Computational Genomics and Proteomics Lab
Discovery of drug mode of action and drug
repositioning from transcriptional responses
Francesco
Iorioa,b
, Roberta
Bosottic
,
Emanuela
Scacheric
,
Vincenzo
Belcastroa
,
Pratibha
Mithbaokara
, Rosa
Ferrieroa
,
Loredana Murinob, Roberto Tagliaferrib, Nicola Brunetti
-
Pierria,d,
Antonella Isacchic,1, and Diego di Bernardoa,e,1
aTeleThon
Institute of Genetics and Medicine, Naples, Italy;
cDepartment
of Biotechnology,
Nerv iano
Medical Sciences, Milan, Italy;
eDepartment
of
Sy stems and Computer Science, “Federico II” Univ ersity of Naples, Naples, Italy;
dDepartment
of Pediatrics, “Federico II” Univ ersity of Naples, Naples,
Italy; and
bDepartment
of Mathematics and Computer Science, Univ ersity of Salerno, Salerno, Italy
Presenter:
Chifeng
Ma
Computational Genomics and Proteomics La
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Structure
•
Background
•
Method & Result
•
Conclusion
Computational Genomics and Proteomics La
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Background
Goal & Key point
Drug Mode of Action
New drug therapeutic effects
/known Drug reposition
Drug
Signature
Extraction
Drug Mode
of Action
Construction
Drug
Distance
Assessment
Computational Genomics and Proteomics La
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Background
Data:Connectivity
Map
Computational Genomics and Proteomics La
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Background
cMap
Data
Data size: 22277*6836
Drug treated sample
Gene
Log fold change:
Log2(drug
treated/normal)
•
1,267 compounds
•
several dosages
•
5 cell lines: HL60, PC3,
SKMEL5, and MCF7/ssMCF7
Computational Genomics and Proteomics La
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Method & Result
Overview
Computational Genomics and Proteomics La
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Method & Result
Drug Signature Extraction
•
D: the set of all the possible permutations of microarray probe
-
set
identifiers (MPI);
•
X: a set of ranked lists of probe
-
set identifiers computed by sorting, in
decreasing order, the genome
-
wide differential expression profiles
obtained by treating cell lines with the same drug;
•
δ: D
2
→ N: the Spearman
’s
Footrule
distance associating to
each pair of
ranked lists in X, a natural number quantifying the similarity between
them;
•
B: D
2
→ D: the
Borda
Merging Function associating to each pair of
ranked lists in X a new ranked list obtained by merging them with the
Borda
Merging Method;
Notation Initialization
Computational Genomics and Proteomics La
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Method & Result
Drug Signature Extraction
Spearman
’s
Footrule
Spearman’s
Footrule
between two samples x and y
Number of genes in the sample here m=22283
The rank list place of the
ith
gene
Computational Genomics and Proteomics La
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Method & Result
Drug Signature Extraction
Borda
Merging Function
A new ranked list of probes z is obtained
by sorting them according to their values
in P in increasing order
Computational Genomics and Proteomics La
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Method & Result
Drug Signature Extraction
Prototype Ranked List Generation
Once a PRL had been
obtained, a signature {
p,q
}
was extracted as the top
250 and bottom 250 as the
signature.
Computational Genomics and Proteomics La
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Method & Result
Drug Distance Assessment
Core distance algorithm:
Gene Set Enrichment Analysis(GSEA)
Computational Genomics and Proteomics La
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Method & Result
Drug Mode of Action Construction
Distance threshold
Computational Genomics and Proteomics La
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Method & Result
Drug Mode of Action Construction
•
A community is defined as a group of nodes densely
interconnected with each other and with fewer
connections to nodes outside the group
Community Identification
Affinity propagation algorithm
106 community
1309 nodes
41047 edges
(856086 edges total)
Computational Genomics and Proteomics La
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Method & Result
Drug Mode of Action Construction
Computational Genomics and Proteomics La
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Method & Result
Drug Mode of Action Construction
•
Anatomical Therapeutic Chemical (ATC) code
---
49/92 assessable communities significantly enrichment
•
GO enrichment analysis
•
MoA
-
Community assessment
Community
-
Mode of Action
relationship assessment
Computational Genomics and Proteomics La
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Method & Result
Drug Distance Assessment
Drug to Community distance
Distance between Drug d and drug x
Number of drugs in C which has a
significant edges with drug d
Computational Genomics and Proteomics La
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Method & Result
Drug Net (DN)
•
n.28 is closest, composed by
the HSP90 in
cMap
data
•
n.40 n.63 Na+∕K+
-
ATPaproteasome
inhibitors
•
n.104 NF
-
kB
inhibitors
HSP90 inhibitors test
Computational Genomics and Proteomics La
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Method & Result
Drug Net (DN)
Test of
cycin
-
dependent
kinases
(CDKs) inhibitors and
Topoisomerase
inhibitors
Biology experiment was conduct to confirm that
TDK inhibitors and
Topo
inhibitors share the
universal inhibitor p21
Computational Genomics and Proteomics La
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Method & Result
Drug Net (DN)
•
Search DN for drugs similar to 2
-
deoxy
-
D
-
glucose(2DOG)
---
n.1
---
induce
autophagy
•
Closest Drug
---
Fasudil
---
never been previously linked
to
autophagy
•
Biology experiment to confirm that
Computational Genomics and Proteomics La
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Conclusion
•
Developed a general procedure to predict the
molecular effects and
MoA
of new compounds, and to
find previously unrecognized applications of well
-
known drugs
•
Analyzed the resulting network to identify communities
of drugs with similar
MoA
and to determine the
biological pathways perturbed by these compounds.
•
In addition, experimentally verified a prediction
•
A website tool was implemented at
http://mantra.tigem.it
Computational Genomics and Proteomics La
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Computational Genomics and Proteomics La
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Reference
•
1.
Terstappen
GC,
Schlupen
C,
Raggiaschi
R,
Gaviraghi
G (2007) Target
deconvolutionstrategies
in drug
discovery. Nat Rev Drug
Discov
6:891
–
903.
•
2.
di
Bernardo D, et al. (2005)
Chemogenomic
profiling on a genome
-
wide scale using reverse
-
engineered
gene networks. Nat
Biotechnol
23:377
–
383.
•
3. Ambesi
-
Impiombato A, di Bernardo D (2006) Computational biology and drug discovery:
From single
-
tTarget
to network drugs.
Curr
Bioinform
1:3
–
13.
•
4. Berger SI,
Iyengar
R (2009) Network analyses in systems pharmacology. Bioinformatics 25:2466
–
2472.
•
5. Hopkins AL (2008) Network pharmacology: The next paradigm in drug discovery. Nat
Chem
Biol
4:682
–
690.
•
6. Mani KM, et al. (2008) A systems biology approach to prediction of
oncogenes
and molecular perturbation
targets in B
-
cell lymphomas. Mol
Syst
Biol
4:169.
•
7. Gardner TS,
di
Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying
compound mode of action via expression profiling. Science 301:102
–
105.
•
8.
Hu
G,
Agarwal
P (2009) Human disease
-
drug network based on genomic expression profiles.
PloS
One
4(8):e6536.
•
9. Hughes TR, et al. (2000) Functional discovery via a compendium of expression
profiles.Cell
102(1):109
–
126.
•
10.
Kohanski
MA, Dwyer DJ,
Wierzbowski
J,
Cottarel
G, Collins JJ (2008) Mistranslation of membrane
proteins and two
-
component system activation trigger
antibioticmediated
cell death. Cell 135(4):679
–
690.
Computational Genomics and Proteomics La
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The End
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