Discovering functional relationships between RNA
expression and chemotherapeutic susceptibility
using relevance networks
Atul J. Butte†‡, Pablo Tamayo§, Donna Slonim§, Todd R. Golub§¶, and Isaac S. Kohane†
†Children’s Hospital Informatics Program and Division of Endocrinology, Department of Medicine, Children’s Hospital, 300 Longwood Avenue, Boston, MA
02115;§Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142; and¶Dana-Farber Cancer Institute, 44 Binney Street,
Boston, MA 02115
Communicated by Louis M. Kunkel, Harvard Medical School, Boston, MA, August 16, 2000 (received for review May 1, 2000)
In an effort to find gene regulatory networks and clusters of genes
that affect cancer susceptibility to anticancer agents, we joined a
database with baseline expression levels of 7,245 genes measured
by using microarrays in 60 cancer cell lines, to a database with the
amounts of 5,084 anticancer agents needed to inhibit growth of
those same cell lines. Comprehensive pair-wise correlations were
calculated between gene expression and measures of agent sus-
ceptibility. Associations weaker than a threshold strength were
removed, leaving networks of highly correlated genes and agents
called relevance networks. Hypotheses for potential single-gene
determinants of anticancer agent susceptibility were constructed.
The effect of random chance in the large number of calculations
performed was empirically determined by repeated random per-
mutation testing; only associations stronger than those seen in
multiply permuted data were used in clustering. We discuss the
advantages of this methodology over alternative approaches, such
as phylogenetic-type tree clustering and self-organizing maps.
genomic regulation hidden in the large amounts of data. There
have been four general techniques used to ascertain the func-
tions of genes from expression data. One way is to list genes by
fold-increase or decrease after an intervention. This method has
been used to analyze gene expression patterns in human cancer
and to find inflammatory disease-related genes (1, 2). Although
important, this method typically elucidates only the one regu-
latory network examined.
A second method involves the assignment of each gene to a
multidimensional point with coordinates equal to expression
levels at various time points or experiments. Euclidean distances
between points are calculated, then graphed by using phyloge-
netic-type trees. Related genes are thought to be closer to each
other in the multidimensional space. This technique has been
used to predict functional relationships between genes thought
to be involved in central nervous system development (3, 4). The
third method involves taking the same type of multidimensional
space and constructing self-organizing maps to find clusters of
points (5). However, there are problems with both of these
methods using Euclidean distances, most notably the difficulty in
finding genes negatively associated with each other.
Finally, a fourth method involves phylogenetic-type tree clus-
tering using branch lengths proportional to the correlation
coefficient calculated between gene expression levels (6). This
methodology has been used to hierarchically cluster chemother-
apeutic agents by mechanism of action (7, 8) and to cluster the
with this method is that it clusters genes into a single structure
and pairs each gene with one other, when several regulatory
pathways may be present in biological systems and expressed
genes can participate in more than one pathway.
Our purpose here was to develop a methodology that distin-
guishes true biological associations from noise, generating hy-
ith the increasing availability of RNA expression microar-
rays, the current focus is now on elucidating networks of
potheses of putative functional relationships between pairs of
genes. Specifically, we used baseline RNA expression levels
measured from the NCI60, a set of 60 human cancer cell lines
used by the National Cancer Institute Developmental Thera-
peutics Program to screen anticancer agents since 1989 (8). We
joined the gene expression levels to a database with measures of
RNA expression levels in the cell lines correlated with the
inhibition of growth of these same cell lines to thousands of
anticancer agents. To be clear, RNA expression levels were
measured without any exposure to anticancer agents. As shown
below, this methodology, termed relevance networks, is able to
form clusters without having the problems listed above that are
inherent in other methodologies. A feature of a clustering
technique such as relevance networks, is that it allows us to find
correlations across disparate biological measures, such as RNA
expression and susceptibility to pharmaceuticals.
Gene Expression Data. RNA expression was measured at baseline
in the NCI60 cell lines. Details of the steps needed to measure
RNA expression levels in cells have been described (5). HU6800
arrays (Affymetrix, Santa Clara, CA) were used, containing
probe sets for 6,416 human genes (5,223 known genes and 1,193
expressed sequence tags). The details of the expression data set
and the expression data are available in its entirety at http:??
www.genome.wi.mit.edu?MPR. Because probe sets for some
genes are present more than once on the array, the total number
on the array is 7,245. Affymetrix software was used to calculate
the relative abundance of each gene from the average difference
of intensities between matching and mismatched probe-pairs
designed to hybridize a particular sequence.
