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Journal ArticleDOI

Peripheral blood gene expression and IgG glycosylation profiles as markers of tocilizumab treatment in rheumatoid arthritis.

TL;DR: A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented and gene sets discriminating between responders and nonresponders were found and validated.
Abstract: Objective. Tocilizumab, a humanized anti-interleukin-6 receptor monoclonal antibody, has recently been approved as a biological therapy for rheumatoid arthritis (RA) and other diseases. It is not known if there are characteristic changes in gene expression and immunoglobulin G glycosylation during therapy or in response to treatment. Methods. Global gene expression profiles from peripheral blood mononuclear cells of 13 patients with RA and active disease at Week 0 (baseline) and Week 4 following treatment were obtained together with clinical measures, serum cytokine levels using ELISA, and the degree of galactosylation of the IgG N-glycan chains. Gene sets separating responders and nonresponders were tested using canonical variates analysis. This approach also revealed important gene groups and pathways that differentiate responders from nonresponders. Results. Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlated with treatment. Significantly, 4 genes determined responders after correction for multiple testing. Ten of the 12 genes with the most significant changes were validated using real-time quantitative polymerase chain reaction. An increase in the terminal galactose content of N-linked glycans of IgG was observed in responders versus nonresponders, as well as in treated samples versus samples obtained at baseline. Conclusion. As a preliminary report, gene expression changes as a result of tocilizumab therapy in RA were examined, and gene sets discriminating between responders and nonresponders were found and validated. A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented.

Summary (1 min read)

Introduction

  • Significantly, 4 genes determined responders after correction for multiple testing.
  • A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented.

MATERIALS AND METHODS

  • The Research Ethics Committee of University of Debrecen Medical and Health Science Center approved the clinical protocol and the study, which was in compliance with the Helsinki Declaration.
  • Microarray data (Gene Expression Omnibus accession number: GSE25160) were analyzed with Genespring GX10 (Agilent Biotechnologies).
  • The Journal of Rheumatology Copyright © 2012.
  • First, the glycoproteins were denaturated by addition of 5 µl denaturation buffer (New England Biolabs, Ipswich, MA, USA) at 98°C for 10 min. APTS labeling.
  • This method reduces the dimensionality of the data so that the smallest number of artificial and uncorrelated dimensions and principal components explains as much variation as possible12.

RESULTS

  • The authors used a binary outcome variable to assess clinical responder status: patients with ACR0 or ACR20 scores were classified as nonresponders (Patients 1, 2, 12, 13); and patients with ACR50 or ACR70 scores were clas- sified as responders (Patients 3 to 11).
  • These suggest that the clinical characteristics of the patient groups do not allow clear differentiation between various stages of responsiveness to therapy.
  • Microarray analysis of all samples at baseline and Week 4 (n = 26) revealed 59 genes that showed significant differences between baseline and Week 4 after correction for multiple testing.
  • Comparing the ratio of IgG G0 and G1+G1’+G2, which is the ratio of agalactosylated and galactosylated IgG glycans, there was a decrease in responders compared to nonrespon- ders at baseline and after treatment .

DISCUSSION

  • In conclusion, the combination of peripheral blood gene expression analyses, clinical scores, and IgG galactosylation can be used to predict clinical response to tocilizumab thera- py in RA.
  • Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlated with treat- ment.
  • The authors had access to only a relatively small patient group (n = 13), which remains a limitation of their study; therefore these results need further validation on independent, larger sample sets.
  • The normalized mRNA values of B4GALT1 encoding ß1,4-galactosyltransferase, which catalyzes the addition of galactose to human IgG, differentiates between responders and nonresponders at baseline in a statistically significant way, and the degree of galactosylation of the N-glycans of IgG increases significantly after treatment.

ACKNOWLEDGMENT

  • The authors thank Ibolya Fürtos for help in processing samples; Prof. Sandor Sipka for the ELISA experiment and analysis; and Emese Petoné for help with sample collection.
  • Microarray analysis was carried out by the Microarray Core of the Debrecen Clinical Genomics Center.

