried. Sample preparation and microarray hybridisation was
performed as described previously, apart from the different
postprocessing and prehybridization described above.
Data storage and filtering was performed using the Stanford
Microarray Database (http://genome-www5.stanford.edu//)
as described previously. Statistical Analysis of Microarrays
 (SAM) was used to determine significantly differential
expressed genes. A gene was considered as significantly differential
expressed if the False Discovery Rate (FDR) was equal to or less
than 5%. Cluster analysis was used to define clusters of co-
coordinately changed genes after which the data was visualized
Microarray data in this paper are stored in the publicly
accessible Stanford Microarray Database website http://smd.
stanford.edu/which supports the MIAME guidelines. In addition,
data is stored in the Gene Expression Omnibus (GSE10655). The
National Center for Biotechnology Information (www.ncbi.nlm.
nih.gov/) Genbank accession numbers for the genes and gene
products discussed in this paper are listed in Table S1.
RNA (0.5 mg) was reverse transcribed into cDNA using a
Revertaid H-minus cDNA synthesis kit (MBI Fermentas, St. Leon-
Rot, Germany) according to the manufacturers’ instructions.
Quantitative realtime PCR was performed using an ABI Prism
Pharmacogenomics of IFNb
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7900HT Sequence detection system (Applied Biosystems, Foster
City, CA, USA) using SybrGreen (Applied Biosystems). Primers
were designed using Primer Express software and guidelines
(Applied Biosystems) and are listed in table 1. To calculate
arbitrary values of mRNA levels and to correct for differences in
primer efficiencies a standard curve was constructed. Expression
levels of target genes were expressed relative to housekeeping gene
glyceraldehydes-3-phosphate dehydrogenase (GAPDH).
In vitro study
Freshly isolated PBMCs were washed using PBS containing 1%
fetal calf serum (FCS; BioWhittaker, Cambrex) and plated in 24-
wells culture plates at a density of 26106cells per ml per well.
Cells were left unstimulated or activated with 10 Units
recombinant IFNß (Abcam, Cambridge, UK) for 4 h after which
RNA was isolated using the Rneasy Qiagen RNA isolation kit
(Qiagen) according to the manufacturers’ instructions. A DNAse
(Qiagen) step was included to remove genomic DNA. Quantity
and purity of the RNA was tested using the Nanodrop
spectrophotometer (Nanodrop Technologies, Wilmington, Dela-
Correlation analyses were performed using Graphpad Prism 4
software. First, data was tested for normal distribution. For
normally distributed data, a Pearson correlation was used. A
Spearman rank correlation was calculated in case of nonparamet-
ric distribution of the data. Correlations were considered
significant if p-values were less than 0.05.
Pharmacogenomics of IFNß therapy in MS
In order to understand the pharmacological effects of IFNß
therapy we analysed the peripheral blood gene expression profiles
of 16 RRMS patients at baseline and one month after the start of
therapy. Two class paired analysis using Significant Analysis of
Microarrays (SAM) at a False Discovery Rate (FDR) of less than
5% between pre- and post-therapy data was applied to identify
genes that significantly changed in expression after IFNß
treatment. Surprisingly, only 3 genes, ‘‘Interferon alpha-inducible
protein 27’’ (IFI27), ‘‘Tripartite motif-containing 69’’ (TRIM69) and
‘‘Epithelial stromal interaction protein 1 (breast)’’ (EPSTI1), showed a
Given the heterogeneous nature of MS we questioned whether
the observed poor yield of response genes upon IFNß treatment of
the whole MS cohort could be a reflection of averaging out
differences as a consequence of variation in pharmacological
responsiveness between the patients. To test this hypothesis we
investigated the pharmacological response at the individual patient
level by calculating for each patient and for each gene the ratio of
gene expression pre- vs. post therapy (log-2 ratios). We selected
genes that showed at least a two-fold change in expression after
IFNß treatment in at least 7 patients. A total of 126 genes met this
criteria and were subsequently subjected to a two-way hierarchical
(unsupervised) cluster analysis (Figure 1A). Compliant with our
hypothesis, this analysis showed a marked variation in biological
response to IFNß between patients. Some patients showed
upregulated genes, whereas in other patients the same genes were
downregulated or unchanged after IFNß therapy. As anticipated,
part of this gene expression pattern is consistent with expression of
known IFN response genes . We next selected the cluster of
genes showing the most inter-individual variation resulting in 28
IFN-induced genes (Table S1) that clustered tightly together
(R=0.925) indicating a coordinate regulation of these genes
(Figure 1B). The expression data of some of the IFN-induced genes
was validated by quantitative realtime PCR and showed a good
correlation with the microarray data (Table 2). These findings
confirmed the hypothesis that there exists considerable variation in
the pharmacological effects of IFNß between patients with RRMS.
