Association between Prediagnostic Allergy-Related Serum Cytokines and Glioma
Association between Prediagnostic Allergy- Related Serum Cytokines and Glioma
Judith Schwartzbaum 0 1
Michal Seweryn 0 1
Christopher Holloman 0 1
Randall Harris 0 1
Samuel K. Handelman 0 1
Grzegorz A. Rempala 0 1
Ruo-Pan Huang 0 1
Brett Burkholder 0 1
Adam Brandemihl 0 1
Henrik Kallberg 0 1
Tom Borge Johannesen 0 1
Anders Ahlbom 0 1
Maria Feychting 0 1
Tom K. Grimsrud 0 1
0 1 Division of Epidemiology, College of Public Health, Ohio State University , Columbus , Ohio, United States of America, 2 Comprehensive Cancer Center, Ohio State University , Columbus , Ohio, United States of America, 3 Division of Biostatistics, College of Public Health, Ohio State University , Columbus , Ohio, United States of America, 4 Mathematical Biosciences Institute, Columbus, Ohio, United States of America, 5 Department of Mathematics, University of Lodz, Lodz, Poland, 6 Department of Statistics, Ohio State University , Columbus , Ohio, United States of America, 7 Center for Pharmacogenetics, Wexner Medical Center, Ohio State University , Columbus , Ohio, United States of America , 8 RayBiotech , Inc., Norcross, Georgia, United States of America , 9 RayBiotech , Inc. Guangzhou, China, 10 Buckeye Psychiatry, Columbus , Ohio, United States of America, 11 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, 12 Department of Registration, Cancer Registry of Norway , Oslo , Norway , 13 Department of Research, Cancer Registry of Norway , Oslo , Norway
1 Editor: Michael Platten, University Hospital of Heidelberg , GERMANY
Allergy is inversely related to glioma risk. To determine whether prediagnostic allergyrelated serum proteins are associated with glioma, we conducted a nested case-control study of seven cytokines (IL4, IL13, IL5, IL6, IL10, IFNG, TGFB2), two soluble cytokine receptors (sIL4RA, sIL13RA2) and three allergy-related transcription factors (FOXP3, STAT3, STAT6) using serum specimens from the Janus Serum Bank Cohort in Oslo, Norway. Blood donors subsequently diagnosed with glioma (n = 487) were matched to controls (n = 487) on age and date of blood draw and sex. We first estimated individual effects of the 12 serum proteins and then interactions between IL4 and IL13 and their receptors using conditional logistic regression. We next tested equality of case-control inter-correlations among the 12 serum proteins. We found that TGFB2 is inversely related to glioblastoma (Odds Ratio (OR) = 0.87, 95% Confidence Interval (CI)) = 0.76, 0.98). In addition, before diagnosis, we observed associations between IL4 (OR = 0.82, 95% CI = 0.66, 1.01), sIL4RA (OR = 0.80, 95% CI = 0.65, 1.00), their interaction (OR = 1.06, 95% CI = 1.01, 1.12) and glioblastoma. This interaction was apparent > 20 years before diagnosis (IL4-sIL4RA OR = 1.20, 95% CI = 1.05, 1.37). Findings for glioma were similar. Case correlations were different from control correlations stratified on time before diagnosis. Five years or less before diagnosis, correlations among case serum proteins were weaker than were those among controls. Our findings suggest that IL4 and sIL4RA reduce glioma risk long before diagnosis and early gliomagenesis affects circulating immune function proteins.
Data Availability Statement: The authors confirm
that some access restrictions apply to the data
underlying the findings. This study is based on data
collected by the Cancer Registry of Norway (a
national disease register) and biological specimens
owned and administered by the Cancer Registry of
Norway. The data on which the present study is
based contain sensitive information and are located
on a security server at the Cancer Registry of Norway
with restricted access. These data are available upon
request to Judith Schwartzbaum (ja.