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916 The Journal of Rheumatology 2012; 39:5;
doi:10.3899/jrheum.110961
Personal non-commercial use only. The Journal of Rheumatology Copyright © 2012. All rights reserved.
Peripheral Blood Gene Expression and IgG
Glycosylation Profiles as Markers of Tocilizumab
Treatment in Rheumatoid Arthritis
BERTALAN MESKO, SZILARD POLISKA, SZILVIA SZAMOSI, ZOLTAN SZEKANECZ, JANOS PODANI,
CSABA VARADI, ANDRAS GUTTMAN, and LASZLO NAGY
ABSTRACT. Objective. Tocilizumab, a humanized anti-interleukin-6 receptor monoclonal antibody, has recently
been approved as a biological therapy for rheumatoid arthritis (RA) and other diseases. It is not known
if there are characteristic changes in gene expression and immunoglobulin G glycosylation during ther-
apy or in response to treatment.
Methods. Global gene expression profiles from peripheral blood mononuclear cells of 13 patients with
RA and active disease at Week 0 (baseline) and Week 4 following treatment were obtained together with
clinical measures, serum cytokine levels using ELISA, and the degree of galactosylation of the IgG
N-glycan chains. Gene sets separating responders and nonresponders were tested using canonical vari-
ates analysis. This approach also revealed important gene groups and pathways that differentiate
responders from nonresponders.
Results. Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlat-
ed with treatment. Significantly, 4 genes determined responders after correction for multiple testing. Ten
of the 12 genes with the most significant changes were validated using real-time quantitative polymerase
chain reaction. An increase in the terminal galactose content of N-linked glycans of IgG was observed in
responders versus nonresponders, as well as in treated samples versus samples obtained at baseline.
Conclusion. As a preliminary report, gene expression changes as a result of tocilizumab therapy in RA
were examined, and gene sets discriminating between responders and nonresponders were found and
validated. A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA
treated with tocilizumab was documented. (First Release April 1 2012; J Rheumatol 2012;39:916–28;
doi:10.3899/jrheum.110961)
Key Indexing Terms:
TOCILIZUMAB GENE EXPRESSION IgG GLYCOSYLATION
DRUG RESPONSE ANTI-INTERLEUKIN-6 RECEPTOR
From the Department of Biochemistry and Molecular Biology, Apoptosis
and Genomics Research Group, Hungarian Academy of Sciences;
Research Center for Molecular Medicine, Medical and Health Sciences
Center, University of Debrecen; Clinical Genomics Center, Medical and
Health Sciences Center, University of Debrecen; Department of
Rheumatology, Institute of Medicine, Medical and Health Sciences Center,
University of Debrecen, Debrecen; and Biological Institute, Eötvös
University, Budapest, Hungary.
Dr. Nagy is an International Scholar of Howard Hughes Medical Institute
and holds a Wellcome Trust Senior Research Fellowship in Biomedical
Sciences. He is supported by grants from the Hungarian Science
Research Fund (OTKA NK72730), the Hungarian Ministry of Health (ETT
294-07), MOLMEDREX (FP7-REGPOT-2008-1. #229920), and
TAMOP-4.2.2/08/2, and TAMOP-4.2.1/B-09/1KONV-2010-0007 imple-
mented through the New Hungary Development Plan co-financed by the
European Social Fund and the European Regional Development Fund.
B. Mesko, MD, Department of Biochemistry and Molecular Biology;
S. Poliska, PhD, Department of Biochemistry and Molecular Biology and
Research Center for Molecular Medicine, Medical and Health Sciences
Center, University of Debrecen; S. Szamosi, MD; Z. Szekanecz, MD, PhD,
Department of Rheumatology, Institute of Medicine, Medical and Health
Sciences Center, University of Debrecen; J. Podani, PhD, Biological
Institute, Eötvös University; C. Varadi, BSc; A. Guttman, PhD, Horváth
Laboratory of Bioseparation Sciences, Medical and Health Sciences
Center, University of Debrecen; L. Nagy, MD, PhD, Department of
Biochemistry and Molecular Biology and Research Center for Molecular
Medicine, Medical and Health Sciences Center, University of Debrecen.
Address correspondence to Dr. L. Nagy. E-mail: nagyl@med.unideb.hu
Accepted for publication December 14, 2011.
Biological therapies brought a new era in the treatment of
rheumatoid arthritis (RA) and other chronic inflammatory dis-
eases. Because their use is expensive, identification of mark-
ers or establishment of scoring systems allowing prediction of
the outcome of treatment and/or disease progression would be
highly desirable.
Besides the many tumor necrosis factor-α (TNF)
inhibitors
1
, other emerging biotherapies such as inhibitors of
the interleukin 1 (IL-1) or IL-6 pathways have also been in
focus recently.
IL-6 can activate cells through binding to mem -
brane-bound (IL-6R) and soluble receptors (sIL-6R), which
has been found to play a key role in acute and chronic inflam-
mation; joint destruction, pannus development, and increased
bone resorption
2
; and inflammatory cell migration
3
. As many
of the articular and systematic manifestations could be
explained by the effect of IL-6, the inhibition of IL-6R rapid-
ly became a validated therapeutic target in RA.
Tocilizumab, a humanized anti-IL-6R monoclonal anti-
body blocking IL-6-mediated signal transduction, in combina-
tion with methotrexate (MTX) is approved as a biological
therapy for moderate to severe RA in adult patients with inad-
equate response to prior disease-modifying antirheumatic
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917
Mesko, et al: Effects of TCZ in RA
drugs (DMARD) or TNF inhibitors
4
or those who do not tol-
erate that therapy. In such cases tocilizumab causes a signifi-
cant reduction in disease activity
5,6
.
There are an increasing number of gene expression studies
focusing on the pathomechanism of RA using either peripher-
al blood mononuclear cells (PBMC)
7
or synovial tissue
8
. As
PBMC are easy to access and analyze and are considered key
cells of inflammation, it is particularly intriguing to assess
whether predicting responsiveness to biological therapies is
possible by the combination of PBMC gene expression pat-
terns and clinical measures. This approach has been proven
successful in other diseases such as breast cancer
9
.
We extended this approach to patients treated with
tocilizumab and performed global gene expression profiling
and scoring of clinical measures using canonical variates
analysis (CVA) to identify gene sets that can differentiate
responders from nonresponders.
N-glycosylation of human immunoglobulins, especially
IgG1, plays a critical role in the bioactivity of this group of
important proteins; and in patients with RA a decrease in ter-
minal galactose content of the N-linked glycans at the con-
served Fc region (Asn 297) glycosylation site of IgG occurs as
compared to a corresponding age-matched control popula-
tion
10
. Interestingly, infliximab, a chimeric monoclonal anti-
body that binds soluble TNF-α, reducing its biological activi-
ty and inflammation, can reduce the concentration of agalac-
tosyl (G0) glycan of IgG1 in patients with active RA who clin-
ically improved according to the American College of
Rheumatology (ACR) criteria following the infliximab/MTX
treatment
11
. It is particularly interesting to determine whether
other biologic therapies such as tocilizumab can produce the
same effect. This and the transcriptomics data obtained on
galactosyl transferase expression and its effects on response to
treatment prompted us to investigate changes in the relative
amount of agalactosyl glycan of IgG1 in RA.
MATERIALS AND METHODS
Patients. The Research Ethics Committee of University of Debrecen Medical
and Health Science Center approved the clinical protocol and the study, which
was in compliance with the Helsinki Declaration. Signed informed consent
was obtained from all individuals who provided blood samples.
Thirteen white patients (9 women, 4 men) who met the ACR criteria for
RA were included in the study; all had active disease at the time of blood draw.