Relationship between pharmacological response and
baseline gene expression levels
Previously, we demonstrated significant differences in the
expression of type I IFN-induced genes between untreated RRMS
patients. Here we investigated whether there is a relationship
between the differential in vivo responsiveness to IFNß and baseline
expression levels of IFN-induced genes. Therefore, we tested for
each patient whether there is an association between the mean
Table 1. Primers used for quantitative realtime PCR
Genes Genbank accession nr.Sense primer Antisense primer Length PCR product (bp)
MxA NM 002462 TTCAGCACCTGATGGCCTATC GTACGTCTGGAGCATGAAGAACTG92
OAS1 NM 016816 TGCGCTCAGCTTCGTACTGAGGTGGAGAACTCGCCCTCTT175
STAT1 NM 007315TGCATCATGGGCTTCATCAGCGAAGTCAGGTTCGCCTCCGTTC 156
RSAD2NM 080657 GTGGTTCCAGAATTATGGTGAGTATTT CCACGGCCAATAAGGACATT90
IRF7NM 004031 GCTCCCCACGCTATACCATCTACGCCAGGGTTCCAGCTTCAC99
ISG15NM 005101 TTTGCCAGTACAGGAGCTTGTGGGGTGATCTGCGCCTTCA 151
Table 2. Correlation between microarray data and realtime
Genesp valueR value
Pharmacogenomics of IFNb
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expression levels of the IFN response gene cluster (shown in
Figure 1B) before therapy with the response ratio after therapy.
This analysis demonstrated that the mean baseline expression of
the 28 IFN response genes negatively correlates with the in vivo
IFN-induced response levels (p=0.0049 and R=20.6657)
(Figure 2A), suggesting that the baseline gene expression level of
Figure 1. A. Biological response to IFNß therapy in MS patients Two-way hierarchical cluster analyses using gene expression ratio’s
(biological response). This diagram contains genes that were at least two-fold up- or downregulated after IFNß therapy in at least seven patients.
Upregulated genes after therapy are indicated by a red colour, downregulated by a green colour and genes that show no differences in expression
after therapy are indicated in black. B. Cluster of IFN-induced genes Selection of genes clustering together based on similar biological response
profiles within the patient group. The genes clustered together with a correlation of 0.925 and are known to be induced by IFN. The mean expression
ratio of all genes in this IFN cluster is referred to as the biological IFN response.
Pharmacogenomics of IFNb
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these genes could serve a role as predictive marker for the
pharmacological responsiveness to IFNß.
In order to create a gene set that best predicts the
pharmacological response to IFNß we selected those genes whose
expression shows the most significant negative correlation between
baseline and biological response (with a cut off of p,0.01 and
R,20.65). This resulted in a gene set containing 15 genes
(Table 3). Comparing baseline gene expression levels and
biological response using the average of these 15 genes revealed
a significant negative correlation (R=20.7208; p=0.0016)
(figure 2B). To exclude a potential bias of the gene selection at
baseline, we analyzed the correlation of the biological response
determined by the mean expression value of the selected 15 IFN-
induced genes with the baseline values of all genes on the array.
This resulted in three additional genes (IFI44L, MT1E and
IMAGE:1879725; R,20.65 and variance .1.00) that significant-
ly correlated with the pharmacological response to IFNß therapy.