), the corresponding
author, given that the interested party obtains
Funding: National Cancer Institute, National
Institutes of Health (grant number R01CA122163) to
JS. RayBiotech, Inc and Buckeye Psychiatry
provided support in the form of salaries for authors
(RayBiotech, Inc.: Ruo-Pan Huang, Brett Burkholder;
Buckeye Psychiatry: Adam Brandemihl), but did not
have any additional role in the study design, data
collection and analysis, decision to publish, or
preparation of the manuscript. The specific roles of
these authors are articulated in the ‘author
Competing Interests: Ruo-Pan Huang and Brett
Burkholder are employed by RayBiotech, Inc. and
Adam Brandemihl by Buckeye Psychiatry. There are
no patents, products in development or marketed
products to declare. This does not alter the authors'
adherence to all the PLOS ONE policies on sharing
data and materials, as detailed online in the guide for
Glioma is a heterogeneous primary malignant brain tumor with a median survival time, for the
most common adult subtype, glioblastoma, of only 14 months . The absence of treatment
that insures long term survival and the brief duration of preclinical symptoms, make it essential
that both risk factors for and preclinical evidence of this tumor be identified. Given first, the
inverse association between self-reported allergy, asthma , prediagnostic serum IgE  and
glioma and, second, the fact that glioblastoma-initiating cells inhibit T-cell growth and increase
proliferation of immune suppressive regulatory T cells , we undertook a study of
prediagnostic serum immune function proteins to determine whether they affect glioma risk or would
indicate early gliomagenesis. Such information may eventually allow prevention, earlier
diagnosis or a better understanding of gliomagenesis.
Cytokines control immune reactions related to glioma and its microenvironment. Although
there are no known empirical studies of associations between prediagnostic serum cytokines
and glioma, there are numerous experimental studies of cytokine expression in glioma tissue
and the tumor microenvironment . In addition, there are observational studies of cytokine
levels in the peripheral circulation of glioma patients [6, 7]. We identified seven cytokines from
previous glioma [5–7] or allergy literature [8, 9] (IL4, IL13, IL5, IL6, IL10, IFNG and TGFB2)
to determine whether, they were associated with glioma before diagnosis. We also included two
soluble cytokine receptors (sIL4RA  and sIL13RA2 ) and an exploratory component
consisting of three allergy-related transcription factors (FOXP3 , STAT3  and STAT6
). Although transcription factors are intracellular proteins and therefore would not
normally be found in serum, they may be released into the serum in response to pathological
conditions resulting in cell death [15, 16]. For example, Chaung et al.  report that, in response
to hemorrhagic shock, mitochondrial transcription factor A can be measured in the serum of
Cytokines work in concert [18, 19] therefore their analysis should allow for this synergy [20,
21]. Wu et al.  constructed a complex mathematical model of intercellular signaling
networks in early stage glioma development. Their model predicts initially strong correlations
among cytokines and growth factors in the tumor microenvironment. However, with the onset
of rapid tumor growth, most of these correlations are eliminated. Thus, in addition to
gliomacontrol differences in individual circulating serum protein concentration, we evaluated
correlations among serum proteins. Our aim was to understand whether there is an association
between 12 serum proteins measured before diagnosis and glioma. To achieve this goal, we
conducted a nested case–control study using prospectively collected serum samples from the
Janus Serum Bank in Oslo, Norway.
Materials and Methods
The Janus Serum Bank was established in 1972 to conduct epidemiological studies of cancer
[22–24]. This biobank is now owned by the Cancer Registry of Norway and contains serum
samples from approximately 167,000 men and 158,000 women. Approximately 90% of the
serum donors were participants in routine cardiovascular health examinations conducted by
the National Health Screening Services. In addition to physical examinations, blood was drawn
to evaluate cholesterol and lipid levels. Residual volumes of these samples were stored in the
Janus Serum Bank. The majority of these donors were between ages 35 and 49 years old at the
time of blood donation. In addition, approximately 10% of the serum samples came from male
and female Red Cross Blood Bank donors. Most of these donors were between ages 20 and
65 years old at the time of their blood donation. Samples were stored at −25°C and underwent
one thaw–freeze cycle in preparation for the present study.
The final data set contained no personal identifiers. However, initially, personal identification
numbers were used to link Janus Serum Bank project blood donors to the Cancer Registry of
Norway. We analyzed serum samples from 512 blood donors who were subsequently diagnosed
with glioma (International Classification of Disease, Oncology, Third Edition [ICD-O-3]
morphology codes 9380–9411, 9420–9480, and 9505) between January 1, 1974 and December 31,
2007. However, we subsequently excluded 13 case participants diagnosed with medulloblastoma
or primitive neuroectodermal tumor (ICD-O-3 codes 9470–9474) or pilocytic astrocytoma
(ICD-O-3 code 9421) because of their small number together with differences in age
distributions of these tumors  compared with those of the other glioma participants. Of the
remaining 499 case participants, 12 were excluded because they did not have a matching control leaving
487 cases with glioma, 315 of them with glioblastoma (ICD-O-3 morphology code 9440).