Anticancer Agent Susceptibility Data. We used a validated subset of
the National Cancer Institute Human Tumor Cell Line Screen
containing 5,084 anticancer agents tested against the NCI60
panel (7, 10, 11). The amount of growth inhibition compared
with control was measured at several dosages for each chemical
50% growth inhibition, was calculated. For this analysis, suscep-
tibility was expressed as the negative logarithm of GI50.
Although the susceptibility data and baseline RNA expression
data were not measured simultaneously, these were both char-
acteristics, or features, of the same cell lines. The data were
‡To whom reprint requests should be addressed at: Children’s Hospital, 300 Longwood
Avenue, Boston, MA 02115. E-mail: firstname.lastname@example.org.
The publication costs of this article were defrayed in part by page charge payment. This
article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C.
§1734 solely to indicate this fact.
Article published online before print: Proc. Natl. Acad. Sci. USA, 10.1073?pnas.220392197.
Article and publication date are at www.pnas.org?cgi?doi?10.1073?pnas.220392197
October 24, 2000 ?
vol. 97 ?
concatenated together, making a total of 12,329 features mea-
sured on the 60 cell lines, or cases. The National Cancer Institute
data set was not completely comprehensive, in that there were
18,616 missing anticancer agent susceptibility values.
Removing Features with Low Entropy. Each feature in the data set
was first analyzed to ascertain whether it contained a sufficient
range of values. Outlier values in a feature will bias the corre-
lation coefficient when that feature is compared with others;
thus, we desired to minimize spuriously high correlation coeffi-
H ? ? ?
x ? 1
p?x? log2?p?x?? ,
where log2 is base 2 logarithm, and p(x) is the probability a value
was within decile x of that feature. For example, a gene with the
expression amounts 20, 22, 60, 80, and 90 would have deciles 7
units wide, with two values in the first decile, one in the sixth
decile, and one in the ninth and 10th decile, making H ? 1.92.
We excluded from further analysis the 5% of features that had
the lowest entropy (i.e., the least uniformly distributed values)
and were likely to bias the correlation coefficient, even though
this meant we were unable to construct hypotheses with these
features. Of the original 12,329 features, we excluded 544 RNA
measurements and 93 anticancer agents, leaving 6,701 RNA
expression levels and 4,991 measures of anticancer agent sus-
ceptibility. The genes and anticancer agents removed are listed
Relevance Networks. We evaluate the similarity of features by
comprehensively comparing all features with each other in a
pair-wise manner over the same cases. Several similarity metrics
have been previously used in this methodology, including mutual
information (12, 13). In this experiment, we rate the similarity of
patterns of features by using
where abs is the absolute value function and r2is the sample
correlation coefficient. In effect, r ˆis the same as r2, the pair-wise
correlation coefficient around which a large statistical literature
has been built, but retaining the original positive or negative sign
of r. All features are connected to all other features with an r ˆ2.
We hypothesize that features with a high abs(r ˆ2) represent
hypotheses of a biological relationship. We choose a threshold
abs(r ˆ2), then display only the fraction of relationships at or above
that threshold. Groups of features that are connected to each
other with abs(r ˆ2) higher than the threshold will aggregate and
abs(r ˆ2), one can tune the relevance networks to include or exclude
hypothetical relationships. At lower thresholds, the hypotheses
generated may represent novel and true functional relationships,
but also will be harder to distinguish from random noise.
Using this methodology, relevance networks were constructed
from the 11,692 features (baseline expression of 6,701 genes and
measures of susceptibility to 4,991 anticancer agents) in the 60
NCI60 cell lines. There were 68,345,586 pair-wise comparisons
between features, of which roughly 22 million relationships were
between a pair of genes, 12 million relationships were between
two anticancer agents, and 33 million were between a gene and
an anticancer agent.
The distribution of r ˆ2is shown in Fig. 1. Overall, the distri-
bution had a mode at zero and was skewed such that there were
32% more positive correlations than negative ones. Five percent
of associations had abs(r ˆ2) above 0.17. For each gene and
anticancer agent, measurements were randomly permuted 100
independent times. The average distribution of r ˆ2with standard
deviations for these permuted data sets also is plotted in Fig. 1.
