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A review of connectivity map and computational approaches in pharmacogenomics

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Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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A review of connectivity map and computational
approaches in pharmacogenomics
Aliyu Musa, Laleh Soltan Ghoraie, Shu-Dong Zhang, Galina Galzko,
Olli Yli-Harja, Matthias Dehmer, Benjamin Haibe-Kains and
Frank Emmert-Streib
Corresponding author: Frank Emmert-Streib, Department of Signal Processing, Predictive Medicine and Data Analytics Laboratory, Tampere University of
Technology, Korkeakoulunkatu 1, FI-33720 Tampere, Finland. Tel.: 00358 50301 5353; E-mail: frank.emmert-streib@tut.fi
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular
Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this
is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap
and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a re-
view of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
Key words: pharmacogenomics;drug discovery;bioinformatics;drug repurposing;drug repositioning;big data
Introduction
Recently, there is an increasing interest in the computational
analysis of drug perturbation data sets. Such data types are now
routinely used to aid our understanding in drug discovery and
disease therapeutics [1,2]. With the rapid accumulation of gen-
omics and chemical informatics data in the past decade, several
new systematic approaches to drug discovery have been pro-
posed. For example, some study the drug–target structural
relationships for specific drugs to discover new targets impli-
cated in diseases, whereas others predict biochemical inter-
actions of small molecules with their respective targets using,
e.g. the Connectivity Map (CMap) approach [35]. However, for
either type of investigations, machine learning [6] and biomed-
ical text mining [7] approaches have been vital to uncover hid-
den relationships between drugs and potential new indications.
Overall, applying these methods on drug perturbation data sets
Aliyu Musa is a PhD Student at Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology. His
research focuses on ‘Big Data’ analysis for drug discovery and cancer therapeutics.
Laleh Soltan Ghoraie is a Postdoctoral Research Fellow at Princess Margaret Cancer Centre, University Health Network. She is interested in applications of
Machine Learning in Bioinformatics.
Shu-Dong Zhang is a Senior Lecturer in Stratified Medicine (Statistics/Bioinformatics) at Northern Ireland Centre for Stratified Medicine, University of
Ulster. His research focuses on the analysis of large-scale gene expression profiling data for drugs and diseases, and their applications in biomarker dis-
covery for stratified medicine and drug repurposing.
Galina Glazko is assistant professor of Biostatistics and Computational Biology at Department of Biomedical Informatics, University of Arkansas for
Medical Sciences. Her research focuses mainly on computational biology and biostatistics and their application in gene regulatory networks.
Olli Yli-Harja is Professor at Tampere University of Technology Department of Signal Processing. He has been involved in development of computational
tools and software for systems biology using advanced methods of signal processing and statistics.
Matthias Dehmer is Professor at UMIT, Department for Biomedical Computer Science and Mechatronics. He is interested in Graph Theory, Data Science,
Data Analysis, Big Data, Complex Networks and Machine Learning.
Benjamin Haibe-Kains is Scientist at the Princess Margaret Cancer Centre, University Health Network and Assistant Professor at the University of Toronto.
His research focuses on the development and application of machine learning algorithms to analyze high-throughput genomic data in biomedicine,
mostly in cancer studies.
Frank Emmert-Streib is Associate Professor in the Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of
Technology. His research interests lie in computational biology, predictive analytics and data science.
V
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Briefings in Bioinformatics, 2017, 1–18
doi: 10.1093/bib/bbw112
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has proven to be beneficial in enhancing the understanding of
the connection between genes, drugs and diseases [810] be-
cause such methodologies can lead to generation of novel
hypotheses beyond classical pharmacology by translating new
knowledge from genomic in vitro screens and cell-based assays
to the patients.
Computational screening of drugs has been greatly facili-
tated by the advent of connectivity mapping methods, specific-
ally CMap and the Library of Integrated Network-based Cellular
Signatures (LINCS) [3,11]. CMap and LINCS are comprehensive,
large-scale drug perturbation databases containing transcrip-
tomic profiles of dozens of cultivated cell lines treated with
thousands of chemical compounds serving as reference data-
bases. That means, these ‘big data’ resources provide simple yet
important platforms to characterize ‘signatures’ of gene expres-
sion changes induced by small molecules. Such drug perturb-
ation signatures have been used to determine connections,
similarities or dissimilarities among diseases, drugs, genes and
pathways, but we are far from fully understanding their
capabilities.
The purpose of this article is to provide a state-of-the art sur-
vey of recent advances in CMap studies and related methods
used in drug discovery, as well as reviewing computational
tools that have been applied in the field. Furthermore, we dis-
cuss examples of applications of these methodologies being
currently used both in drug repurposing/repositioning and in
drug discovery process. An earlier review of connectivity map-
ping has been provided by Qu et al. [12], neglecting, however,
methodological developments. A complementary presentation
has been given in [13] focusing on publicly available resources
and databases that can be used for generic genomic investiga-
tions of disorders.
Put simply, the goal of the CMap in genomic drug discovery
studies is to identify disease or drug-associated gene signatures
that correlate with perturbations on the transcriptomics level as
a response to administrated small molecules or drugs [14]. It is a
common approach used to identify inverse drug–disease rela-
tionships by comparing disease molecular features and drug
molecular features, such as gene expression. This approach
starts by generating a disease gene expression signature by
comparing disease samples and normal tissue samples, fol-
lowed by querying drug–gene expression reference databases.
This makes the CMap technique effective and widely popular in
drug discovery, posing a primary advantage, as it does not re-
quire a detailed mechanism of action (MoA) or prior knowledge
of drug targets to work [15]. However, CMap comes with some
limitations, such as limited drug perturbation data, a limited
drug coverage, dosage-dependent conditions and the uncer-
tainty of applying cell lines or animal model expression pat-
terns to human systems. Also, the methodology can be
expensive and time-consuming before it can generate a signifi-
cant portion of all safe dosage conditions for a limited number
of cell lines for CMap [12].
The connectivity mapping methods
CMap: the connectivity map
The connectivity map was introduced by Lamb et al. [3] in 2006.
The basic concept of CMap is to use a reference database con-
taining drug-specific gene expression profiles and compare it
with a disease-specific gene signature. The CMap method is
performed by simply submitting a list of genes thought to be
relevant to a particular disease. A researcher is returned a list of
drugs having either presumptive efficacy for the disease or,
more realistically, whole mechanisms of action that are well
known, thereby enhancing biological understanding of the dis-
ease. This allows identifying connections between drugs, genes
and diseases. The overall goal of CMap is to predict potentially
therapeutic drug candidates.
The principal workflow of CMap is shown in Figure 1.A
phenotype of interest such as a disease or biological condition
is described by a gene expression signature, i.e. a set of genes
that uniquely represents the underlying phenotype. In [3], the
gene signature corresponds to a list of differentially expressed
genes (DEG), named h, that contains up- and downregulated
genes as shown Figure 1A.
The gene signature set is then used to query the CMap cata-
log of gene expression profiles. The CMap database is a collec-
tion of paired gene expression profiles representing a series of
structured microarray experiments. All experiments were con-
ducted using a microarray platform (Affymatrix HT_HG_U133A
array with 22 283 probesets in addition to HG_U133A with
22 277 probesets) and standardized preprocessing (MAS 5.0).
The experiments were carried out in various cell lines to pertur-
bagens (drugs and bioactive small molecules) at varying con-
centrations and time points against vehicle controls. The initial
database (Build 1) contained 455 instances, i.e. treatment-
control pairs, where treatment constitutes a selection of 165
drugs, 42 different concentrations, 2 time points and 5 cell lines.
The updated version (Build 2) contains 6100 instances with
more drugs (1309) and concentration (156) but the same cell
lines, for a parallel series of analysis. The instance is the basic
unit of data and metadata in CMap. Each instance is uniquely
identified by an instance identifier. After preprocessing, the re-
sulting probe-level summaries are subject to further analysis
(scaling treatment values to corresponding vehicle controls,
thresholding, etc.). The fold change of treatment to control val-
ues was calculated for each probeset, sorted into decreasing
order and converted to a rank vector, separately for each in-
stance. Thus, the probeset that is most upregulated will receive
Rank 1 and the most downregulated will receive 22 283. So, for
Build 2, the CMap database is n¼22;283 p¼6100 matrix. The
instance rankings are used to compare query lists. It is import-
ant to note that while these rankings may be perceived as a
crude form of summarization, the absence or sparsity of treat-
ment replication precludes usage of summaries incorporating
variation. Hence, for every drug, there is an instance representa-
tion in the reference database, corresponding to the treatment
and the control condition.
The gene signature, h, is compared with the ranked probesets
of the treatment versus control gene expression profiles that are
ranked in descending order according to the fold changes of the
probesets. By splitting the gene signature, h, into two lists con-
taining only upregulated genes, h", and downregulated genes,
h#, a so-called connectivity score is estimated via several auxil-
iary variables using a nonparametric rank-ordered Kolmogorov–
Smirnov (KS) test, similar to the method introduced in [16].