A control participant for each glioma case was randomly selected, according to an incidence
density sampling scheme, from among blood donors. Controls were individually matched to
cases on date of blood collection (±3 months), date of birth (±1 year), county of residence at
blood collection and gender. Matched control participants were required to be alive at the date
of diagnosis of the case to which they were matched and free from any cancer except
non-melanoma skin cancer. In addition, to save valuable serum for use in subsequent biobank studies,
potential controls diagnosed with rare tumors (i.e., all tumors other than breast, prostate, and
colorectal) after the corresponding case’s date of glioma diagnosis were rejected from the
study. Of the 506 control subjects whose serum samples were analyzed 19 were not included in
the study because there was no serum from the corresponding glioma case to which they were
matched, leaving a total of 487 controls.
The research plan on which the present study is based was approved by the Regional Ethics
Committee of Southern Norway and the Norwegian Data Protection Authority. During the
Janus Serum Bank’s first years, 1973–1992, donors gave broad verbal consent for use of samples
in “cancer research” . No samples were collected from 1993 to 1996. Samples from 1997
and later were collected in conjunction with an explicit informed consent document (Act
Relating to Biobanks, § 12, http://ec.europa.eu/research/biosociety/pdf/norwegian_act_
biobanks.pdf). These signed forms are stored either at the Cancer Registry of Norway or the
Norwegian Institute of Public Health. The Norwegian Data Protection Authority (https://
www.datatilsynet.no/English/) has approved of the use of the Janus data and biological samples
collected during the period 1972–2004, while requiring that participants that blood donors are
free to unconditionally withdraw their consent at any time. Upon withdrawal, their serum
samples will be destroyed and associated data deleted (Act Relating to Biobanks, § 14, http://ec.
europa.eu/research/biosociety/pdf/norwegian_act_biobanks.pdf). As additional participant
protection, all research projects using specimens from the Janus repository and data from the
Cancer Registry of Norway need approval from a Regional Committee for Medical and Health
Research Ethics. Donors are informed about ongoing research projects through the Cancer
Registry web pages (http://www.kreftregisteret.no/en/Research/About-our-Research/).
Cytokine array kits, consisting of a combination of two Human Cytokine Antibody Arrays
(G2000, n = 174 and G4000, n = 274) from RayBiotech, Inc. (Norcross, Georgia) were used to
measure 278 serum cytokines, soluble cytokine receptors and transcription factors. These array
kits were mailed to Professor Eivind Hovig's Laboratory at Oslo University, Norway where
serum samples were randomly assigned to print batches. The antibody- based microarray assay
is analogous to a sandwich ELISA assay using two sets of anti-cytokine or transcription factor
antibodies. The hybridized arrays were scanned for fluorescence using the Agilent scanner
G2505C. The scans were obtained with photomultiplier tube settings first at 100% of maximal
intensity. If spots were saturated (meaning reaching the maximum 16 bit gray scale level), this
would lead to loss of linearity of saturated spots. Several spots were saturated and were
rescanned at 30 pmt to prevent spot saturation. Some of these rescans at 30 pmt failed, due to
an attempt to remove high background signals, through a washing procedure. This washing
procedure generated more background. For these, the 100 pmt scan was used. However, this
procedure was applied to few slides, and resulted in a very low level of saturated spots. Tiff
images were made from these scans. The Tiff images were segmented using GenePix 6.0, i.e.
converted from image spots to numerical values of grey scale levels per spot. The GenePix
result files were read into the statistical programming language R. The "F532 Median" column
was chosen as signal without background subtraction, i.e. using median grey level values per
spot. A clear batch effect was observed, probably due to different print batches. Replicate spots
were subsequently averaged. Ninety-five samples were analyzed twice in different batches (one
was analyzed in three different batches) and these values were also averaged.