Two additional patients were excluded later in the study due to allergic reac-
tions or elevated liver enzyme levels. After subjects fasted for 12 h overnight,
all blood samples were obtained locally between 8:00 AM and 9:00 AM before
the first administration of tocilizumab at Week 0 (baseline) and the second at
Week 4, and were processed within 1 hour after sample collection.
Details of medications, which remained unchanged during the study, and
comorbidity are shown in Tables 1 and 2. Comedication was given after blood
was taken.
Clinical measures including Disease Activity Score (DAS) were assessed
at the time of the first tocilizumab infusion (baseline), at the second infusion
(Week 4), and at Week 14 when remission was determined based on national
protocols using ACR criteria. Dosage of tocilizumab was 8 mg/kg body
weight per infusion.
The inclusion criteria in our study were (1) fulfillment of the 1987
American Rheumatism Association criteria; (2) receiving concomitant MTX
treatment of maximum 20 mg/wk; (3) age between 30 and 60 years; (4) fail-
ure to respond to at least 2 DMARD; (5) active disease defined as having
DAS evaluated in 28 joints (DAS28) > 3.2; (6) having stable MTX, pred-
nisolone, and nonsteroidal antiinflammatory drug doses during the previous 4
weeks before inclusion in the study; and (7) having discontinued previous
DMARD at least 4 weeks prior to inclusion. Exclusion criteria were (1) preg-
nancy or breastfeeding; (2) current or recent malignancies; (3) active infec-
tious disease; or (4) history of acute inflammatory joint disease of a different
origin. All patients were TNF-blocking therapy-naive.
PBMC and RNA isolation. Venous peripheral blood samples were collected
(10 ml) in vacuum collection tubes containing EDTA (BD Vacutainer
K2EDTA; Becton-Dickinson, Franklin Lakes, NJ, USA) and 10 ml in native
tubes for the extraction of serum. PBMC were separated by Ficoll gradient
centrifugation. Total RNA was extracted from PBMC using Trizol reagent
(Invitrogen, Carlsbad, CA, USA), according to the manufacturers protocol,
on the day of blood sampling. RNA quality was checked on Agilent
Bioanalyser 2100 (Agilent Technologies, Palo Alto, CA, USA), all samples
had a 28S/18S ratio between 1.5 and 2.0 and the RNA integrity number was
between 9 and 10. Quantity was determined by NanoDrop.
Microarray analysis. Affymetrix GeneChip Human Gene 1.0 ST array was
used to analyze global expression pattern of 28,869 well annotated genes.
Ambion WT Expression Kit (Applied Biosystems, Foster City, CA, USA) and
GeneChip WT Terminal Labeling and Control Kit (Affymetrix, Santa Clara,
CA, USA) were used for amplifying and labeling 250 ng of RNA samples.
Samples were hybridized at 45°C for 16 h, and then standard washing proto-
col was performed using GeneChip Fluidics Station 450, and arrays were
scanned on GeneChip Scanner 7G (Affymetrix). CEL files of microarrays
were uploaded to the Gene Expression Omnibus (GSE25160).
Univariate data analysis. Microarray data (Gene Expression Omnibus acces-
sion number: GSE25160) were analyzed with Genespring GX10 (Agilent
Biotechnologies). Affymetrix data files were imported using the Robust
Multi-array Analysis algorithm, and median normalization was performed.
Regarding the baseline versus Week 4 comparison, 26 samples (13 samples at
baseline and 13 at Week 4) were used, and 20% of probe sets with the lowest
expression levels were filtered out in the first step (5733 probe sets filtered
out). Then the list of 23,136 probe sets was filtered by fold change (1.2-fold
cutoff), and statistical analysis was performed using paired Mann-Whitney U
test with Benjamini-Hochberg multiple-testing correction.
Regarding the responder versus nonresponder comparison, 13 samples
(from baseline) were used; 20% of probe sets with the lowest expression
levels were filtered out in the first step (5679 probe sets). Then the list of
23,190 probe sets was filtered by fold change (1.2-fold cutoff) and statistical
analysis was performed using unpaired t test with Benjamini-Hochberg cor-
rection for multiple testing.
Functional categorization of genes was performed with Panther
Classification System (http://www.pantherdb.org/).
Validation by RT-QPCR. Real-time quantitative polymerase chain reaction
(RT-QPCR) was performed to validate a subset of differently expressed tran-
scripts identified by microarray analysis. Individual gene expression assays
(Applied Biosystems) of 12 genes selected for validation were used.
Reactions were performed in an ABI Prism HT 7900 machine (Applied
Biosystems) in triplicate, and all samples (n = 26) were included in the vali-
dation set. Relative gene expression levels were calculated by comparative Ct
method that results in normalizing to GAPDH expression for each sample.
Unpaired and paired t tests were used for statistical analysis (p < 0.05 was
considered significant).
ELISA. Concentrations of IL-6, IL-1ß, and IL-8 in serum were determined
with an ELISA kit (Amersham, UK), and the results were given in pg/ml by
the Regional Immunology Laboratory, Third Department of Internal
Medicine, Medical and Health Science Centre, University of Debrecen.
Measuring decrease in degree of galactosylation of IgG N-glycans. IgG was
isolated from 9 of the 13 samples from patients with RA using protein A affin-
ity pulldown. The N-glycans were released by peptide-N-glycanase F
(PNGase F). The released glycans were then fluorescently labeled with
Personal non-commercial use only. The Journal of Rheumatology Copyright © 2012. All rights reserved.
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918 The Journal of Rheumatology 2012; 39:5;
doi:10.3899/jrheum.110961
Personal non-commercial use only. The Journal of Rheumatology Copyright © 2012. All rights reserved.
aminopyrene-trisulfonate and analyzed by capillary gel electrophoresis with
laser-induced fluorescence detection. The aim of this part of the study was to
investigate the changes in the relative amount of agalactosylated (G0) glycans
before and after the treatment.
Protein A affinity. Protein A is a surface protein originally found in the cell
wall of the bacteria Staphylococcus aureus with the ability to bind
immunoglobulins via their Fc region. In our experiments, Phytip (PhyNexus,
San Jose, CA, USA) columns were used with 20 µl protein A resin bed vol-
ume. In the first step 100 µl serum was dissolved in 200 µl Phynexus protein
A capture buffer (50 mM NaH
2
PO
4
, 0.7 M NaCl, pH 7.4). The IgG molecules
were captured by passing the sample through the resin bed (4 cycles at flow
rate 100 µl/min). During purification steps 500 µl Wash Buffer I (50 mM
NaH
2
PO
4
, 0.7 M NaCl, pH 7.4) was rinsed through the resin bed (1 cycle at
flow rate 250 µl/min) followed by a second wash step with 1000 µl Wash
Buffer II (150 mM NaCl) rinsed through the resin bed (1 cycle at flow rate
250 µl/min). After washing steps, captured IgG was recovered from the pro-
tein A column by rinsing with 150 µl enrichment buffer (200 mM NaH
2
PO
4
,
140 mM NaCl, pH 2.5; 4 cycles at flow rate 100 µl/min). Since the pH of the
elution buffer was 2.5, a buffer exchange was necessary using 10 kDa
Microcon spin filters (Millipore, Billerica, MA, USA).
PNGase F digestion. PNGase F [peptide-N4-(acetyl-ß-glucosaminyl)-aspara -
gine amidase] cleaves asparagine-linked glycan structures from glycopro-
teins. While PNGase F deaminates the asparagine to aspartic acid, it leaves
the released oligosaccharide intact.
First, the glycoproteins were denaturated by addition of 5 µl denaturation
buffer (New England Biolabs, Ipswich, MA, USA) at 98°C for 10 min. After
denaturation, 35 µl HPLC water, 12.5 µl G7 buffer, 12.5 µl NP40, and 10 µl
PNGase F (Prozyme, Hayward, CA, USA) were added to the solution and
digested overnight at 37°C.
APTS labeling. 8-aminopyrene-1,3,6-trisulfonic acid (Beckman Coulter,
Brea, CA, USA) was used as perfect fluorescent dye for all capillary gel elec-
trophoresis (CGE) analysis. The glycans were labeled via reductive amination
by the addition of 1 µl 0.2 M APTS in 15% acetic acid and 1 µl 1 M