The resultant ‘connectivity score’ is normalized using ran-
dom permutation described in [3] by Lamb et. al., assuming val-
ues from 1to þ1 to reflect the closeness or connection
between the expression profiles. A positive connectivity score is
obtained for having most of the downregulated genes at the top
of the reference profile and most of the upregulated genes at
the bottom (Figure 1B). In contrast, a negative connectivity score
is obtained for a reversed mapping, meaning that most of the
upregulated genes are at the bottom of the reference profile and
most of the downregulated genes are at the top [17]. A positive
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correlation denotes the degree of similarity and a negative cor-
relation emphasizes an inverse similarity between a query sig-
nature and a reference profile derived from an individual
chemical perturbation; thus, implicating the exposure to a par-
ticular chemical can mimic or reverse the expression pattern of
the biological state of interest. A null connectivity score occurs
when the up- and downregulated genes are randomly distrib-
uted over the reference profile. See Figure 1B for a visualization
of the different cases. Overall, the results are obtained as a list
of connectivity scores for all small molecules in the reference
database, one connectivity score for each small molecule.
Finally, the top-scoring drugs are selected by sorting all con-
nectivity scores in descending order and identifying a relevance
threshold (Figure 1C). Unfortunately, in [3], no measure of statis-
tical significance, via a statistical hypothesis test, has been used
formally. In contrast, only a basic approach has been suggested
involving a resampling procedure.
Since the first introduction of the CMap principle and meth-
odology, there have been numerous applications of this ap-
proach by many research groups with a particular focus in drug
discovery and development. Therefore, the CMap approach can
be used as a method of screening chemicals by matching the
gene signature of a novel pertubagen against the reference pro-
file [18,19]. The chemicals sharing similar gene expression pat-
tern, similar activities or mechanisms can be retrieved. A highly
representative phenotype-specific gene signature set of a given
biological state; pathological, genomic perturbations or induced
by chemicals is seen as the first step of implementing CMap
technique. The signature can be generated through a computa-
tional analysis using the genome-wide gene expression profiles.
Although there is no precise way of creating optimal gene signa-
tures, the conventional approach is to identify and use the DEG
that are statistically significant displaying an association with a
given phenotype.
Figure 1. Mechanistic overview of the working principle of the CMap method and the CMap database for drug discovery.
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Reference drug perturbation databases and data sets
There are a few valuable databases and data sets containing
gene expression response profiles effected by chemical com-
pounds that are publicly available. Hence, these data provide in-
formation about the perturbation effects that drugs have on the
transcriptomics level of a cell. In Table 1, we provide an
overview of the most important generic resources. However, we
would like to note that there are additional disease-specific re-
sources available, e.g. for cancer [20], that provide also disease-
relevant relationships with drug compounds and targets.
Henceforth, we focus on the two largest general purpose drug
perturbation data sets CMap and LINCS L1000.
Table 1. An overview of generic drug perturbation databases and data sets
Database/
data set
Description URL link
CMap [3] A database of genome-wide gene expression profiles produced on treatment
of 564 gene expression profiles generated for five cancer cell lines (Build 1).
The current version consists of 1309 compounds and 7;000 gene expres-
sion profiles (Build 2).
https://www.broadinstitute.org/CMap/
LINCS L1000
[11]
The Library of Integrated Cellular Signatures (LINCS) is an NIH program,
which funds the generation of perturbation profiles across multiple cell
and perturbation types, as well as readouts, at a massive scale. The data
consist of 20000 perturbagens, 15 cell lines, 1;400;000 gene expression
profiles and 25 assays.
http://www.lincsproject.org/
DP14 and
DP92 [21]
The DP14 data set contains GEPs of OCI-LY3 cell line (a human diffuse large
B-cell lymphoma cell line) treated with 14 distinct individual compounds
and profiled at 6, 12 and 24 h following compound treatment, all in tripli-
cate. For treatment, two different concentrations of the compounds corres-
ponding to IC20 at 24 h and IC20 at 48 h were used. GEP of DMSO-treated
samples and profiled at the three different time points, all in octuplicate
were used as control, resulting in 276 GEPs from this data set. DP92 data set
contains GEPs of 92 distinct FDA-approved, late-stage experimental and
tool compounds in three different B-cell lymphoma cell lines (OCI-LY3,
OCI-LY7 and U-2932), profiled at 6, 12 and 24 h following compound treat-
ment. All compounds were treated using IC20 at 24h concentration. DMSO
was used as control media at each of the three time points, resulting in 857
GEPs.
http://www.ncbi.nlm.nih.gov/geo/
query/acc.cgi?acc¼GSE60408
GEODB [21] This data set contains GEP of 13 different compounds, obtained from nine in-
dependent expression sets obtained from the Gene Expression Omnibus
(GEO). Each expression set had at least six DMSO controls and six samples
for compound treatment. Three of the expression sets were profiled on
MCF7 breast cancer cell lines (GSE9936—three compounds, GSE5149 and
GSE28662—two compounds), and two on MDA-MB-231 metastatic breast
cancer lines (GSE33552—two compounds). The rest of the expression sets
were profiled in a B-cell lymphoma cell lines, which are chronic lympho-
cytic leukemia patient-derived cell lines(GSE14973), K422 non-Hodgkin’s
lymphoma cell lines (GSE7292), lytic-permissive lymphoblastoid cell lines
(GSE31447), diffuse large B-cell lymphoma patient-derived cell lines
(GSE40003) and mantle cell lymphoma cell lines (GSE34602).
http://www.ncbi.nlm.nih.gov/geo/
Follicular
lymphoma
[22]
CB33, SUDHL4 and SUDHL6 cells provided by R. Dalla-Favera (Columbia
University, NY) were maintained in IMDM (Life Technology), supplemented
with 10% FBS (Gemini) and antibiotics. The HF1 follicular cell line provided
by R. Levy (Stanford University, CA) was maintained in DMEM (Life
Technology), supplemented with 10% FBS and antibiotics. Cells were tested
negative for mycoplasma. Cells were not further authenticated.
Antibodies: rabbit anti-MYC (XP) (Cell Signaling Technology); rabbit anti-
FOXM1 and mouse anti-GAPDH (SantaCruz); rabbit anti-HMGA1, anti-ATF5,
anti-NFYB, mouse anti-TFDP1 (Abcam), Alprostadil, Clemastine,
Cytarabine and Troglitazone (Tocris), Econazole nitrate and Promazine
hydrochloride (Sigma) were reconstituted in DMSO (Sigma).
http://cancerres.aacrjournals.org/
content/early/2015/11/20/0008-5472.
CAN-15-0828.abstract
RAF-inhibitor
resistant
[23]
The data set consists of 143 proteomic/phenotypic entities under 89 perturb-
ation conditions. In perturbation experiments, the drugs are applied to cell
cultures after SkMel-133 cells are grown to about 40% confluence in com-
plete RPMI-1640 medium (10% heat-inactivated fetal bovine serum, 100
units/ml each of penicillin and streptomycin and incubated at 37
Cin5%
CO
2
) in six-well plates. After 24 h drug administration, the perturbed cells
are harvested. In control experiments (i.e. no drug condition), cells are
treated with the DMSO drug vehicle for 24 h.
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CMap
The CMap database consists of genome-wide transcriptional ex-
pression profiles of bioactive compounds from cultured cell
lines. In the original CMap study [3], the reference database con-
sisted of 564 gene expression profiles generated from exposing
five different human cell lines (MCF7, PC3, SKMEL5, HL60 and
ssMCF7) with 164 small molecules [3] (Build 1). In Build 2, this
has been significantly extended to 1309 approved small mol-
ecules applied to the same five human cell lines leading to over
7000 gene expression profiles. Build 1 and Build 2 use an
Affymetrix platform for generating the gene expression data. So
far, several methods have been developed using the CMap data-
base (either Build 1 or Build 2), either for new drug reposition-
ing/repurposing approaches or for improving the performance
of the original CMap method, also in comparison with other
data sets [2427]. Notably, Cheng et al. [28] presented a system-
atic approach to quantitatively assess the performance of such
methods. Hence, this study can be seen as a benchmark ap-
proach to assess any new methodology in the future.
LINCS L1000
The LINCS supported by the NIH, comprises 5000 genetic per-
turbagens (e.g. single-gene knockdowns or overexpressions)
and 15000 perturbagens induced by chemical compounds (e.g.
drugs) [29]. To date, over one million gene expressions have
been profiled and collected for this project using the L1000 tech-
nology [29]. The L1000 platform has been developed at the
Broad Institute by the CMap team to facilitate rapid, flexible and
high-throughput gene expression profiling at lower costs.