This article is the first of two analyzing associations among prediagnostic serum protein
levels and glioma. In the present paper we were especially interested in the period near the
time of diagnosis because we wished to determine whether the early tumor affects immune
function serum proteins. However, small samples (e.g., 55 glioma cases, 55 controls) typically
increase the risk of false positive findings . That is, they are more likely to yield statistically
significant results when the null hypothesis actually holds than are large samples. However,
Wacholder et al. show that when associations for which there is prior evidence are tested in
small samples, the probability of false positive findings is reduced. We therefore restricted this
initial analysis to a group of 12 allergy and glioma-related serum proteins which, based on
previous literature [5, 8, 9], have the highest a-priori probability of being associated with glioma.
We first compared the case and control distributions of matching variables (i.e., sex, age and
date of blood collection) by inspection in the total data set and among participants whose
blood was drawn 5 years before diagnosis. In subsequent analyses, controls were assigned
the date of diagnosis of the case to which they were matched.
To evaluate quality control we estimated the median coefficient of variation and the
interquartile range (IQR) of each serum protein that was measured in more than one batch (n = 95)
by case status. Samples were randomly assigned to batches independently of their case status
(which was not known by the laboratory personnel). We used the Chi-Square and Fisher’s
Exact Test to compare the batch distribution by cases and controls.
We next minimized the potential influence of outliers by transforming serum protein values
to a natural log scale and then standardizing them to a mean of zero and standard deviation of
one. If outliers still affected the results, we replaced serum protein values with their ranks.
To determine whether each prediagnostic serum protein was independently associated with
glioblastoma or glioma, we used conditional logistic regression models, conditioned on
matched set or batch and stratified on time before diagnosis (All times, 5, >15 years). In
addition, based on prior knowledge [8, 9], we used separate regression models to evaluate
interactions between IL4 and IL13, the central allergy cytokines and their receptors.
In the remaining analyses we regard the 12 serum proteins as components of a biological
system 21]. We therefore evaluated correlations among them in case-control groups or
matrices stratified on time before diagnosis. To visualize associations among these proteins, we first
graphed separate glioma and glioblastoma case and control Pearson correlation matrices by
time before diagnosis (All times, 5, > 15). (Results using Spearman rank correlations were
similar but are not shown.) We next tested the equality of case and control correlation matrices
by time before diagnosis (All times, 5, > 10, > 15, > 20 years) using the Jennrich test (16).
To find the relative magnitude of case and control correlation coefficients, we added all the
absolute values of case correlation coefficients and did the same for the absolute values of
control correlations. We then subtracted the case sum from the control sum. Next, we created
1000 bootstrap samples for each of the five time categories and averaged their case-control
absolute correlation sum differences. If the sum of absolute values of control correlations was
larger than the sum of case correlations, then the difference of the sums would be positive. If
case correlation coefficients were larger, then the sum difference would be negative.
To identify individual serum proteins that were driving case-control differences, we
calculated absolute differences between case and control correlation coefficients for each serum
protein by time before diagnosis. All analyses were conducted using SAS statistical software,
version 9.3 (SAS Institute Inc, Cary, NC) or the R language and environment (R Core Team
(2013). R: A language and environment for statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. (URL http://www.R-project.org/)).
Results and Discussion
Characteristics of Study Participants
Table 1 shows the success of the matching scheme. Both glioblastoma and glioma cases and
controls are virtually identical with respect to the matching variables (age of and date at blood
draw and sex). Participants were relatively young when their blood was collected (e.g., for total
glioma the median age = 42 years, (IQR = 40, 43 years). Therefore, participants diagnosed with
glioma whose blood was drawn 5 years before diagnosis were predictably younger at
diagnosis (median age at diagnosis = 45 years, IQR = 43, 48 years) than were those in the total sample
(median age at diagnosis = 57 years, IQR = 51, 63 years). The prediagnostic period under
investigation is relatively long with a median time between blood collection and date of diagnosis of
15 years (IQR = 9, 21 years).
Coefficients of Variation in Duplicate Samples
Duplicate samples (47 glioma cases, 48 controls) were measured in different batches and the
median coefficients of variation were calculated for each serum protein. S1 Table shows that all
coefficients of variation were .11 or less except for those for IL5 and sIL13RA. The average of
the coefficient of variation medians over all 12 serum proteins is .09 for cases and the same
value for controls.