Permutation was unable to create any associations with r ˆ2at or
data set with abs(r ˆ2) at 0.80 were reproduced by permutation in
less than 1% of trials and were viewed as highly unlikely to be
generated through random chance (i.e., a signal substantially
stronger than noise). This was used to determine the threshold
abs(r ˆ2), in that the threshold needed to be at or above 0.80 to
maximize the signal strength over noise. We feel this use of
permutation to guide the analysis was critical. For example,
previously published reports on alternative analyses of this data
highlighted associations with r2at 0.55, which is well within the
attainable range through random permutation (14).
With the threshold abs(r ˆ2) set to 0.80, there were 202 con-
structed relevance networks, containing 834 features and 1,222
associations (Fig. 2). The majority of associations were between
pairs of measures of anticancer agent susceptibility. Despite the
large number of associations shown, this represents fewer than
is too small to make out specific details and enlarged versions of all
are available at http:??www.chip.org?genomics. The relevance net-
works were graphically displayed by using nodes to represent genes
and anticancer agents, and links between nodes to represent
hypothetical functional relationships between features. The graph-
ical layout of relevance networks was automatically generated by
using the GRAPH EDITOR TOOLKIT (Tom Sawyer Software, Berke-
ley, CA). Seven specific networks are shown in Table 1.
lines was joined with a database containing the amounts of 5,084 anticancer
agents needed to inhibit growth of those same cell lines. The joined database
contained 12,329 features measured in 60 cell lines. The 637 features that did
not contain a sufficient range of values were removed, using an entropy-
based method described in the text. The remaining 11,692 features were
compared against each other in a pairwise manner making 68,345,586 pairs,
in an effort to find anticancer agent susceptibility patterns and gene expres-
sion patterns that were correlated with each other. The distribution of cor-
negative, of r). For each feature, gene and susceptibility measurements were
randomly permuted 100 times. The average distribution of r ˆ2for each per-
permutation was unable to create an association with r ˆ2at or over 0.80 or
lower than ?0.85.
Butte et al. PNAS ?
October 24, 2000 ?
vol. 97 ?
no. 22 ?
We categorized the associations we found in the 202 networks
into a taxonomy of three types: identity or synonymy, derivation,
and biologic relationship.
Specific Clusters Found Through Analysis of RNA Expression and
Anticancer Agent Susceptibility. Fifteen of the 202 networks dem-
onstrated synonymy-type associations; 10 of these linked the
expression of RNA used as endogenous or spiked controls in the
Affymetrix HU6800 array. Four of these 15 networks linked
genes that were listed under multiple GenBank accession num-
bers: SRP20 (L10838 and D28423), tropomyosin alpha chain
(M19267 and Z24727), small nuclear ribonucleoprotein B
(X17567 and X52979), and nicotinamide N-methyltransferase
(U08021 and U51010). One network linked expression levels of
laminin receptor precursor (M14199) and laminin receptor mRNA
(U43901). These synonymy networks act as a positive control, in
that measurements from similar sets of probe pairs should be
similar, and the expression patterns should be highly correlated.
One hundred seventy eight of the 202 networks linked anti-
cancer agents exclusively, one of which is shown as network 1 in
Table 1. The majority of these associations were between one
anticancer agent and another compound chemically related to or
derived from the first. The larger networks had associations
between many compounds with similar mechanisms of biological
action (for example, the alkylating agents).
The remaining nine of the 202 networks showed associations
of the third type: those suggesting potential biological relation-
ships. Six of these are listed in Table 1 as networks 2–7. One
network (not shown) linked melanoma-associated antigens 2, 3,
and 12. These three genes are expressed in melanoma and
several other malignant tumors and share a high degree of
sequence similarity (15). Another network (not shown) linked
(not shown) linked two related sequences from major histocom-
patibility class I (D32129 and X12432).
In Table 1, network 2 correctly linked keratin 8 and 18, two
intermediate filament proteins. Keratin 18 is a type I (acidic)
keratin and keratin 8 is a type II (neutral?basic) keratin (16),
which are known to be coexpressed and function together to
stabilize each other from degradation (17, 18). Keratins 8 and 18
do not have a significantly similar sequence.