Specifically, the L1000 technology measures the expression of
only 978 so-called landmark genes, and the expression values
for the remaining genes are estimated by a computational
model using additional data from the Gene Expression Omnibus
(GEO) [30]. A user-friendly access to the database is provided by
the LINCS cloud Web page (http://www.lincscloud.org/l1000/),
which is a Web-based application allowing users to browse and
query the LINCS database.
In a simplified view, the L1000 data can be considered as a
‘big matrix’ where the rows correspond to 22 268 genes and the
columns are the millions of perturbations induced by the small
molecules. It is clear that such a large data set presents new
challenges to computational systems biologists who aim to ana-
lyze and visualize Big Data. In Table 2, we provide a brief over-
view of tools and software developed so far to explore and
understand the L1000 database.
CMap variations and extensions
ssCMap: statistically significant connectivity map
New methods of pattern matching algorithm and data normal-
ization were applied using CMap approach to help reduce noise
effects, results interpretation and strengthen the methods reli-
ability in generating unproven hypotheses [26]. For example, an
important method has been introduced by Zhang et al. [33],
Table 2. Tools and softwares developed for browsing, visualizing and querying the LINCS database
Name Description Features URL link
Enrichr [31] Enrichr is an easy-to-use intuitive enrichment analysis
Web-based tool providing various types of visualization
summaries of collective functions of gene lists.
Access, Search, Navigation,
Integration, Visualization
and Signature Enrichment
http://amp.
pharm.mssm.
edu/Enrichr
LINCS Data Portal The current version of the portal has features for searching
and exploring LINCS database.
Access, Search, Browse and
Navigation
http://lincsportal.
ccs.miami.
edu/dcic-
portal
Slicr Slicr (LINCS L1000 Slicer GSE70138 data only) is a metadata
search engine that searches for LINCS L1000 gene expres-
sion profiles and signatures matching users input
parameters.
Access, Search, Navigation,
Integration, Visualization
and Signature Enrichment
http://amp.
pharm.mssm.
edu/Slicr
L1000CDS
2
[32] L1000CDS
2
queries gene expression signatures against the
LINCS L1000 to identify and prioritize small molecules
that can reverse or mimic the observed input expression
pattern.
Access, Search, Navigation,
Integration, Visualization
and Signature Enrichment
http://amp.
pharm.mssm.
edu/
L1000CDS2
LIFE A semantically enhanced Web-based application that en-
ables access, navigation and exploration of a knowledge
base built by integrating and indexing all the LINCS data
types. LIFE allows access, navigation and exploration of
LINCS assays, biomolecules, related concepts and LINCS
screening results via a variety of views such as proteins,
genes, cell lines, small molecules. LIFE provides flexible
navigation of the LINCS assay and data landscape via list
functionality covering important assay biomolecules and
concepts; this enables a variety of use cases.
Access, Query, Search, Browse,
Navigation and Download
http://life.ccs.
miami.edu/life
iLINCS iLINCS is a portal that handles LINCS L1000 and
KinomeScan data. It facilitates integration of LINCS data-
derived signatures with other genome-scale signatures.
Access, Search, Navigation,
Leverage Ontology,
Visualization and Download
http://life.ccs.
miami.edu/life
LINCS Canvas
Browser [29]
Compact visualization of thousands of L1000 experiments;
clustering of perturbations based on signature similarity;
interactive gene list enrichment analysis using 32 gene
set libraries; query up- and downregulated gene lists
against over 140 000 L1000 conditions.
Access, Search, Navigation,
Integration, Visualization
and Signature Enrichment
http://www.
maayanlab.
net/LINCS/LCB
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called statistically significant connectivity map (ssCMap). The
approach uses connectivity score computation with permuta-
tion tests at both treatment instance level and treatment set
level that offers a statistical means to control over the possible
false connections between the gene signature and the reference
profiles. Because the CMap concept uses the entire genomic in-
formation of the patients and of the drug, one may view this ap-
proach as an attempt at systems treatment. However, it suffers
from having many draw backs as mentioned in [33]. In particu-
lar, it has no specific reference to the biological functions
altered by the disease in question. A top-ranked drug could be
misleading for having strong effects on a subset of functions at
the expense of altering other functions that are not associated
with the disease [34].
The ssCMap method introduces a new ranking score using
the following steps. First, treatment and control instances are
treated similarly, making the effect of the treatment instances to
be determined by DEG. Second, the genes that are affected by the
treatment instance, that is, genes that are highly differentially
expressed, are given more weight. Finally, the up- and downregu-
lated genes are handled equally, in such a way that 2-fold of the
up- or downregulation of a gene has the same relevance in con-
structing the reference profile. The genes are ordered using the
absolute value of their log expression ratios (fold change), as the
up- and downregulated genes are considered the same.
Moreover, the most significant gene will be at the top of the list,
while most of the insignificant gene will be at the bottom. This
ensures that the genes are ranked by their importance in the ref-
erence profile [33]. Assuming there are in total Ngenes, the first
gene in the list will be assigned a rank Nif it is upregulated, or a
rank Nif it is downregulated. In general, the ith gene in the list
will be ranked with ðNiþ1Þfor upregulation or ðNiþ1Þfor
downregulation. The ssCMap uses new scoring scheme for repre-
senting a query gene signature either with ordered or unordered
gene list. The important gene expressed will be assigned a rank
mor mdepending on whether it is up- or downregulated, where
mis the number of genes in the gene signature. The connection
strength [33] is calculated between reference profile Rand gene
signature sto measure a connection between reference profile
and gene signature.
CðR;sÞ¼X
m
i¼1
RðgiÞsðgiÞ:(1)
Where g
i
represents the ith gene in the signature, sðgiÞis its
signed rank in the signature and RðgiÞis this gene’s signed rank
in the reference profile (Equation 1). To have maximum connec-
tion between reference profile and gene signature, Zhang et al.
achieved it by matching mgenes and their regulation status in
the reference profile and the gene signature in the correct order
(for ordered gene signature) as shown in Equation 2. For an un-
ordered gene signature, all the genes in the list have equal weight
because there is no particular ordering; therefore, maximum con-
nection strength for unordered is calculated using Equation 3.
C0
maxðN;mÞ¼X
m
i¼1
ðNiþ1Þðmiþ1Þ:(2)
Cu
maxðN;mÞ¼X
m
i¼1
ðNiþ1Þ:(3)
The overall connectivity score (c) is calculated by dividing
the connection strength with the maximum connection
strength of a given gene signature and reference profile
Equation 4. The connectivity score ranges from 1 to 1, where 1
indicates a maximum positive connection of gene signature
with the reference profile, while 1 indicates a negative connec-
tion. To test the connection score, ssCMap uses a simple pro-
cedure to test the null hypothesis between the gene signature
and the reference profile that is achieved by generating a ran-
dom gene signature of ordered/unordered list using random se-
lection without replacement with equal probability of either up-
or downregulation. After generating the signature, ssCMap cal-
culates the connectivity score (c) of each signature as well as the
P-value associated with the connectivity score denoted by P.
Here, ~
cis the connectivity score between a random gene signa-
ture and a reference profile. The same procedure is repeated to
estimate the sampling distribution of the random signatures.
Zhang et al. provide a user-friendly software application for the
ssCMap algorithm [35].
ConnectivityScoreðcÞ¼CðR;sÞ=CmaxðN;mÞ:(4)
CMapBatch: a meta-analysis of drug response
Fortney et al. [27] have recently adapted a parallel CMap ap-
proach across multiple gene signatures of a disease, and named
the method ‘CMapBatch’. Specifically, instead of applying CMap
to one individual gene signature, the authors apply it to mul-
tiple gene signatures for the same disease and then combine
the resulting outcomes. Therefore, their approach is similar to a
meta-analysis. It is common for a complex disease to have
more than one signature available, and this justifies the appli-
cation of CMap to multiple gene signatures of a disease.
Previously, other groups [36,37] addressed this issue by combin-
ing those different gene signatures before applying CMap [35].
However, Fortney et al. emphasize that combining gene signa-
tures is problematic for strongly nonoverlapping gene sets. This
problem has been addressed by CMapBatch.
Formally, for each disease signature, CMapBatch obtains a
list of connectivity scores corresponding to all the small mol-
ecules (1309 in CMap Build 2) and combines them by using the
Rank Product method [38] to assign a consensus ranking on
each drug for all the tested gene signatures. The Rank Product
method was originally developed to identify DEG for replicated
experiments based on the ranking of the individual experi-
ments. Fortney et al. analyzed 21 signatures (s¼21) for lung can-
cer obtained from Oncomine [27,39]. The results reveal that
CMapBatch produces indeed a more stable list of drugs when
compared with the individual gene signatures. Specifically, the
median overlap of the top 50 drugs for 21 individual gene signa-
tures was 22, but for CMapBatch, the overlap was 39 drugs.
Furthermore, for a FDR threshold value of 0.01, 247 small mol-
ecules have been identified that significantly reverse the gene
expression changes of the tested signatures.