Case-Control Distributions by Batch
As a result of random assignment of serum samples to batch, batches were evenly distributed
between all cases and controls S2 Table (Chi-Square (12df) = 1.95; P = 1.00). In addition,
batches were evenly distributed among cases and controls in the subset whose blood samples
were collected 5 years before glioma diagnosis (Fisher’s Exact Test; P = .99). Furthermore,
conditioning the logistic regression models, discussed below, on batch did not affect our
5 Years before Diagnosis 22 73 (53, 93) 42 (41, 46)
5 Years before Diagnosis 55 58 (45, 72) 42 (41, 46)
Samples were not assigned to rescanning at random (See Cytokine Microarray Analysis above),
however, the random assignment of samples to batch resulted in the equal distribution of
rescanned samples among cases and controls (30 glioma cases, 29 controls; Chi-Square (1df) =
0.02, P = .89; 21 glioblastoma cases, 21 controls; Chi-Square = 0.00, P = 1.00). The hypothesis
of equal distribution of rescanned samples among cases and controls was not rejected.
Associations between Individual Cytokine Concentration and
Glioblastoma and Glioma
When all glioblastoma cases (n = 315) and their controls (n = 315) were included in the
analysis, only TGFB2 was statistically significantly associated with glioblastoma (OR = 0.87, 95%
CI = 0.76, 0.98). S1 Fig shows a graph of TGFB2 against time before diagnosis during the ten
years prior to diagnosis. This graph suggests a diminution of TGFB2 concentration among
glioblastoma cases, but not among their matched controls, as the time of diagnosis
Other than TGFB2, none of the 11 serum proteins was independently related to total glioma
or glioblastoma or either tumor within the strata of time before diagnosis. In the context of
these negative results, we must consider the TGFB2 finding with skepticism because it was the
only statistically significant test (P < .05) among the 72 that we conducted (12 serum proteins
tested separately for their association with glioma and glioblastoma for three time periods). We
would expect approximately four of these tests to be false positives; however, we only found
one significant result. In addition, Wu et al. , whose glioma model evaluates prediagnostic
correlations among cytokines, suggest that individual cytokine concentration at one point in
time may not be informative in a dynamic system and may even be misleading. They argue
that in a signaling system it is the interaction among the signaling cytokines that is of central
importance which may not be related to their concentration.
In spite of our doubts of the validity of the TGFB2 finding, it is worthwhile to examine previous
literature on its association with glioma because, as previously noted in the Statistical Methods
Section above, the higher the prior probability of a finding, the less likely it is to be a false
positive. TGFB2 was initially labeled “glioblastoma-T-cell suppressor factor” due to its apparent
role in glioma progression. In fact, modulation of this cytokine has been proposed as a goal of
glioblastoma treatment . However, in an article entitled, “TGF-B: Duality of Function
between Tumor Prevention and Carcinogenesis”, Principe et al.  present evidence of tumor
suppressor activities of TGFB in early stage carcinogenesis.
Interaction between IL4 and sIL4RA
Fig 1 shows a stronger inverse association between the standardized logs of IL4 and sIL4RA
among controls than among cases indicating an interaction by case status. Outliers more than
three standard deviations from the mean were excluded from the graph to avoid their undue
their influence. The case-control interaction is stronger when all values are included (S2 Fig)
however it is not possible to determine which figure (Fig 1 or S2 Fig) accurately represents the
true IL4 sIL4RA association. Therefore, to be conservative, we included all values, as in S2 Fig,
but transformed them to ranks. The data were then stratified on time before diagnosis. In
Table 2, the positive interaction term indicates that IL4’s inverse association with glioblastoma
and glioma is reduced over levels of sIL4RA (and vice versa). That is, the observed negative
effects of IL4 and sIL4RA on these tumors are not as negative as their main effects alone would
suggest. Using the same statistical models as those in Table 2, we found no evidence of
interactions among IL13 and the sIL4RA or sIL13RA2 receptors.
IL4 and sIL4RA Literature
To the extent that IL4 and sIL4RA are components of a complex biological system, the models
in Table 2 are overly simple. Nonetheless, the previous literature may assist in understanding
Fig 1. Association between IL4 and sIL4RA among glioma cases and controls (Observations more than three standard deviations from the mean
are excluded). Graph on left shows the association among glioma controls (n = 482); graph on right shows the association among glioma cases (n = 474).
the implications of the models Table 2. It has been established that sIL4RA inhibits IL4 .