NaBH
3
CN. The labeling reaction was incubated overnight at 37°C.
CGE-laser induced fluorescence analysis. For CGE analysis of the labeled
glycans, 60 cm NCHO-coated capillary columns (Beckman Coulter) with 50
µm diameter were used with the ProteomeLab carbohydrate sieving matrix
(Beckman Coulter). All injections were accomplished by 1 psi (pounds per
square inch) for 10 s and the separation voltage was 30 kV.
Data were analyzed using paired and unpaired t tests in GraphPad Prism
(p < 0.05 was considered statistically significant).
Multivariate exploratory analysis. Because the number of patients was 13, the
number of features describing patients was 22, and the number of probe sets
was 28,869, structure hidden in the data could be recovered only by multi-
variate methods.
Principal components analysis (PCA). This method reduces the dimensional-
ity of the data so that the smallest number of artificial and uncorrelated
dimensions and principal components explains as much variation as possi-
ble
12
. The success of variance compression is data-dependent and is measured
Table 1A. Patient measures.
Patient
Characteristic 36 10 45 7 8 911 2 12 13 1
Response at Week 14 RR R RR RR R RNR NR NR NR
ACR response at 70 70 70 50 50 50 50 50 50 20 20 20 0
Week 14
Gender FF F FF FM FFMMMF
Duration of disease, mo 36 480 204 24 192 24 60 156 96 48 156 180 300
Smoking (Brinkman 00 0 00 00 0 0150 375 240 0
index)
DAS28 at Week
0 5.36 5.06 5.2 4.36 5.07 5.21 5.15 6 5.82 7 4.62 5.21 6.42
4 3.33 3.62 2.39 2.53 6 3.75 3.86 4.88 2.8 6.21 3.1 1.21 3.98
14 0.16 1.21 1.64 2.54 2.68 3.2 3.82 3.21 1.97 4 2.72 3.37 5.82
HAQ at Week
0 1.375 2.25 0.75 1.375 2.62 2.125 1 2.75 2.25 1.75 1.75 1.875 0.75
4 1.625 1.125 0.5 0.5 2.125 212.625 1.875 2.25 1.875 1.25 1.5
14 0.25 0.125 0.375 0.625 1.5 1.75 0.5 1.25 2 1.875 1.625 1.25 1.5
CRP at Week
0 8.02 23.2 12.4 4.7 12 0.4 40.25 15.6 7.77 45.4 178.18 2.34 12.5
4 0.5 2.51 0.52 1.5 4 0.6 22.81 8.7 0.5 45.4 161.81 0.5 3.87
14 0.5 0.54 0.9 0.5 0.5 0.5 16.7 5.4 0.5 129.56 59.6 0.5 3.2
ESR at Week
0822 22 14 40 10 50 40 50 23 90 10 38
4282511 6 19 28 2 33 76 26
14 14 4 79 318 4280 20 8 28
Previous DMARD 23 3 54 23 354623
MTX dose, mg/wk 15 15 7.5 20 10 15 12.5 20 15 7.5 15 20 10
Prednisone use, mg/wk 00 0 00 00 0 84040
Comorbidity None HT None None None HT None HT CD None None None HT
Data are shown as mean ± SD regarding the total group; and mean ± range in both responders and nonresponders. ACR: American College of Rheumatology
criteria; R: responder, NR: nonresponder, Brinkman index: no. cigarettes smoked per day × smoking years, calculated by summing separate Brinkman indices
in 3 age periods; DAS28: Disease Activity Score; HAQ: Health Assessment Questionnaire; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate;
DMARD: disease-modifying antirheumatic drug; MTX: methotrexate, HT: hypertension, CD: Crohn disease.
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919
Mesko, et al: Effects of TCZ in RA
by the relative percentages pertaining to each component. Often, 2–3 compo-
nents explain 60%–70% of variation, allowing graphic display of results by
biplot, which is a simultaneous arrangement of study objects and original
variables for a given pair of components. The role of variables in influencing
data structure can be evaluated on the basis of length and directionality of
arrows pointing to variable positions. These are obtained after arbitrary
rescaling of component correlations to allow for effective visualization.
Canonical variates analysis. Whereas PCA recovers underlying structures in
the data without any a priori grouping of objects, separation between predefined
groups of objects is best revealed by CVA. CVA was used to determine whether
the groups of responders and nonresponders are separable in the multidimen-
sional space spanned by the genetic variables, and if so, which gene subsets
have the best discriminatory power. The results of CVA are the so-called canon-
ical scores obtained from the canonical functions derived through eigenanaly-
sis, which serve as coordinates of observations in the canonical space.
Since the maximum number of canonical axes is 1 less than the number
of groups, in our study CVA did not allow graphic display, and separation
of responders and nonresponders is expressed merely by a list of scores for
observations on a single canonical axis. If the observations are taken at ran-
dom and the variables satisfy multivariate normality, then statistical proce-
dures are available to test the significance of group separation.
Nevertheless, if these criteria are not met, as in our case, examination of the
2 groups as to whether they overlap on the canonical axis or not provides
equally meaningful information. A partial limitation of CVA is that the num-
ber of variables cannot exceed the number of observations (patients).
Therefore, many CVA runs were carried out using different subsets of
genes, each subset defined on a logical basis. As a control, we used several
sets of genes selected randomly from a set of genes known to have no influ-
ence on group separation.
Computations were performed using the Syn-Tax 2000 package
13
.
RESULTS
Clinical characteristics. We used a binary outcome variable to
assess clinical responder status: patients with ACR0 or
ACR20 scores were classified as nonresponders (Patients 1, 2,
12, 13); and patients with ACR50 or ACR70 scores were clas-
sified as responders (Patients 3 to 11). Within 4 and 14 weeks
of tocilizumab therapy, disease activity of all patients
decreased significantly when all patients were considered as a
single group (Table 1A and 1B).
Figure 1A shows distribution of the patient population
according to a combination of ACR categories, DAS28
improvement between baseline and Week 14 when responder
status was assessed, and DAS28 at Week 14. PCA of clinical
measures can also differentiate between groups of responders
and nonresponders although there is a clear transition zone,
and results were most influenced by 2 patients (Figure 1B;
Appendix, Supplementary Figure 1). These suggest that the
clinical characteristics of the patient groups do not allow clear
differentiation between various stages of responsiveness to
therapy. ELISA analyses of serum from this group of patients
did not reveal significant differences in IL-6 or IL-8 serum
cytokine levels (Appendix, Supplementary Figure 2), there-
fore these data were not used for further analyses (levels of
IL-1ß could not be detected).
Global gene expression analyses and validation. Microarray
analysis of all samples at baseline and Week 4 (n = 26)
revealed 59 genes that showed significant differences between
baseline and Week 4 after correction for multiple testing. The
list of genes and their functional categories, such as response
to external stimulus, immune system process or regulation of
Table 1B. Patient measures in responders and nonresponders.
Characteristic Total, mean ± SD Responders (range) Nonresponders (range)
Response at Week 14, n 13 94
Gender, F/M 9/4 8/1 1/3
Age, yrs 47.31 ± 8.81 48.2 (37–60) 45.2 (35–53)
Duration of disease, mo 150.46 ± 130.13 141.3 (24–480) 171 (48–300)
Smoking (Brinkman index) 58.85 ± 121.01 0 191.2 (0–375)
DAS28 at baseline 5.42 ± 0.72 5.25 (4.36–6) 5.81 (4.62–7)
Week 4 3.66 ± 1.4 3.68 (2.39–6) 3.62 (1.21–6.21)
Week 14 2.79 ± 1.41 2.27 (0.16–3.82) 3.97 (2.72–5.82)
HAQ at baseline 1.74 ± 0.66 1.83 (1–2.75) 1.53 (0.75–1.875)
Week 4 1.55 ± 0.65 1.48 (0.5–2.62) 1.71 (1.25–2.25)
Week 14 1.12 ± 0.66 0.93 (0.12–2) 0.56 (1.25–1.87)
CRP at baseline 27.9 ± 47.17 13.8 (0.4–40.2) 59.6 (2.34–178)
Week 4 19.47 ± 44.67 4.62 (0.5–22.81) 52.89 (0.5–161.81)
Week 14 16.83 ± 37.6 2.89 (0.5–16.7) 48.21 (0.5–129.56)
ESR at baseline 32.08 ± 22.96 28.4 (8–50) 40.2 (10–90)
Week 4 15.38 ± 20.87 9.22 (2–28) 29.25 (2–76)
Week 14 14.46 ± 21.3 5.77 (1–18) 34 (8–80)
Previous DMARD 3.46 ± 1.27 3.33 (2–5) 3.75 (2–6)
MTX dose, mg/wk 14.04 ± 4.39 14.44 (7.5–20) 13.13 (10–20)
Prednisone use, mg/wk 1.23 ± 2.52 0.89 (0–8) 2 (0–4)