The method was used to further highlight more effective
drug candidates inhibiting cancer growth and the results com-
pare favorably with the results of the original CMap. Thus, scal-
ing up transcriptional knowledge increases the hit percentage
significantly from 44 to 78% of the top-ranked drugs. Moreover,
the resultant drug hits were characterized in silico and showed
slow growth significantly in nine lung cancer cell lines from the
NCI-60 collection [27]. In total, 247 candidate therapeutics were
identified for which two genes, CALM1 and PLA2G4A, are found
to be markers for drug targets in lung cancer [40].
Despite the fact that CMapBatch was only tested for lung
cancer, the proposed meta-analysis can be used for any disease
phenotype to prioritize therapeutics.
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Extensions of the CMap similarity metric
The CMap ability of finding connections and similarities be-
tween genes, diseases and drugs makes it useful in many appli-
cations but has a few draw backs. One of these is failure to
apply a comprehensive measure to validate the significance of a
gene signature when queried against reference profiles [33].
Several studies have focused on improving the original KS stat-
istics used as the ‘similarity metric’ by CMap. We highlight
some of these methodologies in Table 3.
High-performance computing platforms in CMap
As a computational and bioinformatics framework, connectivity
mapping has been underpinned by the powers of modern com-
puters. Throughout the development of connectivity mapping,
particularly CMap and its extensions, intensive permutation
tests are required to provide statistical rigor, and the ever-
growing expansion of the reference database has required faster
processing and/or better software architectures to fulfill such
requirements.
To address these issues related to the computational de-
mands, Zhang and his group developed high-performance com-
puting (HPC) models of connectivity mapping, called cudaMap
[45], which uses the computing power offered by the graphics
processing units (GPUs) of modern computers; a recent exten-
sion is QUADrATiC [46], which is a scalable gene expression
connectivity mapping framework for repurposing Food and
Drug Administration (FDA)-approved drugs. The framework
uses multiple processor cores to achieve high-speed connectiv-
ity mapping. Furthermore, concerted efforts have also been
made to formulate and standardize the procedures for creating
quality gene signatures across multiple data sets [47] and deter-
mining the optimal lengths of query gene signatures [48].
Computational evaluation of CMap methods
Transcriptional expression profiles are widely used to find
drug–disease or drug–drug relationships that could lead to new
methods in drug discovery [28]. However, a remaining challenge
is to evaluate methods based on such data sets. Despite the suc-
cess of various CMap approaches, there are few ways to quanti-
tatively evaluate the performance of the connectivity score for
the association between drugs and diseases by computational
means. There are two ways to computationally evaluate CMap:
first, evaluate drug–drug relations [18,42] and second, evaluate
disease–drug relations [28].
In evaluating drug–drug relationship, a drug signature is
used to query CMap to retrieve related drugs that have the same
ATC codes or chemical structures that are similar as studied in
[18,42]. However, in evaluating disease–drug relations, a disease
signature is used to query CMap to retrieve known drugs not-
ably in [28].
Iskar et al. [18] were among the first to study a quantitative
evaluation of CMap methods to identify similar compounds
using an ATC classification. They created labeled benchmark
sets using compound chemical similarities and ATC codes.
They focused on early retrieval performance where the false-
positive rate (FPR) is <0.1. At these FPRs, their calculated AUCs
were significantly different from random.
Cheng et al. [42] also used the ATC codes to benchmark the
similarity metrics using two different methods: the batch DMSO
control and mean-centering normalization. Focusing on early
retrieval performance (FPR ¼0.1), eXtreme cosine (XCos)
method outperforms the original CMap similarity metric based
on KS test. It is also robust in terms of drug–drug relationship
prediction with compounds that have higher treatment effect
on treated cell lines. Therefore, the authors further extended
the method for evaluating various CMap similarity metrics with
compound profiles that have higher treatment effect.
However, not all performance evaluations tend to work as
pointed out by [49] because of the following reasons: First, a lack
of high-quality disease signatures, as many diseases may not be
represented accurately by the reference profiles in the gene sig-
nature. Second, the benchmark sets used to represent the drug–
disease association might not be comprehensive enough to cap-
ture all drug–disease linkages. Finally, the drug cellular profiles
are limited to only treating fewer cell lines, which explains why
some of the neoplastic disease signatures perform better than
nonneoplastic disease signatures [28].
Applications of CMap in pharmacogenomics
Since the introduction of Build 1 in 2006, the CMap database
and the CMap method have been applied in a large number of
pharmacogenomics studies. These studies can be categorized
with respect to their application purpose. Specifically, CMap has
been used to identify novel phenotypic relations for disease
treatment, for drug repurposing/repositioning and for studying
drug combinations [50].
Discovering novel phenotypic relations
The most fundamental but also the most difficult task for which
the CMap database can be used is to identify a novel therapeutic
treatment for a disease [5]. This is also called a lead discovery. It
aims at establishing an advantageous connection between the
administration of a drug and a phenotypic response of the pa-
tient. Several studies used a CMap analysis to improve the
understanding of disease/phenotype associations by combining
some of the therapeutic agents identified in cancer [5153].
These studies have shown the full potential of the application
of CMap in drug discovery and in identifying cancer disease
therapeutic targets. Table 4 provides a list of applications in
finding drug targets or pathways and their associations with a
disease.
As an example, McArt et al. [60] used the ssCMap to find con-
nections for small molecule candidates that can be used for a
phenotypic analysis in the laboratory [35]. Specifically, their
study used a DNA microarray and RNA sequencing platform,
and they identified the same gene signature for which the re-
sulting drug (cotinine) suppressed androgen-driven cell prolifer-
ation [61]. Furthermore, they experimentally validated cotinine,
which inhibits proliferation in LNCaP cells [60].
Recently, a study conducted by Lim et al. [53] used a gastric
cancer gene signature to query CMap. The results of their ana-
lysis showed that histone deacetylase inhibitors (HDAC), which
include vorinostat and trichostatin A, were potential drug can-
didates for treating gastric cancer [53]. These findings were ex-
perimentally validated in vitro using gastric cancer cell lines,
where vorinostat significantly inhibited cell viability in a dose-
dependent manner [53].
Spijkers-Hagelstein et al. used CMap to demonstrate a reverse
effect of PI3K inhibitors in infants with MLL-rearranged acute
lymphoblastic leukemia (ALL). The study found the PI3K inhibitor
LY294002 to be significantly effective in reversing prednisolone-
resistance profile and induce sensitivity [51,62]. Moreover, the
prednisolone-sensitizing effects of LY294002 on two cell lines
studied consist of five downregulated genes, namely PARVB,
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Table 3. List of methodologies that extend the CMap similarity metric
Method name Description Advantage Disadvantage
ProbCMap: Probabilistic
drug connectivity
mapping [41]
A probabilistic connectivity mapping
by [41] was introduced as a model-
based alternative to the original
CMap. The method uses a probabil-
istic model that focuses on the rele-
vant gene expression effects of a
drug as a probabilistic latent factor
derived from the data on cell lines.
Finding functionally and chemically
similar drugs based on transcrip-
tional response profiles.
It has been shown that gene expres-
sion response factors between cell
lines can be promising when a mul-
tisource probabilistic model is used.
The method allows retrieval of a
combination of drugs.
It also shows how drug combination
retrieval provides complementary
information when compared with a
single-drug retrieval.
It is more sensitive to plat-
form differences.
The method intentionally ig-
nores possible cell line-spe-
cific effects of the drugs.
The approach relies on the
assumption that it is suitably
chosen based on the prob-
abilistic model.
Connectivity score
based on partial-rank
metrics [26]
This extension of the connectivity
score was introduced by Segal et al.
[26]. They apply partial-rank metrics
and empirical null distributions for
scoring CMap queries by accommo-
dating a query order, in contrast to
the KS scoring, which uses a rank
ordering of gene expression profiles
in the target instance to generate an
ordering of the query.
More effective methods than KS by
computing a per experiment score
that measures ‘closeness’ between
the signature and the reference
profiles.
New approaches measuring close-
ness for the common scenario
wherein the query constitutes an
ordered list.
Advance an alternate inferential ap-
proach based on generating empir-
ical null distributions that
characterize the scope, and capture
dependencies, embodied by the
database.
Hard to develop effective fit-
ting algorithms for large
instances.
Number of inferential prob-
lems surrounding use of met-
rics extended to partial
rankings, such as reconciling
asymptotic distributions.
XCos: Cosine-based
similarity [42]
The xCosine is introduced as alterna-
tive method used to computation-
ally evaluate the similarity between
reference profile and gene signa-
ture. In this novel CMap approach,
Cheng et al. used the Anatomical
Therapeutic Chemical (ATC) classifi-
cation as the benchmark to meas-
ure differences and similarities of
XCos method to other CMap scoring
methods, data processing methods
and signature sizes [42].
XCos outperforms CMap when used
with a larger number of features
(top 500).
Help find the analytical approaches
that are more accurate in evaluating
the CMap data.