For this reason, it has been proposed that this soluble receptor be used to treat allergic
conditions . Therefore, the positive value of the IL4-sIL4RA interaction term is consistent with
the allergy-glioma hypothesis in that blocking IL4, an important allergy cytokine, increases the
risk of glioma. Unfortunately, the association between this interaction and glioma is more
complex in that sIL4RA activates IL13 , another cytokine central to allergy. Also a problem in
interpreting our models in the context of allergy is that we did not measure expression of
membrane-bound (memIL4RA), a mediator of IL4, which could confound the IL4-sIL4RA
association. That memIL4RA receptor may participate in glioma progression is suggested by
Schwartzbaum et al.’s  finding that expression of the memIL4RA receptor in glioblastoma
tissue is inversely related to a measure of tumor aggression (CD133). Nestor et al  report
that sIL4RA concentration is inversely related to expression of memIL4RA, however, the
interpretation of this result in the context of allergy and glioma would depend on whether IL4 was
bound to sIL4RA. This literature confirms the complex processes underlying a potential
association between IL4, its receptors and glioma risk.
Odds Ratios (95% Confidence Intervals
Time from blood collection to tumor diagnosisb
a. Single rank changes were too small to interpret therefore one unit of IL4 andsIL4RA equals 100 ranks.
b. Controls were assigned the date of diagnosis of the case to which they were matched.
In Fig 2 the intensity of color indicates the strength of the correlations, with red showing
strong positive and blue strong negative correlations. In this figure, 5 years before
diagnosis, control correlation coefficients (bottom graph) are, in general, further from the null than
are case correlation coefficients (top graph). In particular, negative correlations of serum
proteins with sIL4RA are stronger among controls than among cases. In Fig 3, >15 years
before diagnosis, while case-control correlation coefficient differences persist for sIL4RA,
overall case and control correlation differences are smaller than in Fig 2 ( 5 years before
Statistical Tests of Equality of Case and Control Correlation Matrices
All tests of equality of case-control correlation matrices, stratified on five levels of time before
diagnosis, were highly statistically significant (P < .0001) for both glioma and glioblastoma.
However, this statistic was designed to test the equality of correlation matrices and not the
direction of their differences. Furthermore, this result does not provide information as to
whether differences between case-control correlation matrices are primarily attributable to
observed correlations with sIL4RA or a more general inequality.
Fig 2. Glioma Case and Control Serum Protein Pearson Correlation Matrices 5 Years before Diagnosis. The top graph shows case correlations, the
bottom graph shows control correlations. Color scale: blue = negative correlations, green, yellow = moderate correlations, red = positive correlations.
Differences between Case and Control Sums of Absolute Values of
To quantify the apparent visual difference between case and control correlation coefficients
that we observed in Fig 2, we added all case correlations and all control correlations and
compared their sums. We used absolute rather than actual values because we were interested in the
distance of correlations from zero and a correlation of -0.70 is the same distance from zero as a
correlation of 0.70. A positive value of the difference of case from control sums in Table 3
indicates that control correlation coefficients are further from the null and a negative value suggests
that case correlations are further from the null.
Table 3 shows that 5 years before diagnosis, consistent with our visual impression of
Fig 2, control coefficients are further from the null than are case coefficients. More than ten
years before diagnosis, case correlation coefficients appear to be further from the null, however,
these negative differences are small, non-significant and may therefore represent sampling
variation around approximately equal case and control correlation distances from the null.
Paradoxically, while we have indicated that these serum proteins interact and should
therefore be treated as a system, our analysis implicitly assumes independence of individual
correlation coefficients (i.e., the correlations are not confounded by each other nor do they interact).
That is, if correlation coefficients do not represent true associations between variables it may
Fig 3. Glioma Case and Control Serum Protein Pearson Correlation Matrices > 15 Years before Diagnosis. The top graph shows case correlations, the
bottom graph shows control correlations. Color scale: blue = negative correlations, green, yellow = moderate correlations, red = positive correlations.
not be meaningful to add them. The assumption of validity of the correlation coefficients is a
first step in understanding the effects of the preclinical tumor on these serum proteins and is
made for purposes of quantifying our visual impressions of Figs 2 and 3.
