Discovery of drug mode of action and drug

lessfrustratedBiotechnology

Oct 23, 2013 (3 years and 7 months ago)

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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
b

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
b

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
b

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
b

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
b

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
b

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,
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R,
Gaviraghi

G (2007) Target
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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

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Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying
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Hu

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P (2009) Human disease
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Kohanski

MA, Dwyer DJ,
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Computational Genomics and Proteomics La
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The End

Thank you!

Question?