Finds good transcriptional response
to drug treatment that appears to
have sufficient consistency in MoA.
The method is used to determine
the compound classes, which have
robust expression profiles in the
CMap data.
It emphasizes early retrieval, which
is important because in reposition-
ing the aim is to sacrifice some true
positives to keep false positives low.
Multiple ATC codes per com-
pound can lead to errors, and
redundant ATC codes may
inflate results.
Many ATC codes do not prop-
erly characterize MoAs.
Averaging over multiple cell
lines averages biological vari-
ation for compounds that
may have differential re-
sponses in the multiple cell
lines.
XSum: Systematic
evaluation of con-
nectivity map [28]
This method uses a similarity metric
that systematically evaluates mul-
tiple CMap methodologies by as-
sessing their performance on many
drug profiles across a curated data
set consisting of multiple disease
gene signatures [28].
Using XSum, CMap can significantly
enrich true positive drug-indication
pairs by a novel matching
algorithm.
It can be used as an effective simi-
larity measure to enhance the KS
statistics as well as filtering drug-
induced data.
The algorithm has a relative early
retrieval performance.
It can help tremendously in experi-
mental validation using small num-
ber of hypotheses.
The overall retrieval performance is
weak.
The drug–disease benchmark stand-
ard was not able to capture all
known drug–disease association.
(continued)
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D123, FCGR1B, PSTPIP2 and S100A2. Interestingly, the mentioned
genes appear to be expressed in children with ALL samples with
prednisolone-resistant, but not in ALL samples with
prednisolone-sensitive samples.
Another interesting study from Engerud et al. [25] found by
applying CMap that HSF1 and HSF1-related gene signatures are
correlated with a high-risk disease state in endometrial cancer,
and they also shed light on the underlying biological mechan-
isms. The results showed how HSF1 levels can predict a re-
sponse to drugs targeting HSP90 or any possible protein
synthesis. Furthermore, their results also justified that the HSF1
level and HSF1-related signatures impact on carcinogenesis
during disease progression and found that HSF1 can be used for
developing new therapeutic targets [17]. Therefore, HSP90 in-
hibitors are seen as novel targeted therapeutics for patients
with high HSF1 levels in tumors [25,63].
In addition, a similar approach of CMap application has been
used to investigate relationships between drugs and microRNAs
(miRNAs) [64]. Jiang et al. proposed a novel high-throughput ap-
proach to identify the biological links between drugs and
miRNAs in 23 different cancers and constructed the Small
Molecule-MiRNA Network for each cancer to systematically
analyze the properties of their associations. They concluded
that most of the miRNA modules comprised miRNAs that had
similar target genes and functions or were members of the
same miRNA family. The majority of the drug modules involved
compounds with similar chemical structures, modes of action
or drug interactions. Another common approach is to identify
drug–miRNA relationships by comparing disease molecular fea-
tures and drug molecular features, such as gene expression.
Wang et al. [65] proposed a novel computational approach to
identify associations between small molecules and miRNAs
based on functional similarity of DEG. The results show 2265 as-
sociations between FDA-approved drugs and diseases, where
35% of the associations have been reported in the literature.
Also, 19 potential drugs were identified for breast cancer, in
which 12 drugs were reported by previous studies. Their studies
provide a valuable perspective for repurposing drugs and pre-
dicting novel drug targets, which may provide new way for
miRNA-targeted therapy [65].
Duan et al. introduced an improved computational method
that potentially shows the importance of using the newly gen-
erated publicly available LINCS L1000 data set to rapidly priori-
tize small molecules that could reverse or mimic expression in
disease and other biological states. The DEG of these profiles
were calculated using the characteristic direction method [66].
The L1000CDS
2
uses the users’ input of either a gene-set
method or cosine distance method to compare the input signa-
tures with the L1000 signatures to perform the search via a
state-of-the-art Web interface. The L1000CDS2 method provides
prioritization of thousands of small-molecule signatures, and
their pairwise combinations, predicted to either mimic or re-
verse an input signature. It also predicts drug targets for all the
small molecules profiled using L1000 assay. To further show-
case the usefulness of the approach, they collected expression
signatures from human cells infected with Ebola virus at 30, 60
and 120 time points. Querying these signatures against
L1000CDS
2
, kenpaullone compound was identified. A GSK3B/
CDK2 inhibitor has shown a dose-dependent efficacy in inhibit-
ing Ebola infection in vitro without causing cellular toxicity in
human cell lines [67].
Using the CMap approach, Zhu et al. found vorinostat as a
possible candidate therapeutic drug in gastric cancer. The
HDAC inhibitor (e.g. vorinostat and trichostatin A) has an in-
verse correlation with a gastric gene signature, which shows an
interesting therapeutic target. Studies have already revealed the
efficacy of vorinostat as therapeutic drug that suppresses
growth of various cancer cell lines [68]. Moreover, many ana-
lysis of cancer-related cell lines and gastric cancer patients
showed vorinostat to be effective in altering expression levels,
hence making it effective for the upregulation of autophagy-
specific genes [69,70].
Siu et al. [71] highlighted the potential benefits of polyphyllin
D as a therapeutic drug for non-small cell lung cancer (NSCLC).
Interestingly, the extracts of the Paris polyphylla plant, contain-
ing polyphyllin D, have been long used in traditional Chinese
medicine for cancer treatment [72]. Their CMap analysis indi-
cated that polyphyllin D is a trigger for estrogen receptor-
induced apoptosis and mitochondria-mediated apoptotic path-
ways [73].
CMap-based elucidation of drug MoA
In pharmacology, understanding the exact effect of an active
compound on a system represented, e.g. by a gene signature, is
the central focus. Specifically, it is important to identify possible
new compounds that are performing activities based on par-
ticular targets [12]. Given a compound phenotypic gene signa-
ture, the CMap method [3] can be applied to identify such novel
active compounds. Thus, it provides a new hypothesis-
generating tool to identify signaling pathways affected by a
compound, connecting a biological state to the discovery of
Table 3. Continued
Method name Description Advantage Disadvantage
As the CMap performance is not
optimized, that process is prone to
be overfitting and bias.
Module-based chemical
function similarity
search [43]
This approach evaluates CMap (Build
1) data set using expression pattern
comparison-based chemical func-
tion similarity search, seen as an
improvement of CMap that can pro-
vide more biological information of
the chemicals.
Module-based expression pattern
comparison provides a possibility to
identify functional modules or path-
ways with two similar profiles.
It can help in finding chemicals that
are functionally alike because they
affect similar pathways or biological
processes.
Uses GO [44] modules to reduce fea-
ture selection.
It is limited to GO system to
define gene set.
When searching for related
profiles for a given chemical,
both module based and
CMap give similar rankings,
especially when two target
chemicals have close ranks.
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Table 4. An overview of the application of CMap for a number of different diseases
Disease Method Data set Result Drug Reference
CNS injuries CMap tool Human MCF7 breast adenocar-
cinoma (GSE34331)
The findings show the hypothesis that
inhibition of calmodulin signaling might
allow neurons to alleviate substrate
derived neurite growth restriction and CNS
regeneration.
Calmodulin and piperazine
phenothiazine (repurposed)
[54]
GBM Pathway analysis
and CMap tool
GBM data sets (GSE4290,
GSE7696, GSE14805,
GSE15824 and GSE16011)
Investigated antitumor drugs in GBM cell
lines and identify novel drugs that can
suppress GBM tumors.
Thioridazine [55]
Gaucher disease (GD1) Pathway analysis
and CMap tool
GD1 mouse (GSE2308) Predicted highly enriched anti-helminthic
compounds for new drug action on GD1
and repurposing.
Albendazole and oxamniquine [52]
Ovarian cancer CMap tool MCF7 and PC3 cell lines
(GSE5258)
Found a compound as PI3K/AKT pathway
inhibitor that shows the mechanism of
cancer therapeutics.
Thioridazine [56]
Stem cell leukemia (SCL) GSEA and CMap tool hESCs cell lines (GSE54508) Found two HDAC inhibitors as potential in-
ducers that can be used in treating SCL and
acute megakaryoblastic leukemias.
Trichostatin A and suberoyla-
nilide hydroxamic acid
[57]
T-cell acute lymphoblastic
leukemia (T-ALL)
GSEA and CMap tool Human and mouse T-ALL cell
lines (GSE12948, GSE8416 and
GSE14618)
Identified interconnecting regulatory path-
ways as therapeutic targets for T-ALL.
HDAC, PI3K and HSP90
inhibitors
[51]
Prostate cancer CMap tool Celastrol- and gedunin-treated
cell lines (GSE5505 and
GSE5508)
Identified target pathways of androgen
receptor (AR) signaling and modulation of
HSP90 MoA.
Celastrol and gedunin [17]
Gastric cancer Hierarchical cluster-
ing and CMap tool
Yonsei gastric cancer
(GSE13861)
Predicted two possible drug candidates for
gastric cancer therapy.