ACR: American College of Rheumatology criteria; R: responder, NR: nonresponder, Brinkman index: no. ciga-
rettes smoked per day × smoking years, calculated by summing separate Brinkman indices in 3 age periods;
DAS28: Disease Activity Score; HAQ: Health Assessment Questionnaire; CRP: C-reactive protein; ESR: ery-
throcyte sedimentation rate; DMARD: disease-modifying antirheumatic drug; MTX: methotrexate.
Personal non-commercial use only. The Journal of Rheumatology Copyright © 2012. All rights reserved.
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920 The Journal of Rheumatology 2012; 39:5;
doi:10.3899/jrheum.110961
Personal non-commercial use only. The Journal of Rheumatology Copyright © 2012. All rights reserved.
Table 2A. Significant differences in gene expression between baseline and Week 4.
Transcript Gene Symbol Localization p Functional Category RA
Cluster ID Associations
(PMID)
8004221 Arachidonate 12-lipoxygenase ALOX12 17p13.1 0.011 Immune system process
8170119 Four and a half LIM domains 1 FHL1 Xq26 0.023 Immune system process
7945262 Junctional adhesion molecule 3 JAM3 11q25 0.028 Immune system process 18821692
8041383 Latent transforming growth factor beta binding protein 1 LTBP1 2p22-p21 0.011 Immune system process 17594488
8016832 Monocyte to macrophage differentiation-associated MMD 17q 0.011 Immune system process
7900683 Myeloproliferative leukemia virus oncogene MPL 1p34 0.011 Immune system process
8157650 Prostaglandin-endoperoxide synthase 1 PTGS1 9q32-q33.3 0.016 Immune system process 16269423
7919984 Selenium binding protein 1 SELENBP1 1q21-q22 0.019 Immune system process
8126269 Triggering receptor expressed on myeloid cells-like 1 TREML1 6p21.1 0.011 Immune system process
8173135 Aminolevulinate. delta-. synthase 2 ALAS2 Xp11.21 0.016 Lipid metabolic process
8115397 Chromosome 5 open reading frame 4 C5orf4 5q31-q32 0.023 Lipid metabolic process
8051583 Cytochrome P450. family 1. subfamily B. polypeptide 1 CYP1B1 2p21 0.019 Lipid metabolic process
8112274 ELOVL family member 7 ELOVL7 5q12.1 0.023 Lipid metabolic process
8050240 Ornithine decarboxylase 1 ODC1 2p25 0.023 Lipid metabolic process
8149927 Clusterin CLU
8p21-p12 0.013 Regulation of apoptosis 17056579
8060745 Spermine oxidase SMOX 20p13 0.011 Regulation of apoptosis
8018864 Suppressor of cytokine signaling 3 SOCS3 17q25.3 0.013 Regulation of apoptosis 18820827
8101762 Synuclein. alpha SNCA 4q21 0.009 Regulation of apoptosis
8123744 Coagulation factor XIII. A1 polypeptide F13A1 6p25.3–p24.3 0.009 Response to external stimulus
8007931 Integrin. beta 3 ITGB3 17q21.32 0.011 Response to external stimulus 15345499
8090162 Integrin. beta 5 ITGB5 3q21.2 0.016 Response to external stimulus
8100966 Platelet factor 4 - CXCL4 PF4 4q12-q21 0.028 Response to external stimulus 2672272
8100971 Chemokine (C-X-C motif) ligand 7 PPBP 4q12-q13 0.023 Response to external stimulus
7922200 Selectin P SELP 1q22-q25 0.009 Response to external stimulus
8136067 Tetraspanin 33 TSPAN33 7q32.1 0.013 Response to external stimulus
7982597 Thrombospondin 1 THBS1
15q15 0.016 Response to external stimulus 19485899
8111772 Disabled homolog 2 DAB2 5p13 0.023 Signal transduction
8096845 Epidermal growth factor (beta-urogastrone) EGF 4q25 0.016 Signal transduction 19680656
8176174 Membrane protein. palmitoylated 1. 55kDa MPP1 Xq28 0.023 Signal transduction
8145736 Neuregulin 1 NRG1 8p21-p12 0.019 Signal transduction
7954293 Phosphodiesterase 3A. cGMP-inhibited PDE3A 12p12 0.028 Signal transduction
8102532 Phosphodiesterase 5A. cGMP-specific PDE5A 4q25-q27 0.023 Signal transduction
8169617 Progesterone receptor membrane component 1 PGRMC1 Xq22-q24 0.028 Signal transduction
7991602 Proprotein convertase subtilisin/kexin type 6 PCSK6 15q26.3 0.005 Signal transduction
8115327 Secreted protein. acidic. cysteine-rich (osteonectin) SPARC 5q31.3-q32 0.009 Signal transduction 15660456
8109093 Actin-binding LIM protein 3 ABLIM3 5q32 0.004 Unclassified or other
7924996 Chromosome 1 open reading frame 198 C1orf198 1q42.2 0.003 Unclassified or other
8118249 Chromosome 6 open reading frame 25 C6orf25 6p21.31 0.013 Unclassified or other
7973403 CKLF-like MARVEL transmembrane domain containing 5 CMTM5 14q11.2 0.023 Unclassified or other
7942204 Cortactin CTTN 11q13 0.009 Unclassified or other
8078650 Carboxy-terminal domain. RNA polymerase II CTDSPL 3p21.3 0.011 Unclassified or other
8145005 Erythrocyte membrane protein band 4.9 (dematin) EPB49 8p21.1 0.009 Unclassified or other
7904361 Family with sequence similarity 46. member C FAM46C 1p12 0.006 Unclassified or other
8117034 Guanosine monophosphate reductase GMPR 6p23 0.003 Unclassified or other
8016044 Integrin. alpha 2b ITGA2B 17q21.32 0.007 Unclassified or other
8166723 Kell blood group precursor (McLeod phenotype) XK Xp21.1 0.005 Unclassified or other
8118235 Lymphocyte antigen 6 complex. locus G6D LY6G6D 6p21.3 0.028 Unclassified or other
8103695 Microfibrillar-associated protein 3-like MFAP3L 4q32.3 0.028 Unclassified or other
8096415 Multimerin 1 MMRN1
4q22 0.009 Unclassified or other
8062312 Myosin. light polypeptide 9. regulatory MYL9 20q11.23 0.002 Unclassified or other
8145766 Neuregulin 1 MST131 8p12 0.023 Unclassified or other
7944876 Neurogranin (protein kinase C substrate. RC3) NRGN 11q24 0.013 Unclassified or other
8089015 Protein S (alpha) PROS1
3p11-q11.2 0.016 Unclassified or other
8071268 Septin 5 SEPT5 22q11.21 0.004 Unclassified or other
8057797 Serum deprivation response SDPR 2q32-q33 0.011 Unclassified or other
8120833 SH3 domain binding glutamic acid-rich protein like 2 SH3BGRL2 6q13-15 0.019 Unclassified or other
7948900 Small nucleolar RNA. C/D box 30 SNORD30 11q13 0.013 Unclassified or other
8156706 Tropomodulin 1 TMOD1 9q22.3 0.011 Unclassified or other
8063716 Tubulin. beta 1 TUBB1 20q13.32 0.009 Unclassified or other
p value after correction; functional categories that genes were assigned to and whether they are associated with rheumatoid arthritis are shown. Underlined
genes play a role in IL-6 signaling according to the Direct Interaction Pathway Analysis of Genespring GX10. PMID: PubMed (US National Library of
Medicine) ID.
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TL;DR: Pharmacogenomics identified SNPs or multiple genetic signatures that may be associated with responses to traditional disease-modifying drugs and biologics.
Abstract: The “Bermuda triangle” of genetics, environment and autoimmunity is involved in the pathogenesis of rheumatoid arthritis (RA). Various aspects of genetic contribution to the etiology, pathogenesis and outcome of RA are discussed in this review. The heritability of RA has been estimated to be about 60 %, while the contribution of HLA to heritability has been estimated to be 11–37 %. Apart from known shared epitope (SE) alleles, such as HLA-DRB1*01 and DRB1*04, other HLA alleles, such as HLA-DRB1*13 and DRB1*15 have been linked to RA susceptibility. A novel SE classification divides SE alleles into S1, S2, S3P and S3D groups, where primarily S2 and S3P groups have been associated with predisposition to seropositive RA. The most relevant non-HLA gene single nucleotide polymorphisms (SNPs) associated with RA include PTPN22, IL23R, TRAF1, CTLA4, IRF5, STAT4, CCR6, PADI4. Large genome-wide association studies (GWAS) have identified more than 30 loci involved in RA pathogenesis. HLA and some non-HLA genes may differentiate between anti-citrullinated protein antibody (ACPA) seropositive and seronegative RA. Genetic susceptibility has also been associated with environmental factors, primarily smoking. Some GWAS studies carried out in rodent models of arthritis have confirmed the role of human genes. For example, in the collagen-induced (CIA) and proteoglycan-induced arthritis (PgIA) models, two important loci — Pgia26/Cia5 and Pgia2/Cia2/Cia3, corresponding the human PTPN22/CD2 and TRAF1/C5 loci, respectively — have been identified. Finally, pharmacogenomics identified SNPs or multiple genetic signatures that may be associated with responses to traditional disease-modifying drugs and biologics.