Although these genes did not cluster tightly together with the
previously selected genes, they may be important in the response
To investigate whether the observed negative correlation
between baseline and treatment induced changes are stable over
time we measure the expression level of the most significant
correlating gene (RSAD2; see Table 3) again after three and six
months of IFNß therapy. The negative correlation between
baseline expression level and biological response was maintained
after 3 months (p=0.0027,
(p=0.0004, R=20.8154) of therapy (figure 2C and D). To
validate our results, we measured expression levels of RSAD2 in a
second independent group of patients (n=30) before and after
IFNß treatment. In this independent study group we confirmed
the negative correlation between baseline gene expression level
and treatment induced biological response (p,0.0085 and
R=20.7363) and6 months
Comparative analyses of different treatment regimens
Since in the present study different pharmaceutical IFNb
preparations were used for treatment, we wanted to exclude the
possibility of potential differences in pharmacokinetics and
Figure 2. Correlation between baseline and biological response to IFNß therapy. Biological responses were calculated, using a set of IFN-
induced genes (A and B) or a single IFN-induced gene (C and D) and correlated with baseline levels, resulting in a significant negative correlation. In C
and D the expression levels of RSAD2 is measured by quantitative realtime PCR and normalized to the expression levels of GAPDH. A. IFN cluster as
described in Figure 1B; B. Selection of 15 genes; C. Biological response after three months, using RSAD2 gene expression levels; D Biological response
after six months using RSAD2 gene expression levels.
Pharmacogenomics of IFNb
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exposure as an explanation for our findings. Different studies have
indicated no or negligible differences in bioavailability between
different treatment preparations and routes of administration
[19,20]. To exclude a possible bias in our results due to differences
in frequency of injection [20,21] we divided our patients in two
groups based on frequency of injection and compared their
biological responses. One group of patients (group A) consists of
patients with weekly treatment (Avonex) and the other group of
patients (group B) who are treated three to four times a week
(Rebif and Betaferon). Comparison of the response rates between
the treatment groups revealed a similar range of response levels
independent of the treatment regimen for both the test cohort
(group A, n=4 and group B, n=12) based on microarray data,
and the validation cohort (group A n=6 and group B n=24)
based on quantitative PCR data (Figure 3). To provide further
evidence that our results were not influenced by the frequency of
injection we confirmed the negative correlation between the
response rate and baseline IFN response gene expression in the
group of frequently dosed patients (group B: test cohort (n=12),
R=20.8361, p=0.0007; validation cohort (n=24), R=20.4513,
Altogether, these results reveal that the observed negative
correlation between baseline IFN signature and the extent of the
biological response is not biased by the treatment regimen.
Confirmation of ex vivo findings by in vitro IFNß
stimulation of PBMC isolated at baseline
To further confirm that the observed inter-individual pharma-
cological differences were a consequence of differential respon-
siveness of peripheral blood cells and to exclude i. blood sampling
error differences because of possible differential time-intervals
between blood sampling and injection of IFNß, and ii. interference
of inhibitory plasma proteins such as neutralizing antibodies, we
performed an in vitro cell stimulation assay. Therefore we used
purified PBMCs isolated prior to treatment, which were cultured
for 4 hours in the presence of recombinant IFNß. To analyze the
in vitro response to IFNß at baseline we measured the expression of
a selected set of three known IFNß response genes and IFNß itself
in resting and IFNß treated PBMCs by quantitative realtime PCR.
The selected IFNß response genes were i. RSAD2, which showed
the most significant correlation of biological response versus
Figure 3. Comparative analysis between different treatment regimens. Comparison of biological response of Avonex treated patients and
Betaferon or Rebif treated patients. A. Average biological response using the set of 15 IFN-induced genes in the test group of 16 RRMS patients; B.
Biological response using PCR based gene expression levels for RSAD2 in the second independent validation group of 30 RRMS patients.
Table 3. Correlation between baseline and therapy induced
(ratio) expression levels measured at single gene level
Symbol Accession numberp valueR value
Transcribed locusHs.552346 0.0038
IRF7 NM_004031 0.0029
IMAGE:54513859EST AA075776; 39EST
OAS3 NM_006187 0.0076
Transcribed locus Hs.978720.0047
Transcribed locus Hs.125087 0.0108
PARP12 NM_022750 0.0343
Pharmacogenomics of IFNb
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baseline at single gene level (Table 3), ii. MxA, which showed a
good negative correlation and is known as a marker of IFN
bioactivity,  and iii. STAT1, which is one of the components
important for IFNß signaling. We hypothesized that baseline
expression level of these genes influences subsequent IFNß
signaling upon treatment. We compared the in vitro biological
response of these genes to the mean in vivo biological response of
the selected 15 genes. For all genes a significant correlation was
revealed between the in vitro and in vivo biological response
(Table 4). From these results we concluded that the differential
IFNß responsiveness in MS is a consequence of intrinsic
differences of peripheral blood cells in their responsiveness to
IFNß. Moreover, the consistency between the in vivo and in vitro
response to IFNß provides further evidence to exclude the
involvement of different types and dosages of treatment on the
observed pharmacological differences.