Vorinostat and trichostatin A [53]
Myelomatosis CMap tool Human myeloma cell lines
(GSE14011)
Found a drug with potential to induce sup-
pression of cyclin D2 promoter regulation.
Pristimerin [58]
AML CMap tool AML data (GSE7538) Predicted novel treatment of human primary
AML with parthenolide and transcriptional
response of cells.
Celastrol [59]
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disease–gene–drug connections, depending on the level of
observed changes, i.e. the molecular or functional (anatomical)
level.
Availability of computational approaches has sparked usabil-
ity of network models and system biology approaches to obtain a
deeper understanding of the basic biological drug–disease rela-
tions [57]. Specifically, methods have been developed to aid in
finding drugable targets and drug compounds based on a basic
understanding of biological processes in the pathway level.
These include methods such as integrating a functional protein
association network to form a new model, finding information
on a known target and enriched pathways, small molecules with
high connectivity score, investigating side-effect scores based on
ranked gene signatures and the use of novel methods from ma-
chine learning to evaluate CMap data set [7477].
There are also many other functional phenotype-based
approaches that use the CMap resource to understand MoA [7,
7880]. It is widely known that many drugs with therapeutic tar-
gets in cancer prognosis and diagnosis have been identified
using CMap. For example, CMap designated the mTOR inhibitor
rapamycin as a potential therapy for dexamethasone-resistant
ALL in children. A clinical trial is currently underway for assess-
ing this possible new indication [81]. A similar approach by Li
et al. has shown its power in discovering chemicals sharing
similar biological mechanisms and chemicals reversing disease
states. They used CMap and gene ontology (GO) [44] modules to
partition genes into small biological categories and performed
expression pattern comparison within each category [43]. The
method shows robustness in finding chemicals sharing similar
biological effects by using a reduced similarity matrix to meas-
ure the biological distances between query and reference pro-
files. This will pave in reducing experimental noises and
marginal effects and directly correlates chemical molecules
with gene functions.
Iorio et al. [4] generated a drug network (DN) from the CMap
database using a novel distance metric that is able to score the
similarity between gene expression profiles and drug treatment.
The authors partitioned the DN using graph theory tools to
identify groups of drugs (communities) that are densely inter-
connected [63]; the same method was also applied by [82,83].
Their results revealed that these groups were significantly en-
riched with drugs of a similar MoA and therapeutic purpose
and, hence, can be used for such predictive purposes. Their ana-
lysis exemplified their method studying HSP90 and CDK2 inhibi-
tors and showed that the predicted MoAs correspond to results
known in the literature [25,63,84]. Interestingly, their method
revealed a previously unknown MoA link between fasudil, a
Rho-kinase inhibitor, and autophagy. An experimental valid-
ation indeed confirmed this connection suggesting a reposition-
ing of this drug because so far fasudil is approved in Japan for
the treatment of cerebral vasospasm characterized by blood
vessel obstruction.
Kibble et al. uses CMap approach to show, via the case study
of the natural product pinosylvin, how the combination of two
complementary network-based methods can provide novel
mechanistic insights. They illustrate that elucidating the MoA
of multi-targeted natural products through transcriptional
response-based approaches can lead to unbiased hypotheses
that might not have been otherwise conceived and, hence, to
truly novel and even surprising findings [85].
Dudley et al. have shown that CMap data contain sufficient
information about the dynamic activities of human genes for
reconstructing gene–gene interactions in drug-perturbed cancer
cells. They successfully applied a Gaussian Bayesian network
framework [86] to reconstruct a subnetwork containing vali-
dated interactions between genes with known roles in the apop-
tosis pathway. In addition, their network successfully predicted
key players and interactions in drug-induced apoptosis, includ-
ing both intrinsic and extrinsic apoptosis pathways [87].
Choi et al. [5] proposed another computational optimization
method using CMap to find drug MoA. Their study used gene ex-
pression signatures of disease states or physiological processes
with gene expression signatures of small-molecule drugs to pre-
dict novel functional associations between small molecules
sharing the same MoA. The heat-shock protein 90 inhibitors
(HSP90i) were identified in the study as a candidate that sup-
presses homologous recombination (HR) in epithelial ovarian
cancer (EOC) patients [5]. They further showed that sublethal
concentrations of HSP90i 17-AAG suppresses HR sensitivity
observed in ovarian cancer cells [5,88]. Hence, the authors con-
cluded that the combination of 17-AAG and PARP inhibitors
(PARPis) olaparib or carboplatin in EOCs that inhibit HR will be
effective when developing PARPi resistance [5].
Shigemizu et al. [15] introduced a novel methodology similar
to the partial-rank metric, by using gene expression profile to
apply the CMap concept to identify candidate therapeutics for
MoA, targeting possible functions that are beyond drug repos-
itioning [89]. The method uses drug candidates in a pool of com-
pounds that downregulate the overexpressed genes, or
upregulate the underexpressed genes, for a given abnormal
phenotypic condition and demonstrate the utility of their ap-
proach for drug repositioning. The authors pointed out that the
improved functionality of their method will help in identifying
a drug or a group of drugs with potential heterogeneous proper-
ties. On the other hand, the method can be used to find genes
that can be targeted by a set of identified compounds. For in-
stance, the genes RPL35, LAMB1 and CAV1 have been found to
be breast cancer targets [15,90]. Finally, the result of their func-
tional analysis indicated that the MoA of tamoxifen is given by
downregulating TGF-bsignaling [15].
Drug repurposing
Generally, drug repurposing refers to investigating drugs that
are already used for treating a particular disease to see if they
can be safely and effectively used for treating other diseases.
The terms repurposing and repositioning are used interchange-
ably. Owing to the fact that the repurposing of a drug builds on
previous research and development efforts, new candidate
therapies could be ready for clinical usage more quickly and at
reduced costs. Over the past years, many approaches have been
developed for the generic drug repurposing; however, in the fol-
lowing, we will focus on investigations that have been using
CMap to repurpose drugs and to identify novel targets.
For instance, Kunkel and his group [37] used CMap to deter-
mine ursolic acid, a natural compound that is e.g. present in
apples, as a lead compound for reducing fasting-induced
muscle atrophy. They used rodents for an in vivo validation of
the therapeutic concept, demonstrating that ursolic acid is a po-
tentially interesting therapy candidate for muscle atrophy and
perhaps other metabolic diseases.
Applying the connectivity mapping approach to acute mye-
loid leukemia (AML), Ramsey et al. integrated gene signatures
from a mouse model of AML and a cohort of AML patients to
query the ssCMap. They identified entinostat as a candidate
drug able to alter the AML condition toward the normal state.
This prediction was followed up experimentally in cell line as
well as mouse models, and the authors were able to validate the
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predicted effects of entinostat on the signature genes, and
showed that in vivo treatment with this compound resulted in
prolonged survival of leukemic mice [91].
Johnstone et al. used a comparative microarray analysis of
compound-induced changes in gene expression for a possible
drug repurposing, and they discovered a novel compound. This
finding suggests a possible mechanism of calmodulin signaling
using piperazine as promoters of central nervous system (CNS)
neurite growth [54]. This study suggests that calmodulin can be
seen as a novel target enhancing neuron regeneration.
Furthermore, their analysis showed that a previously unrecog-
nized potential for piperazine phenothiazine antipsychotics can
be repurposed for neuron regeneration [54].
Jin et al. [92] presented a novel computational drug-
repurposing method to screen a combined set of drugs together
for treating type 2 diabetes [93]. Interestingly, they found that a
combination of Trolox C and Cytisine is effective for the treat-
ment of type 2 diabetes, but if used separately, neither of the
drugs are effective. Similarly, Sirota et al. [94] integrated a new
gene expression database from 100 diseases and 164 drug com-
pounds, yielding predictions for all drug compounds that show
a high consistency with already known therapeutics. As a dem-
onstration for a novel prediction, an experimental validation for
the antiulcer drug cimetidine was provided as a candidate
therapeutics in the treatment of lung adenocarcinoma (LA).
Malcomson et al. [95] has recently applied computational
drug repurposing successfully, as well, by using sscMap to iden-
tify candidate drugs that could be used to induce A20 and to
normalize the inflammatory response in cystic fibrosis. A20
(TNFAIP3) is a known nuclear factor-kB regulator, which is
reduced in airway cells. The authors used a co-expression-
based analysis to create a gene signature consisting of A20
showing high correlation. Then, Malcomson et al. performed a
connectivity mapping analysis using the sscMap framework.
The identified candidate drugs were subsequently validated in
airway epithelial cells, confirming that ikarugamycin and quer-
cetin have anti-inflammatory effects mediated by induction of
A20. They used small interfering RNA experiments to illustrate
that the anti-inflammatory effect of these two drugs is mainly
because of A20 induction.
Drug combinations
Rather than using single drugs in treating diseases, combin-
ations of multiple drugs are gaining more and more interest.