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Abstract: Background: Glycobiology is an underexplored research area in inflammatory bowel disease (IBD), and glycans are relevant to many etiological mechanisms described in IBD. Alterations in N-glycans attached to the immunoglobulin G (IgG) Fc fragment can affect molecular structure and immunological function. Recent genome-wide association studies reveal pleiotropy between IBD and IgG glycosylation. This study aims to explore IgG glycan changes in ulcerative colitis (UC) and Crohn’s disease (CD). Methods: IgG glycome composition in patients with UC (n ¼ 507), CD (n ¼ 287), and controls (n ¼ 320) was analyzed by ultra performance liquid chromatography. Results: Statistically significant differences in IgG glycome composition between patients with UC or CD, compared with controls, were observed. Both UC and CD were associated with significantly decreased IgG galactosylation (digalactosylation, UC: odds ratio [OR] ¼ 0.71; 95% confidence interval [CI], 0.5–0.9; P ¼ 0.01; CD: OR ¼ 0.41; CI, 0.3–0.6; P ¼ 1.4 · 10 29 ) and significant decrease in the proportion of sialylated structures in CD (OR ¼ 0.46, CI, 0.3–0.6, P ¼ 8.4 · 10 28 ). Logistic regression models incorporating measured IgG glycan traits were able to distinguish UC and CD from controls (UC: P ¼ 2.13 · 10 26 and CD: P ¼ 2.20 · 10 216 ), with receiver–operator characteristic curves demonstrating better performance of the CD model (area under curve [AUC] ¼ 0.77) over the UC model (AUC ¼ 0.72) (P ¼ 0.026). The ratio of the presence to absence of bisecting GlcNAc in monogalactosylated structures was increased in patients with UC undergoing colectomy compared with no colectomy (FDR-adjusted, P ¼ 0.05). Conclusions: The observed differences indicate significantly increased inflammatory potential of IgG in IBD. Changes in IgG glycosylation may contribute to IBD pathogenesis and could alter monoclonal antibody therapeutic efficacy. IgG glycan profiles have translational potential as IBD biomarkers. (Inflamm Bowel Dis 2015;0:1–11)