Previous Literature on Correlations among Prediagnostic Cytokines and Cancer
Our findings of diminished case correlations 5 years before diagnosis are consistent with
those predicted Wu et al.  who constructed an empirically-based mathematical model of
intercellular signaling in both the microenvironment and tumor cells from early gliomagenesis
to the time of rapid tumor growth. Fifteen cytokines and growth factors were among the
signaling constituents in their model (including IL6, IL10 and TGFB which are also analyzed in the
present study). Their model predicts that correlations among these 15 cytokines disappear as
the tumor initiates rapid growth. This prediction is consistent with our finding of the
weakening of the case cytokine correlation structure prior to tumor diagnosis. However, the timing of
changes in cytokine inter-correlations predicted by their model differs from that we observed.
That is, we find changes in the case cytokine correlation structure 5 years before diagnosis
with the median time being three years before diagnosis (IQR = 1,4 years). It is therefore
unlikely that the tumors in our study have entered a stage of rapid development. However,
Time from blood collection to tumor diagnosisa
Difference between Sums of Absolute
Values of Correlation Coefficients (95%
a. Controls were assigned the date of diagnosis of the case to which they were matched.
b. Difference greater than 0.00 indicates absolute values of control correlations are larger than those of
c. If the 95% confidence interval includes zero then its corresponding p-value is not statistically significant.
d. Difference less than 0.00 indicates absolute values of case correlations are larger than those among
e. Controls are matched to cases within three months of the time of blood draw. Therefore a matched pair
may fall into separate time categories thus accounting for unequal numbers in time category.
although their model is empirically based, not all the initial values for the model parameters
were known. These authors write that these unknown parameters would “only change the
quantitative time line” thus possibly accounting for differences between the time of weakening
of the correlation structure predicted by their model and our results. A further discrepancy
between their model and our study is that they modeled interactions among signaling networks
in the tumor microenvironment and the tumor, while we analyzed cytokines in the peripheral
circulation. In addition, we find a prediagnostic weakening of the correlation structure; they
find that correlations among cytokines disappear. Finally, 12 of the 15 cytokines they included
in their model are not included in the present study.
Also consistent with the prediagnostic weakening of case cytokine correlation structure is
evidence presented by Bartee and McFadden  showing that several types of cancer cells
have lost the ability to induce synergy between the antiviral cytokines TNF and IFNB. In a
review of cytokine synergy and its role in anti-viral immunity  they suggest that escape
from the synergistic effects of cytokines may be a step in carcinogenesis.
Graphs of Absolute Differences between Case and Control Correlations
Next, to facilitate visual comparison of case and control correlation matrices in Figs 2 and 3
and determine whether there are individual serum proteins that may be driving case-control
differences, we graphed the absolute difference between case and control correlations for each
serum protein. For example, if the case correlation was-.70 and the control correlation was .70
the difference would be -1.4, however to indicate this difference is of the same magnitude as
that between case and control correlations of .70 and-.70 we excluded the sign.
Fig 4. Absolute difference between glioma case and control correlation coefficients. Lighter colors
indicate larger absolute differences between case and control correlation coefficients. Top graph represents
glioma cases and controls whose blood was drawn 5 years before diagnosis (n = 55 cases, 55 controls).
Bottom graph represents glioma cases and controls whose blood was drawn > 15 years before diagnosis
(n = 228 cases and 230 controls).
In Fig 4, lighter colors indicate larger absolute differences. The salient feature of this figure
is the number of large case-control differences in the top graph ( 5 years before glioma
diagnosis) compared to those in the bottom graph (>15 years before diagnosis). In addition, > 15
years before diagnosis (bottom graph), case-control differences are largest for correlations
involving sIL4RA (with IL4, IL5, IL10, STAT3 and STAT6) and STAT3 (with IL4, IL13, IL5,
IL6, sIL4RA and sIL13RA2). These patterns are also apparent for glioblastoma (S3 Fig).
The relationship between STAT3 in serum and its function as a transcription factor is
unknown; however intracellular STAT3 has opposing effects on gliomagenesis. While there is
extensive evidence of participation of STAT3 in gliomagenesis and progression [35, 36], this
cytokine may also suppress glioblastoma depending on the mutation profile of the tumor .