Such drug combinations are motivated by studies indicating
higher efficacy, fewer side effects and less toxicity compared
with single-drug treatments [36,96,97]. This seems to be par-
ticularly appropriate for complex disorders such as cancer, as
cancer cells possess compensatory mechanisms to overcome
perturbations occurring at the individual signaling pathway
level by means of, e.g. mutations of key receptors or cross-talk
between pathways [98].
For instance, Lee et al. [98] developed the Combinatorial Drug
Assembler as a genomic and bioinformatics system by using
gene expression profiling to target multiple signaling pathways
for a combinatorial drug discovery. The method performs an ex-
pression search against a signaling pathway to compare gene
expression profiles of patient samples (or cell lines) as input sig-
nature, with the expression patterns of the sample treated with
different small molecules. The method then finds the best pat-
tern that matches the combination of two drugs across the in-
put signature related to signaling pathways to detect and
predict those drugs that could be used in a combination
therapy. Furthermore, they performed in vitro validations for
NSCLC and triple-negative breast cancer (TNBC) cells and found
that alsterpaullone and scriptaid as well as irinotecan and
semustin for NSCLC, halofantrine and vinblastine for TNBC,
showed synergistic effects.
Huang et al. [99] proposed a novel systematic computational
approach called DrugComboRanker to find synergistic drug
combinations and to uncover their MoA. The drug functional
framework was built based on genetic profiles and network par-
titions of various DN clusters using a Bayesian nonnegative ma-
trix factorization. By building disease-specific signaling
networks based on disease profiles, drug combinations can be
identified by searching drugs whose targets are enriched in the
reference signaling module of the disease signaling network. An
evaluation of the method was performed for LA and endocrine
receptor-positive breast cancer.
Wang and his group [36] performed a meta-analysis to ob-
tain a list of 343 DEG of LA and used this signature to query
CMap to identify a combination of compounds whose treatment
reverse the expression direction. Compounds in categories such
as HSP90 inhibitor, HDAC inhibitor, PPAR agonist and PI3K in-
hibitor were identified as top candidates. An in vitro validation
demonstrated that either 17-AAG (HSP90 inhibitor) alone or in
combination with cisplatin can significantly inhibit LA cell
growth by inducing cell cycle arrest and apoptosis.
Parkkinen et al. [41] showed their proposed probabilistic con-
nectivity mapping method is capable of identifying drug combin-
ations. Specifically, they define a combined drug profile
consisting of drug pairs by assessing the correlation of their indi-
vidual profiles. Overall, this leads to a ranking of drug pairs rather
than individual drugs. A computational assessment of the pro-
posed method was conducted considering ATC codes and chem-
ical similarity as ground truth. Their hypothesis was that single
drugs with ATC codes having minor response effects will not re-
sult in a high relevance score, as other drugs with stronger effects
will dominate. However, their statistical analysis demonstrated
that a combinatorial matching improves the results for many
polypharmacologic drugs [41]. The authors highlight how LINCS
data set [11] could be used to extend benefits of the group factor
analysis-based probabilistic connectivity mapping in drug com-
bination. As it identifies both single or shared responses across a
large number of cell types, making it valuable for drug discovery
and development would be even possible to impose more struc-
ture on the group factor analysis model, by similarly inferring re-
sponse of a specific cell line to a drug, enabling high relevant
information for personalized medicine studies.
Experimental validations
Using a computational biology approach in combination with
CMap can help in finding new forms of drugs, predicting drug
candidates, pharmacological and toxicological properties in
chemicals [19,100102]. However, these predictions need to be
evaluated experimentally, either by using cell viability after drug
treatment in vitro or tumor growth after drug treatment in vivo
and, in some cases, using survival analysis of drug treatment in
the clinic. Moreover, disease samples collected from patients are
used to investigate the dynamics of disease progression; apart
from that, diverse preclinical models, such as cell lines and ani-
mal models, could be used in experiments to interpret CMap re-
sults, understand disease and validate hypothesis. In this section,
we discuss studies that provided such experimental validations.
Notably, Ishimatsu-Tsuji et al. identified fluphenazine com-
pound as a novel inducer in hair-growth cycle using CMap.
12 | Musa et al.
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Moreover, the results showed the additive effect of two com-
pounds that are being ranked by the CMap analysis [100].
Caiment et al. studied the reliability of the CMap method for clas-
sifying and predicting a drug in different forms. The study was
performed on hepatocellular carcinoma and liver cell model
exposed to a wide range of different compounds using ssCMap
application. The results of the analysis revealed significant posi-
tive connections [103]. Moreover, the method showed how the
CMap approach is robust in predicting a drug’s carcinogenicity
based on data from representative in vitro models by adding more
relevance for predicting human disease state and may be con-
sidered as a classification way of discovering new compounds
[103]. Also, Wang et al. established prediction models for various
adverse drug reactions, including severe myocardial and infec-
tious events. Also, they were able to identify drugs with FDA
boxed warnings for safety liability effectively. Therefore, it illus-
trates that a combination of effective computational methods
and drug-induced gene expression change can be proven as new
cutting edge to have a systematic drug safety evaluation [104].
Public data sets can be leveraged to validate drug hits and
understand drug mechanisms, e.g. drug efficacy and toxicity.
Using in silico drug screening via CMap followed by empirical val-
idations, Cheng et al. discovered that thioridazine can reduce the
viability of glioblastoma (GBM) cells and GBM stem cells, induce
autophagy and affect the expressions of related proteins in GBM
cells. Thus, thioridazine has the potential to treat GBM [55]. In
addition, thioridazine induces autophagy and apoptosis at a high
concentration, functioning through G protein-coupled receptors.
Although drugs in these previous examples were validated
in preclinical models, the question of whether the disease gene
expression was really reversed in disease models remains un-
known. A recent study in a mouse model of dyslipidemia found
that treatments that restore gene expression patterns to their
norm are associated with the successful restoration of physio-
logical markers to their baselines, providing a sound basis to
this computational approach.
PharmacoGx: a computational
pharmacogenomics platform
The availability of large-scale perturbation data sets, such as
CMap and LINCS L1000, opened new avenues for research in
pharmacogenomics. Nonetheless, issues such as lack of stand-
ards for annotation, storage, access and analysis challenge the
full exploitation of the pharmacogenomics data sets. Hence,
unifying platforms are required to integrate the currently exist-
ing data sets and the corresponding mining tools. For data inte-
gration purposes, such platforms should remove biases of
different sources such as batch effects, difference between
profiling platforms and cell-specific differences to best charac-
terize drug-induced effects. Furthermore, the unifying plat-
forms should be easy to use so that users can develop new
methods and functions for easy data manipulation and mining
within the platform [105107]. To address these issues,
PharmacoGx, an open source package, has been recently de-
veloped [108]. To the best of our knowledge, PharmacoGx is cur-
rently the only integrative platform developed for this purpose.
The PharmacoGx platform comprises two fundamental com-
ponents: first, efficient data structures to store pharmacological
and molecular data and experimental metadata (e.g. molecular
profiles of cell lines before and after treatment by compounds)
provided by the pharmacogenomics data sets. The storage
scheme of PharmacoGx provides a common interface for
multiple data sets, standardizes cell line and drug identifiers, and
provides easy access to the data. Furthermore, it facilitates easy
and side-by-side comparison of the pharmacogemonics data sets
that are usually scattered and independently collected.
The second component of PharmacoGx is its set of functions
for data manipulation and mining tasks, such as, removing the
biases of data and creating signatures representing drug-
induced changes in the gene expression of cell lines, implemen-
tation of the connectivity mapping analysis and computing the
connectivity score to infer links between the drug-induced sig-
natures and phenotypes. Furthermore, it should be noted that
such functions are not data set specific. For instance, connectiv-
ity mapping analysis can be performed on not only the CMap
data set but also the LINCS L1000 and any other drug perturb-
ation data set that will be curated and published in the future.
This provides an opportunity to compare the query results from
several data sets alongside one another. These features contrib-
ute to the uniqueness of the PharmacoGx package.
Connectivity mapping via PharmacoGx: a case study
We designed an experiment to show that PharmacoGx package
enables users to easily query the two state-of-the-art perturb-
ation data sets (i.e. CMap and L1000), and facilitates comparison
of the results along each other. For this purpose, we illustrate a
case study similar to the phenothiazines example by Lamb et al.
in the original CMap publication. L1000 and CMap both contain
profiles of five members of phenothiazine antipsychotics (i.e.
chlorpromazine, fluphenazine, prochlorperazine, thioridazine
and trifluoperazine). We first generated a small L1000 signature
set (Supplementary Materials)consistingof10uniqueinstances
of the family members and 990 randomly selected perturbation
signatures from the L1000 data set. The goal of this experiment is
to retrieve phenothiazine family members, from the L1000 and
CMap data sets, using a query signature generated from the pro-
file of only one of the family members (e.g. trifluoperazine). We
used trifluoperazine’s signature to generate a query signature by
selecting only genes whose expression values are highly affected
by the drug (—t-stat—>1). This led to a signature of length 458.