Biological IFN response and clinical parameters
The results described above could point towards a method to
predict responsiveness to IFNß therapy based on baseline
expression levels of IFN-induced genes. In the clinic the response
status of a patient is measured by evaluation of Expanded
Disability Status Scale (EDSS) progression, relapse rate and
disease activity on Magnetic Resonance Imaging (MRI). For the
first patient group (n=16) EDSS progression, number of steroid
interventions and relapse rate two years before initiation of
treatment were assessed retrospectively and compared to the first
two years after start of treatment. With this limited set of response
criteria no association with the predictive pharmacological gene
set of 15 IFN induced genes could be observed.
Our results reveal that RRMS patients show a heterogeneous
pharmacological response to IFNß therapy. In some patients we
demonstrate that administered exogenous IFNß induces functional
activation of the IFN pathway, whereas other patients do not
reveal a functional IFNß response. The latter are characterized by
a biomarker profile reflecting a saturated IFN activation pathway
prior to treatment. Hence the baseline expression of the biomarker
profile reflecting the baseline status of the IFN activity negatively
correlates with the pharmacological effects of IFNß treatment.
This indicates that the baseline expression levels of the selected set
of 15 IFN-induced genes can be used as a predictive marker for
the responsiveness to IFNß treatment.
Thus patients with clinically defined similar disease may have
intrinsic different modes of immune status. These findings make
more evident the complexity of the disease and the relationship to
Although different regimens of IFNß treatment were used in this
study evidence is available that this does not affect our conclusions.
Firstly, there is accumulating evidence that there is no or little
difference between different types of IFNb in terms of their
biological activity and routes of administration [19,20]. Extent and
duration of clinical and biologic effects were independent of the
route of administration of IFNb. Rebif when given s.c. or i.m. was
found to be bioequivalent to Avonex [23,24]. Moreover, there
were no major differences between the results with IFNb1a and 1b
in the duration of the changes in the pharmacodynamic markers
after the two routes of injection [25,26].
Secondly, we excluded a possible bias in our results due to
frequency of injection by analyzing different treatment groups
separately. No significant differences in the range of biological
response levels between Avonex treated patients and Rebif or
Betaferon treated patients were observed, and selection of the
high-frequently (Rebif and Betaferon) dosed patients by excluding
weekly–treated (Avonex) patients from our analyses still resulted in
a negative correlation between baseline IFN levels and biological
Thirdly, in the present study we show that the observed negative
correlation between biological response and baseline levels of IFN
induced genes is consistently observed over time, at one, three and
six months after start of the therapy.
Finally, we showed that response-rates of in vitro stimulated
PBMC isolated prior to treatment are consistent with those of the
ex vivo results. These results convincingly supported the conclusion
that the in vivo biological response is independent of differences in
treatment regimens and interfering serum proteins such as
neutralizing antibodies (Nabs).
Hence, we concluded that the inter-individual variation in
pharmacological response to IFNß therapy is an intrinsic property
of the peripheral blood cell compartment.
Several investigators have recently reported on transcription
based responses to IFNß in MS. Baranzini and colleagues 
used a pre-selected set of 70 genes and reported that (un)supervised
two-way hierarchical clustering does not reveal significantly
differential expressed genes between responders and non-respond-
ers. Using quadratic discriminant analysis-based integrated
Bayesian inference system they found a gene triplet consisting of
apoptosis-related genes as best predictive for good responder
versus poor responder classification. Most of the 70 genes they
selected are represented on our microarray but we didn’t observe a
difference for these genes using a gene-by-gene approach.
However, the majority of genes that we found as predictive for
responsiveness using an open survey approach were not present in
the gene set selected by Baranzini and colleagues and therefore not
identified in their study. A careful comparison between the
different IFNb pharmacogenomics studies [28,29] learns that
there is consistency between these reports and our data with
respect to the heterogeneity of the IFNb response. Although not
explicitly mentioned in these reports, we learned that they
contained evidence for inter-individual differences in response to
IFNb. Overall, despite basic differences in the designs, we confirm
and extend the trends observed in these reports with respect to the
heterogeneity in treatment response rates. In addition, our paired
analysis method provides an ideal approach for a patient centric
mode of data analysis and discloses significant differences in the
expression of an IFN driven response gene set at baseline in
relation to the pharmacological response. Our findings provide a
perfect explanation for the inter-individual variation in the
pharmacological responses mentioned above.