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Abstract: Gene expression has recently been at the forefront of advance in personalized medicine, notably in the field of cancer and transplantation, providing a rational for a similar approach in rheumatoid arthritis (RA). RA is a prototypic inflammatory autoimmune disease with a poorly understood etiopathogenesis. Inflammation is the main feature of RA; however, many biological processes are involved at different stages of the disease. Gene expression signatures offer management tools to meet the current needs for personalization of RA patient’s care. This review analyses currently available information with respect to RA diagnostic, prognostic and prediction of response to therapy with a view to highlight the abundance of data, whose comparison is often inconclusive due to the mixed use of material source, experimental methodologies and analysis tools, reinforcing the need for harmonization if gene expression signatures are to become a useful clinical tool in personalized medicine for RA patients.

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TL;DR: This narrative review will summarise the literature for the available bDMARD classes and focus on where progress has been made, and look ahead and consider the increasing use of ‘omics’ technologies, the potential they hold as well as the challenges, and what is needed in the future to fully realise the ambition of personalised b DMARD treatment.
Abstract: Individualising biologic disease-modifying anti-rheumatic drugs (bDMARDs) to maximise outcomes and deliver safe and cost-effective care is a key goal in the management of rheumatoid arthritis (RA). Investigation to identify predictive tools of bDMARD response is a highly active and prolific area of research. In addition to clinical phenotyping, cellular and molecular characterisation of synovial tissue and blood in patients with RA, using different technologies, can facilitate predictive testing. This narrative review will summarise the literature for the available bDMARD classes and focus on where progress has been made. We will also look ahead and consider the increasing use of ‘omics’ technologies, the potential they hold as well as the challenges, and what is needed in the future to fully realise our ambition of personalised bDMARD treatment.