Query results have been shown in Table 5 as two ranked lists.
PharmacoGx matched trifluoperazine signature as the most simi-
lar to the query signature in both data sets. The other family
members have also been retrieved as top hits in both lists.
Discussion
The CMap methodology has been used in numerous applica-
tions by many research groups with a particular focus in drug
Table 5. Results of retrieving phenothiazines using a query signa-
ture generated from trifluoperazine profile
L1000 rank Drug name CMap rank Drug name
1 Trifluoperazine 1 Trifluoperazine
2 Fluphenazine 2 Thioridazine
3 Thioridazine 3 Fluphenazine
4 Triflupromazine 4 Prochlorperazine
74 Fluphenazine 20 Chlorpromazine
201 Prochlorperazine
253 Chlorpromazine
271 Chlorpromazine
284 Chlorpromazine
402 Chlorpromazine
438 Chlorpromazine
A review of connectivity map |13
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discovery and development as pointed out in this review. These
efforts have been aimed at identifying new therapeutic targets,
drug repurposing/repositioning opportunities, finding new MoA
for new or existing small molecules, predicting side effects and
improving biological understanding. Most of the potentials of
CMap mentioned are undoubtedly beneficial in pharmacogen-
omics research and useful in drug industries, as this approach
has been found to be extremely valuable in multiple biomedical
research scenarios.
The CMap method uses a simplistic model of pattern match-
ing techniques based on an unproven hypothesis to understand
the concept of cell biology in drug discovery. However, there is
no account for dynamics associated with the disease or the
drug under investigation, multi-organ effects and genetic vari-
ations. Therefore, incorporating additional models and data
sources will help in understanding the effect of candidate drugs
in specific disease settings and appropriate cellular tissue and
environmental factors that are more effective in drug discovery/
repurposing applications. Applications are not limited to such
disease-oriented querying with, for example, illustrations of
CMap generating hypotheses concerning MoA being showcased.
While CMap has achieved some notable successes [37,75], path-
ways and network-based models provide a more realistic
system-level insights into the molecular targets of the drug can-
didates, which is an essential step in drug repurposing/repos-
itioning process and phenotypic-based discovery [84].
Moreover, some limitations of the CMap approach can be
highlighted, for example, experimental replicates, a potential
issue with the CMap data (Build 1), as most small molecules
have only one replicate per cell line for each experiment. This
will present some challenges on statistical analysis, such as
finding DEG for small molecules compounds. Another limita-
tion is cell line coverage (the experiment was conducted only
using five human cancer cell lines and not all small molecules
were tested on all cell lines), the limited dosages and time
points (several small molecules were tested using 10 mM con-
centration with 6 h perturbation time point). Another possible
limitation in CMap is the presence of potential batch effect, the
similarity of gene expression profiles observed for unrelated
stimuli in grown or processed cells at the same time. Batch ef-
fects have been identified as a significant source of systematic
error that can be corrected [82]. Attempts to solve the problem
of batch effects have been made in the methods proposed. For
example, Iskar et al. [18] performed a quantitative evaluation of
CMap methods by applying a centered mean approach to nor-
malize the gene expression intensity values in CMap to reduce
batch-specific effects. Also, Iorio et al. uses the pairwise drug-
induced gene expression profile similarity (DIPS) scores be-
tween drug pairs in CMap to calculate total enrichment score
[4]. They used drug compounds with shared ATC classification,
and high chemical similarities to discretize true positives in
their approach. This is relevant in willingness to sacrifice true
positives to keep false positives low. Notably, Cheng et al. used
the ATC classification as a benchmark to address batch effects
using XCos. The novel XCos approach is used to determine
which drug compounds contain robust expression profiles in
CMap data, and which analytical approaches are more accurate
to use when evaluating CMap data set. Although some of these
limitations are derived from the practicality and resource con-
straints at the time of designing the approach, the caveats asso-
ciated with such systems abstraction methodology need to be
addressed during study design, for example, a proper biological
context, relevance of transcriptional changes to disease states,
representation of gene signatures to the global expression
profile and the overall reliability of the approach. Now with the
availability of the LINCS L1000 data set, covering cellular re-
sponses upon the treatment of chemical/genetic perturbagen,
including over 1.4 million gene expression profiles representing
15;000 small molecules compounds and 5000 genes (small
hairpin RNA and overexpression) in 15 cell lines. Researchers
can leverage the publicly available data to overcome some of
the CMap shortcomings.
The LINCS L1000 still lacks quality needed for comprehensive
drug discovery/repurposing, which makes it challenging for
understanding the data-processing pipeline and lead inferences,
mostly because it uses a noisy platform [109]. The current imput-
ation of the computational inferred genes used by the L1000 in
generating the data is also lagging. What is certain is that, the re-
cent methods developed using CMap/LINCS L1000 data have al-
ready shown great promises and constantly becoming more
appealing to researchers in pharmacogenomics. For more com-
prehensive understanding of drug MoA, some methodologies
incorporating other omics than transcriptomics would be benefi-
cial, including, for instance, methylation array for epigenetic
compound such as HDAC inhibitors or 5-AZA-CdR, metabolomics
and proteomics, as well as dynamic or longitudinal data, would
widen the limited view captured by the single time point of tran-
scriptomic responses. This will give the opportunity to shift drug
discovery toward personalized and precision medicine treatment
approach to enhance disease therapies.
Conclusion
In this article, we reviewed the connectivity mapping method-
ology and applications. Perturbation databases, such as CMap or
LINCS, offer a wealth of opportunities for computational drug dis-
covery approaches by enabling pharmacogenomics that extends
beyond classical pharmacology. A reason for this is that these
transcriptomic perturbation databases allow network (nonsingle
gene centered) approaches, e.g. at the pathway or network level.
So far, the majority of applications are focused on different can-
cer types. However, the principal ideas can be translated to any
other type of complex disease opening in this way the door into a
new era of drug discoveries. Research in extending connectivity
mapping concept and methodology is ongoing, and there are still
aspects such as the application of different similarity metrics
that need further investigations. Although few variations and im-
provements over the original CMap have been proposed, the field
lacks systematic evaluations of the new approaches. Therefore,
advantages and disadvantages of different methods are so far
not precisely measurable.
Key Points
Comprehensive review of perturbation databases, e.g.
CMap and LINCS L1000, that can be used for drug dis-
covery and drug repurposing.
Surveying applications of CMap and LINCS L1000 for
novel pharmacogenomics approaches.
Presentation of benchmarking approaches for evaluat-
ing computational drug discovery approaches.
Supplementary Data
Supplementary data are available online at http://bib.oxford
journals.org/.
14 | Musa et al.
by guest on January 10, 2017http://bib.oxfordjournals.org/Downloaded from
Acknowledgment
For professional proof reading of the manuscript we would
like to thank B
arbara Mac
ıas Sol
ıs.
Funding
AM was supported by a fellowship from CIMO (Finland) and
FE-S was supported by TUT (Finland). S-DZ was supported
by a grant of £11.5M (PI Professor Tony Bjourson) from
European Union Regional Development Fund (ERDF) EU
Sustainable Competitiveness Programme for N. Ireland,
Northern Ireland Public Health Agency (HSC R&D) & Ulster
University, and supported by the UK BBSRC/M- RC/EPSRC
co-funded grant BB/I009051/1. MD thanks the Austrian
Science Funds for supporting this work (project P26142). LSG
was supported by the Canadian Cancer Society Research
Institute (grant #703886). BH-K was supported by the
Gattuso Slaight Personalized Cancer Medicine Fund at
Princess Margaret Cancer Centre, the Canadian Institutes of
Health Research, the Natural Sciences and Engineering
Research Council of Canada, and the Ministry of Economic
Development and Innovation/Ministry of Research &
Innovation of Ontario (Canada). GVG was supported in part
by the Arkansas INBRE program, with grants from the
National Center for Research Resources (P20RR016460) and
the National Institute of General Medical Sciences (P20
GM103429) from the National Institutes of Health.
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... In order to relate disease to drug-induced state, CMap calculates a τ-score to assess the correlation between a query signature (a Z-score measuring how gene expression is associated with disease status) and a reference profile (measuring how a given component modifies gene expression). A negative connectivity score emphasizes an inverse similarity between a query signature and a reference profile 25 , indicating the potential utility of the identified molecule to normalize trait-associated gene expression profile. ...
... In order to link disease to drug-induced state, CMap calculates a τ-score to assess the correlation between a query signature (a Z-score measuring how gene expression is associated with disease status) and a reference profile (measuring how a given component modifies gene expression). A negative τ-score indicates that the identified molecule will normalize trait-associated gene expression profile, which can be repurposed to treat the disease 25 . ...
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