Network 3 negatively linked glycoprotein Ib beta, which is a
component of the platelet receptor for von Willebrand factor,
and psd, which contains Sec7 and pleckstrin homology domains.
early stages of hemostasis and is known to interact with signaling
protein 14–3-3 zeta, which also contains pleckstrin homology
domains (19, 20). This link represents a hypothesis that psd
represents another protein involved in the signaling cascade
from the von Willebrand factor receptor.
Putative Link Between a Single-Gene and Anticancer Agent Suscep-
tibility. At a threshold abs(r ˆ2) of 0.80, only one network contains an
association between a gene expression and a measure of anticancer
agent susceptibility, and this network is labeled 7 in Table 1. The
association is between the gene coding for lymphocyte cytosolic
protein-1 (LCP1, pp65, or L-plastin, UniGene Hs.198260), and the
anticancer agent NSC 624044 (4-thiazolidinecarboxylic acid, 3-[(6-
methyl ester, [1R-[1?(R*),6?]]-(9CI)). LCP1 is an actin-binding
protein involved in leukocyte adhesion (21) whose regulation is
steroid hormone receptor-dependent (22). A specific role for
L-plastin in tumorogenicity has been postulated; low-level expres-
sion of L-plastin is thought to occur in most human cancer cell lines
(23). It is hypothesized that phosphorylation of this protein may
regulate lymphokine-activated killer cell adhesion to tumors (24).
Prostate carcinoma invasion is decreased when levels of L-plastin
are suppressed (25). Expression of T-plastin, a related gene, is
increased in cisplatin-resistant cell lines (26). Although there is no
cell lines to anticancer agents. The pairs of features (anticancer agents in green boxes, genes in white boxes) with r ˆ2at or greater than ? 0.80 were drawn with
linethicknessproportionaltor ˆ2.Featureswithoutanassociationat?0.80wereremoved.Associationswithnegativer ˆ2areinred.Sevennetworksarehighlighted
in orange and are in Table 1. Large versions of all figures and descriptions for each accession number may be found at http:??www.chip.org?genomics.
www.pnas.org Butte et al.
in the biomedical literature, other thiazolidine carboxylic acid
derivatives are known to inhibit tumor cell growth (27).
The GI50 of agent NSC 624044 was found to increase in cells
expressing more LCP1. A scatterplot of the RNA expression of
LCP1 versus the GI50 of cell lines against agent 624044 across
cell lines is shown in Fig. 3; the calculated r ˆ2was 0.83.
Using relevance networks, a gene can be directly or indirectly
linked to several genes as well as phenotypic measurements, such
as measures of anticancer susceptibility. Relevance networks
display nodes with varying degrees of cross-connectivity. In the
extreme, these are cliques, where every node is cross-connected
with every other node in a network. An example is in network
1 in Table 1, where five anticancer agents with similar mecha-
nisms of action were highly cross-connected. These highly cross-
connected networks of nodes represent features that are not only
associated pair-wise, but also in aggregate. They represent the
most trusted associations. Phylogenetic-type trees can only link
each feature to one other feature, typically the one it is most
strongly correlated with, and do not display additional links (6).
In addition, phylogenetic-type trees cannot easily cluster dispar-
ate types of biological measures. Phylogenetic-type trees can be
calculated to cluster genes and anticancer agents separately, but
do not allow one to easily determine the associations between
genes and anticancer agents (14).
Several proposed methodologies for functionally clustering
genes involve calculating the Euclidean distance between clus-
ters of cell states in expression space (3–6). However, clustering
by this metric may ignore genes whose expression levels are
highly negatively correlated across cell lines. During relevance
network construction, negative correlations are discovered and
treated the same as positive ones and are used in clustering.
Because several algorithms now exist to functionally cluster
genes, we felt it important to test the significance of our discovered
associations in a statistical and quantitative manner. Using permu-
tations of the data, we calculated 100 distributions of pair-wise
correlations and were able to highlight only those associations and
clusters that were statistically significant in the original data,
chance. Although hypotheses representing true biological relation-
ships may exist in associations with weaker strength, we felt they
could not be statistically distinguished from random noise. It is
possible that if additional experiments were performed or cell lines
collected to ‘‘exercise’’ the expression space, the strength of these
weaker associations could be enhanced.
