Transcript Computational Genomics and Proteomics Lab Method & Result

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, Nerviano Medical Sciences, Milan, Italy; eDepartment of
Systems and Computer Science, “Federico II” University of Naples, Naples, Italy; dDepartment of Pediatrics, “Federico II” University of Naples, Naples,
Italy; and bDepartment of Mathematics and Computer Science, University of Salerno, Salerno, Italy
Presenter: Chifeng Ma
Computational Genomics and Proteomics Lab
Structure
• Background
• Method & Result
• Conclusion
Computational Genomics and Proteomics Lab
Background
Goal & Key point
Drug Mode of Action
New drug therapeutic effects
/known Drug reposition
Drug
Signature
Extraction
Drug
Distance
Assessment
Drug Mode
of Action
Construction
Computational Genomics and Proteomics Lab
Background
Data:Connectivity Map
Computational Genomics and Proteomics Lab
Background
cMap Data
Data size: 22277*6836
Drug treated sample
Gene
• 1,267 compounds
• several dosages
• 5 cell lines: HL60, PC3,
SKMEL5, and MCF7/ssMCF7
Log fold change:
Log2(drug
treated/normal)
Computational Genomics and Proteomics Lab
Method & Result
Overview
Computational Genomics and Proteomics Lab
Method & Result
Drug Signature Extraction
Notation Initialization
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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: D2 → N: the Spearman’s Footrule distance associating to each pair of
ranked lists in X, a natural number quantifying the similarity between
them;
B: D2 → 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;
Computational Genomics and Proteomics Lab
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 Lab
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 Lab
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 Lab
Method & Result
Drug Distance Assessment
Core distance algorithm:
Gene Set Enrichment Analysis(GSEA)
Computational Genomics and Proteomics Lab
Method & Result
Drug Mode of Action Construction
Distance threshold
Computational Genomics and Proteomics Lab
Method & Result
Drug Mode of Action Construction
Community Identification
Affinity propagation algorithm
• A community is defined as a group of nodes densely
interconnected with each other and with fewer
connections to nodes outside the group
106 community
1309 nodes
41047 edges
(856086 edges total)
Computational Genomics and Proteomics Lab
Method & Result
Drug Mode of Action Construction
Computational Genomics and Proteomics Lab
Method & Result
Drug Mode of Action Construction
Community-Mode of Action
relationship assessment
• Anatomical Therapeutic Chemical (ATC) code
--- 49/92 assessable communities significantly enrichment
• GO enrichment analysis
• MoA-Community assessment
Computational Genomics and Proteomics Lab
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 Lab
Method & Result
Drug Net (DN)
HSP90 inhibitors test
•
n.28 is closest, composed by
the HSP90 in cMap data
•
n.40 n.63 Na+∕K+ATPaproteasome inhibitors
n.104 NF-kB inhibitors
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Computational Genomics and Proteomics Lab
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 Lab
Method & Result
Drug Net (DN)
• Search DN for drugs similar to 2-deoxy-Dglucose(2DOG) ---n.1---induce autophagy
• Closest Drug--- Fasudil--- never been previously linked
to autophagy
• Biology experiment to confirm that
Computational Genomics and Proteomics Lab
Conclusion
• Developed a general procedure to predict the
molecular effects and MoA of new compounds, and to
find previously unrecognized applications of wellknown 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 Lab
Computational Genomics and Proteomics Lab
Reference
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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 singletTarget 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 Lab
The End
Thank you!
Question?
Computational Genomics and Proteomics Lab