In the first known study of associations among prediagnostic serum cytokines and glioma, we
identified inverse associations between TGFB2 and glioblastoma and between IL4, sIL4RA and
both glioma and glioblastoma. Negative associations between IL4 and sIL4RA and these
tumors are slightly reduced by their positive interaction. Both the main effects and their
interaction are statistically significant > 20 years before diagnosis suggesting that they alter tumor
risk. Furthermore, five tests of equality of case and control correlation matrices stratified on
time before diagnosis were rejected. In addition, while the correlation structure of cases
weakens 5 years before diagnosis that of controls does not. More than 15 years before
diagnosis, absolute differences between glioma and control correlations are largest for correlations
with sIL4RA and STAT3. These differences are similar for glioblastoma.
The major limitation of the present study is that the observations closest to the time of
diagnosis ( 5 years), in which we found the largest case-control correlation differences, are based on
a relatively small sample (glioma = 55 matched sets, glioblastoma = 22 matched sets).
Therefore it is possible that our statistically significant findings of case-control correlation differences
are false positives. However, the prediagnostic weakening of the correlation structure is
consistent with predictions of an empirically based mathematical model of gliomagenesis . As
this model predicts, we found a pattern of case-control correlation differences, but only one
serum protein concentration difference. Furthermore, due to interrelationships among the 12
selected serum proteins and the relatively small sample, we cannot identify causal correlations
or networks but rather must consider trends in the correlation matrices as a whole together
with correspondence between our findings and those in the previous literature. Finally, it is
possible that results for serum transcription factors actually represent non-specific binding by
antibodies for FOXP3, STAT3 and STAT6. The validity of associations between these
transcription factors, cytokines and glioma can be examined in subsequent studies.
Blood-brain barrier and serum cytokines
Cytokines regulate local intra- and intercellular immune function and due to their strong
affinities with their receptors are produced in small amounts (picograms per milliliter). It may
therefore seem unlikely that serum cytokines would reflect those produced in the brain during
early stages of gliomagenesis. However, recent research suggests communication between the
brain and the peripheral immune system [38–40]. An example of this interaction is the fact
that endothelial cells that constitute the blood-brain barrier secrete cytokines that are released
into the peripheral circulation . In addition, inflammation in the brain elicits a response
from peripheral cytokines . Whether cytokines that we observed in the prediagnostic
serum are those directly produced by the tumor or its microenvironment or represent systemic
responses to gliomagenesis is not essential for the validity of our study. Rather, one of our goals
was to identify cytokines that may be altered by early glioma development whatever their
Using prior knowledge and examining correlations among 12 serum proteins, we have
identified an interaction between IL4 and sIL4RA and glioma that is present long before tumor
diagnosis and may therefore represent a route by which allergy reduces glioma risk. In addition, we
found weakening of serum protein correlations among cases but not among controls 5 years
before diagnosis. Assuming our findings can be replicated, whether this serum protein pattern
is unique to prediagnostic glioma or can be found before diagnosis in people subsequently
diagnosed with other tumors will be determined by subsequent research.
S1 Fig. Association between TGFB2 and time before diagnosis among glioblastoma cases
and controls. Graph is restricted to ten years before diagnosis. Graph on left shows the
association among glioma controls (n = 72); graph on right shows the association among glioma cases
(n = 73).
S2 Fig. Association between IL4 and sIL4RA among glioma cases and controls (All
observations). Graph on left shows the association among glioma controls (n = 487); graph on right
shows the association among glioma cases (n = 487).
S3 Fig. Absolute difference between glioblastoma case and control correlation coefficients.
Lighter colors indicate larger absolute differences between case and control correlation
coefficients. Top graph represents glioma cases and controls whose blood was drawn 5 years
before diagnosis (n = 22 cases, 22 controls). Bottom graph represents glioma cases and controls
whose blood was drawn > 15 years before diagnosis (n = 167 cases and 169 controls).
Conceived and designed the experiments: JS RPH BB TBJ TKG. Analyzed the data: JS MS CH
SKH GAR HK. Contributed reagents/materials/analysis tools: RPH BB. Wrote the paper: JS
RH AB AA MF.
Oncodevelopmental Biology and Medicine. 2013. Epub 2013/12/19. doi: 10.1007/s13277-013-1514-4
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