computational genomics and proteomics lab discovery of drug mode of action and drug repositioning...

Computational Genomics and Proteomics Lab

Discovery of drug mode of action and drugrepositioning 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


Structure

• Background

• Method & Result

• Conclusion


BackgroundGoal & Key point

Drug Mode of ActionNew drug therapeutic effects

/known Drug reposition

Drug SignatureExtraction

Drug Mode of Action

Construction

Drug Distance

Assessment


BackgroundData:Connectivity Map


BackgroundcMap Data

Data size: 22277*6836Drug treated sample

Gene

Log fold change:Log2(drug treated/normal)

• 1,267 compounds • several dosages• 5 cell lines: HL60, PC3,

SKMEL5, and MCF7/ssMCF7


Method & ResultOverview


Method & ResultDrug 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: 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;

Notation Initialization



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



Borda Merging Function

A new ranked list of probes z is obtained by sorting them according to their values in P in increasing order



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.


Method & ResultDrug Distance Assessment

Core distance algorithm: Gene Set Enrichment Analysis(GSEA)


Method & ResultDrug Mode of Action Construction

Distance threshold



• A community is defined as a group of nodes densely interconnected with each other and with fewer connections to nodes outside the group

Community IdentificationAffinity propagation algorithm

106 community1309 nodes41047 edges(856086 edges total)



• Anatomical Therapeutic Chemical (ATC) code --- 49/92 assessable communities significantly enrichment

• GO enrichment analysis

• MoA-Community assessment

Community-Mode of Action relationship assessment


Method & ResultDrug 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


Method & ResultDrug 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



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



• 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


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


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.


The End

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computational genomics and proteomics lab discovery of drug mode of action and drug repositioning...

Documents

computational genomics

drug repositioning

b method result overview

mcf7ssmcf7 slide

b background data

ith gene slide

increasing order slide

connectivity map slide