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.
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.
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.
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
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 = −0.7208; p = 0.0016). The negative correlation was maintained after three (R = −0.7363; p = 0.0027) and six (R = −0.8154; 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 = −0.4719; p = 0.0085). Moreover, the
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.
Multiple sclerosis (MS) is a common inflammatory disease of the central nervous system characterized by progressive neurological 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 disabled.
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 abnormalities. Moreover, the response to IFNß is partial, i.e. disease activity is suppressed by only about one third.
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,
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.
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.
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 40.6±7.7, mean EDSS is 2.3±1.3 (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 34.0±9.9, mean EDSS 2.3±1.1 (range 0–4.5). Patients received either Avonex (n = 7), Betaferon (n = 8), Rebif 22 (n = 4) or Rebif 44 (n = 11).
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 −20 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, Delaware USA).
We used 43K cDNA microarrays from the Stanford Functional Genomics Facility (
Data storage and filtering was performed using the Stanford Microarray Database
Microarray data in this paper are stored in the publicly accessible Stanford Microarray Database website
RNA (0.5 µg) 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 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
Genes | Genbank accession nr. | Sense primer | Antisense primer | Length PCR product (bp) |
NM 002462 | 92 | |||
NM 016816 | 175 | |||
NM 007315 | 156 | |||
NM 080657 | 90 | |||
NM 004031 | 99 | |||
NM 005101 | 151 | |||
NM 002176 | 93 |
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 2×106 cells 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, Delaware USA)
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 nonparametric distribution of the data. Correlations were considered significant if p-values were less than 0.05.
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, “
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-
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.
Genes | p value | R value |
0.0188 | 0.4335 | |
<0.0001 | 0.6972 | |
<0.0001 | 0.7371 | |
<0.0001 | 0.7086 | |
0.0014 | 0.5648 | |
<0.0001 | 0.7051 |
Previously, we demonstrated significant differences in the expression of type I IFN-induced genes between untreated RRMS patients.
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
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<−0.65). This resulted in a gene set containing 15 genes (
Symbol | Accession number | p value | R value |
NM_080657 | 0.0011 | −0.7983 | |
NM_001548 | 0.0004 | −0.7746 | |
NM_002462 | 0.0006 | −0.7619 | |
NM_005101 | 0.0008 | −0.7532 | |
AI347124 | 0.0026 | −0.7168 | |
NM_001002264 | 0.0059 | −0.7162 | |
Hs.552346 | 0.0038 | −0.6977 | |
NM_004031 | 0.0029 | −0.6925 | |
5′EST AA075776; 3′EST AA075725 | 0.0065 | −0.6881 | |
NM_002346 | 0.0035 | −0.6834 | |
NM_016816 | 0.0051 | −0.6822 | |
NM_006187 | 0.0076 | −0.6787 | |
AA142842 | 0.0087 | −0.6707 | |
NM_000062 | 0.0064 | −0.6688 | |
Hs.97872 | 0.0047 | −0.6677 | |
NM_006820 | 0.0196 | −0.6353 | |
Hs.125087 | 0.0108 | −0.6175 | |
NM_005953 | 0.011 | −0.6166 | |
NM_006074 | 0.0118 | −0.6115 | |
NM_152703 | 0.0121 | −0.6102 | |
NM_002535 | 0.0203 | −0.591 | |
NM_002535 | 0.0169 | −0.5865 | |
NM_024119 | 0.0222 | −0.5842 | |
NM_022750 | 0.0343 | −0.5482 | |
NM_014506 | 0.0532 | −0.4914 | |
NM_001547 | 0.086 | −0.4426 | |
NM_022147 | 0.1759 | −0.369 | |
NM_005567 | 0.314 | −0.279 |
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
Since in the present study different pharmaceutical IFNβ preparations were used for treatment, we wanted to exclude the possibility of potential differences in pharmacokinetics and 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
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.
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.
To further confirm that the observed inter-individual pharmacological differences were a consequence of differential responsiveness 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
Genes | p value | R value |
0.0012 | 0.7518 | |
0.0280 | 0.6064 | |
0.0100 | 0.6614 | |
0.0036 | 0.7675 |
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 therapy responsiveness.
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 IFNβ in terms of their biological activity and routes of administration
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 response rate.
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
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
Our data based on paired analysis at the individual patient level clearly show that there is evidence for differences in IFNß responsiveness between patients with MS. The inter-individual differences in IFNß responsiveness may be the result of genetic variation in the IFNß biology.
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 measurement of MRI parameters, e.g. CNS atrophy measures or T1 gadolinium enhancing or the appearance of new T2 lesions.
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.
Gene details for the cluster of 28 genes shown in
(0.05 MB DOC)
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.