Our data based on paired analysis at the individual patient level
clearly show that there is evidence for differences in IFNß
Table 4. Correlation between biological responses of single
IFN-induced genes measured in vitro and mean biological
response (using 15 genes) measured in vivo
Genesp valueR value
RSAD2 0.0012 0.7518
STAT1 0.0100 0.6614
Pharmacogenomics of IFNb
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responsiveness between patients with MS. The inter-individual
differences in IFNß responsiveness may be the result of genetic
variation in the IFNß biology.[2,30] Feng and colleagues 
showed that IFN-induced levels of mRNA and protein for IFN-
regulatory genes (IRF-1 and IRF-2) and antiviral genes (MxA and
29, 59-OAS) were significantly lower in PBMC from patients with
clinically active MS compared to normal controls. They
demonstrated that clinical disease activity was related to decreased
phosphorylation of Ser-STAT-1 and proposed that this could be a
mechanism explaining a defective IFN response. Whereas these
studies provided insight into the IFN responsiveness in terms of a
group average the issue of inter-individual heterogeneity was not
addressed. Other mechanisms that could account for differential
responsiveness to IFNß include variation in activity of inhibitory
transcription factors. Evidence exists that crosstalk with other
cytokine-activated pathways, could cause tachyphylaxis to type I
IFNs. Although type I IFNs have an essential function in
mediating innate immune responses against viruses, they may
already be produced at very low levels in the absence of viral
infections  in serum of a subset of MS patients. Since e.g. IFNa
is known to desensitize further responses to IFNs, the presence of
low endogenous IFNs could block IFNß-induced signals.[33,34]
This explorative pilot study suggests a predictive value of
baseline gene expression levels of IFN-induced genes. Since the
molecular differences most likely reflect distinct pathophysiologic
processes underlying disease, it is tempting to speculate that these
differences will predict individual responsiveness to treatment.
Clinical response to IFNß may be determined by disability
progression and relapse rate. Because MS is a chronic disease with
an unpredictable clinical course it remains difficult to assess
clinical responder status at an individual patient level. A more
objective method for determining disease activity is the measure-
ment of MRI parameters, e.g. CNS atrophy measures or T1
gadolinium enhancing or the appearance of new T2 le-
sions.[3,35,36] However, using these methods it is still extremely
difficult to precisely define the state of responsiveness after a short
period of treatment or preferably before start of the treatment.
These facts emphasize the importance of finding pharmacological
predictors and/or determinants for treatment responsiveness. We
realize that the design of this study does not allow any firm
conclusions to be drawn concerning the clinical parameters
associated with the molecular phenotype.
Hence, further studies in a large cohort of patients starting IFNß
treatment are needed to validate and further investigate the
predictive value of baseline IFN response gene expression levels
and it is of great importance to find a correlation between clinical
parameters and the biological IFN response. In future, molecular
stratification of patients at baseline may be helpful in assembling
homogeneous populations of patients, which will improve the
likelihood of observing drug efficacy in clinical trials.
Found at: doi:10.1371/journal.pone.0001927.s001 (0.05 MB
Gene details for the cluster of 28 genes shown in
The authors gratefully acknowledge the staff and patients of the
department of Neurology at the VUMC hospital who participated in this
study. In addition we would like to thank Lisa van Winsen who at first
recruited the patients for the start of this study.
Conceived and designed the experiments: CV LGMv CP SV. Performed
the experiments: LGMv SV MT JB. Analyzed the data: LGMv SV.
Contributed reagents/materials/analysis tools: LGMv SV. Wrote the
paper: CV LGMv SV. Other: Critically revised the manuscript: CP Tv JK
LFv JB MT. Supervised the statistical analysis and interpretation of the
data: Tv. Collected patient samples: JK LFv JB LGMv. Characterized all
patients clinically: JK LFv. Processed samples for the culture experiments:
LGMv. Initiated and supervised the study: CV. Contributed to the writing
of the paper: CV. Supervised and coordinated this study: CV.
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