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  • ...Another study did not mention the IFN signature but identified increases in the expression of TRAV8-3 (involved in CD8 T-cell response), EPHA4 and CCDC32 and a decrease for DHFR (dihydrofolate reductase, associated with response to MTX) in PBMCs of patients responding to tocilizumab [139]....

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References
More filters
Journal ArticleDOI
31 Jan 2002-Nature
TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
Abstract: Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

9,664 citations

Journal ArticleDOI
TL;DR: The biology of T NF and related family members are discussed in the context of the potential mechanisms of action of TNF antagonists in a variety of immune-mediated inflammatory diseases.

1,517 citations


"Peripheral blood gene expression an..." refers background or methods in this paper

  • ...In our study, we provided 3 pieces of proof of concept evidence to show that (1) gene expression changes associated with tocilizumab therapy can be derived from peripheral blood cells; (2) some baseline gene expression changes, particularly if combined with clinical measures, determine clinical outcome; and (3) a significant increase in the degree of galacto sylation of N-glycans of IgG occurs after tocilizumab therapy....

    [...]

  • ...The inclusion criteria in our study were (1) fulfillment of the 1987 American Rheumatism Association criteria; (2) receiving concomitant MTX treatment of maximum 20 mg/wk; (3) age between 30 and 60 years; (4) failure to respond to at least 2 DMARD; (5) active disease defined as having DAS evaluated in 28 joints (DAS28) > 3....

    [...]

  • ...Exclusion criteria were (1) pregnancy or breastfeeding; (2) current or recent malignancies; (3) active infectious disease; or (4) history of acute inflammatory joint disease of a different origin....

    [...]

Journal ArticleDOI
TL;DR: Tocilizumab could be an effective therapeutic approach in patients with moderate to severe active rheumatoid arthritis.

1,334 citations


"Peripheral blood gene expression an..." refers background in this paper

  • ...2; (6) having stable MTX, prednisolone, and nonsteroidal antiinflammatory drug doses during the previous 4 weeks before inclusion in the study; and (7) having discontinued previous DMARD at least 4 weeks prior to inclusion....

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Journal ArticleDOI
TL;DR: Treatment with MRA was generally well tolerated and significantly reduced the disease activity of RA, and the adverse events were not dose dependent.
Abstract: Objective Interleukin-6 (IL-6) is a pleiotropic cytokine that regulates the immune response, inflammation, and hematopoiesis. Overproduction of IL-6 plays pathologic roles in rheumatoid arthritis (RA), and the blockade of IL-6 may be therapeutically effective for the disease. This study was undertaken to evaluate the safety and efficacy of a humanized anti–IL-6 receptor antibody, MRA, in patients with RA. Methods In a multicenter, double-blind, placebo-controlled trial, 164 patients with refractory RA were randomized to receive either MRA (4 mg/kg body weight or 8 mg/kg body weight) or placebo. MRA was administered intravenously every 4 weeks for a total of 3 months. The clinical responses were measured using the American College of Rheumatology (ACR) criteria. Results Treatment with MRA reduced disease activity in a dose-dependent manner. At 3 months, 78% of patients in the 8-mg group, 57% in the 4-mg group, and 11% in the placebo group achieved at least a 20% improvement in disease activity according to the ACR criteria (an ACR20 response) (P < 0.001 for 8-mg group versus placebo). Forty percent of patients in the 8-mg group and 1.9% in the placebo group achieved an ACR50 response (P < 0.001). The overall incidences of adverse events were 56%, 59%, and 51% in the placebo, 4-mg, and 8-mg groups, respectively, and the adverse events were not dose dependent. A blood cholesterol increase was observed in 44.0% of the patients. Liver function disorders and decreases in white blood cell counts were also observed, but these were mild and transient. There was no increase in antinuclear antibodies or anti-DNA antibodies. Anti-MRA antibodies were detected in 2 patients. Conclusion Treatment with MRA was generally well tolerated and significantly reduced the disease activity of RA.

778 citations


"Peripheral blood gene expression an..." refers methods in this paper

  • ...The inclusion criteria in our study were (1) fulfillment of the 1987 American Rheumatism Association criteria; (2) receiving concomitant MTX treatment of maximum 20 mg/wk; (3) age between 30 and 60 years; (4) failure to respond to at least 2 DMARD; (5) active disease defined as having DAS evaluated in 28 joints (DAS28) > 3....

    [...]

  • ...The inclusion criteria in our study were (1) fulfillment of the 1987 American Rheumatism Association criteria; (2) receiving concomitant MTX treatment of maximum 20 mg/wk; (3) age between 30 and 60 years; (4) failure to respond to at least 2 DMARD; (5) active disease defined as having DAS evaluated in 28 joints (DAS28) > 3.2; (6) having stable MTX, prednisolone, and nonsteroidal antiinflammatory drug doses during the previous 4 weeks before inclusion in the study; and (7) having discontinued previous DMARD at least 4 weeks prior to inclusion....

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Q1. What have the authors contributed in "Peripheral blood gene expression and igg glycosylation profiles as markers of tocilizumab treatment in rheumatoid arthritis" ?

In this paper, gene expression changes as a result of tocilizumab therapy in RA were examined, and gene sets discriminating between responders and nonresponders were found and validated.