The examples listed here show that relevance networks can
successfully cluster baseline gene expression measurements in
cancer cell lines and measures of anticancer agent susceptibility
in those same cell lines. In addition to finding several hypothet-
ical biological relationships between genes and between anti-
cancer agents at the threshold abs(r ˆ2), we found a strong
association between the gene LCP1 and anticancer agent NSC
624044, a thiazolidine carboxylic acid derivative. Other associ-
Table 1. Seven relevance networks of the 202 from Fig. 2
The first network is an example of derivative associations, where several of
the anticancer agents are slightly modified from each other. Networks 2–7
represent those found that demonstrate or potentially contain biological
accession number. Nodes representing measures of susceptibility to a single
anticancer agent are shaded green and have labels starting with P. These
representing the expression levels of a single gene are in white; labels drawn
within each node correspond to the RNA’s GenBank accession code. Specific
genes may be found by using the index at http:??www.ncbi.nlm.nih.gov?
Entrez?nucleotide.html, and anticancer agents may be found by NSC number
by using http:??dtp.nci.nih.gov?docs?dtp_search.html.
Butte et al. PNAS ?
October 24, 2000 ?
vol. 97 ?
no. 22 ?
ations between baseline expression levels gene and anticancer
agent susceptibilities can be found by setting the threshold
strength lower. However, it is important to note that doing so
would have increased the likelihood of finding a spurious
association as demonstrated by permutation analysis. We feel
computing analyses of permuted data should become a mini-
mum standard for testing the statistical significant of a clustering
Given the paucity of ‘‘correct answers’’ in the literature and
the poor annotations in human genome databases, it is difficult
to fully evaluate the generated hypotheses without performing
the necessary specific biologic experiments. Improving annota-
tions in human genome databases eventually will allow for an
automated testing of a hypothesized functional relationship
against known information in the biomedical literature.
One limitation in this methodology is that we restrict the com-
prehensive pairwise comparisons to only those features that dem-
onstrate a sufficient distribution of values across their dynamic
range. Spikes, or outlying values in a nonuniform distribution, may
be an indicator of the true biological range of a feature, such as a
gene that acts as a step function (with only low or high measure-
or anticancer agent truly acts uniquely in a single cell line. Because
we arbitrarily exclude 5% of features, this means that we did not
generate hypotheses that used all of the collected features. We may
have missed reporting a valid hypothesis, while trying to avoid
reporting false-positive hypotheses.
A second limitation in the analysis is that there is no modeling
detected on an oligonucleotide microarray is under analysis, and
noise may be larger at lower expression values. We currently use
raw expression values to compute correlation coefficients. Ide-
ally, a correlation coefficient computed from low expression
values should have a wider confidence interval than one con-
structed from higher, more accurate expression values. One way
to address these issues is to compute the cross-entropy, or the
amount of information gained about the pattern of one feature
given another, instead of correlation coefficient (13).
There are several directions of research indicated. First, cases
that violate the model association between two features may
represent important exceptions that should be studied. Second,
the analysis can be expanded more broadly to include clinical
features, so that these may be associated with RNA expression
patterns. Finally, the specific hypotheses linking genes to each
other and to measures of anticancer agent susceptibility need to
be tested, with the promise of discovering potentially new
pretherapy markers and drug-resistance genes that could help
suggest specific chemotherapeutic agents to use in patients.
We thank Jae Kim, Uwe Scherf, and John Weinstein at the National
Cancer Institute for providing the GI50 data and cell line RNA. We
thank Michael Angelo for his suggestions and Johnny Park and Hilary
Coller for generating the expression data. This research was supported
in part by the grant ‘‘Research Training in Health Informatics’’ funded
by the National Library of Medicine, 5T15 LM07092–07 and R01
LM06587–01, and by grants from Bristol-Myers Squibb, Millennium
Pharmaceuticals, and Affymetrix.
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anticancer agent susceptibility was between lymphocyte cytosolic protein-1
(LCP1) and anticancer agent NSC 624044, a thiazolidine carboxylic acid deriv-
ative. Here, amount of LCP1 expression is plotted against the GI50 of the
with r ˆ2of 0.83.
The highest r ˆ2between a baseline gene expression and measure of
www.pnas.orgButte et al.