Prepared for Date Status SME Forum September 3, 2009 non-confidential emergentec, 2009 ::::::::...

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emergentec biodevelopment GmbHRathausstrasse 5/3

A-1010 Vienna, Austria

www.emergentec.com

in-silico workflows forbiomarker and target identification

Bernd Mayer

[email protected]

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background

emergentec biodevelopment is

developing and applying computational tools for biomarker and target identification.

explorative differential abundance

hypothesisdriven

candidates

from Liu et al. 2006 Cancer Cell 9(4):245-7

functionalinterpretation

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definitions

WORKFLOWFramework for a progression of computational steps, sequentially or in parallel,for identifying biomarkers and targets.

TARGETA compound with biomarker characteristics which can be addressed by therapy means for altering a given cellular status.

BIOMARKERA compound (gene, RNA, protein, metabolite, etc.) withhigh sensitivity and specificity for a given cellular status.

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biomarkers & application

epitope

antibody

an

tib

od

y

ep

itop

e

VACCINE

ELISA

THER. AB

AB in IHC

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the trend

Ex 1 Ex 2 Ex 3 Ex 4 Ex 5ID1 0.76 0.85 0.69 0.57 0.88ID2 2.34 1.74 1.89 1.27 3.22

Group T

Ex 6 Ex 7 Ex 8 Ex 9 Ex 10ID1 -0.18 -0.16 -0.12 -0.08 -0.24ID2 1.45 1.72 1.38 1.66 2.03

Group C

...from descriptive lists of candidates

from Bornholdt et al.2005 Science 310(5747):449-51

to candidates derived from a functional context

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agenda

I. Sequential (iterative) workflows

II. Parallel (integrated) workflows

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sequential procedure

selection

expressionco-regulationinteraction networksfunctional pathways...

omicsgenomicstranscriptomicsproteomicsmetabolomics...

characterizationstructurelocationdeterminants...

verificationPCRELISA, IHC,siRNA...

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focus on SE/SP

1. Filter genesQuality Filter (80%)

41,121 features

16,100 features

1,081 features

2. Replace missing values• KNN – Algorithm

3. Filter genesVariation Filter (SD > 0.9)

1.

2.

3.

4. Statistical testingt-test with maxT correction

n candidates

4.

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refinement of SE/SP

statistical analysis network analysisco-regulation analysis

94.0

67.0

43.0

30.0

14.4

MW

3 4 5 6 pI 7 8 9 10

[KDa]

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co-regulation

Detection of coregulation in differential gene expression profiles. P Perco, A Kainz, G Mayer, A Lukas, R Oberbauer, B Mayer, Biosystems 82, 235-247 (2005).

ID1

ID2

ID3

correct / expand Omics results from statistics

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protein networks

differentially expressed genesgenes of a particular functional category

differentially expressed genes

Biomarkers for cardiovascular disease and bone metabolism disorders in chronic kidney disease: A systems biology perspective. P Perco, J Wilflingseder, A Bernthaler, M Wiesinger, M Rudnicki, B Wimmer, A Kainz, A Lukas, G Mayer, B Mayer, R Oberbauer, Journal of Cellular and Molecular Medicine, 12, 1177-1187 (2008).

Stress Responses and Conditioning Effects in Mesothelial Cells Exposed to Peritoneal Dialysis Fluid. K Kratochwill, M Lechner, Ch Siehs, HC Lederhuber, P Rehulka, M Endemann, DC Kasper, KR Herkner, B Mayer, A Rizzi, Ch Aufricht, Journal of Proteome Research, 8, 1731-1747 (2009).

and interpret in the context of PPIs

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addressable

Workflows for computing subcellular location, PTMs, accessibility, antigenicity, etc.

http://taverna.sourceforge.net/

Machine learning approaches for prediction of linear B-cell epitopes on proteins. J Sollner, B Mayer, Journal of Molecular Recognition 19, 200-208 (2006).

Identifying discontinuous antigenic determinants on proteins based on shape complementarities and binding energies. R Rapberger, A Lukas, B Mayer, Journal of Molecular Recognition 20, 113-121 (2007).

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example workflow

Meta-UP (192)publication meta-analysis

Meta-DOWN (106)down-regulated genes

SEREX (81)exp. derived autoantigens

3

1

Meta-UP (86)up-regulated genes

subcellular location

co-regulation

conjoint pathways

interaction networks

CCP data set;Schaner et al. 2003;Welsh et al. 2001;

candidate protein selection

in silico immunogenicity scoring

experimental verification

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exp. verification

interpreting candidates in their context

Linking the ovarian cancer transcriptome and immunome.R Rapberger, P Perco, C Sax, T Pangerl, C Siehs, D Pils, A Bernthaler, A Lukas, B Mayer, M Krainer, BMC Systems Biology, 3;2:2. (2008).

Identification of a novel melanoma biomarker derived from melanoma-associated endogenous retroviruses. J Humer, A Waltenberger, A Grassauer, M Kurz, J Valencak, R Rapberger, K Wolff, T Muster, B Mayer, H Pehamberger, Cancer Research 66, 1658-1663 (2006).

and their experimental verification

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a parallel approach

concept

object lists

ID1

ID2

...

...IDn

object dependencies

• data consolidation• reduce false positives• derive functional context

goals

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omicsNET construction

Ansatz for Dynamical Hierarchies. S Rasmussen, N Baas, B Mayer, M Nilsson, MW Olesen. Artificial Life 7, 329-353 (2001).

No sequential data enrichment, but one-step data integration

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omicsNET characteristics

Characterization of protein-interaction networks in tumors.Platzer A, Perco P, Lukas A, Mayer B, BMC Bioinformatics. 2007 Jun 27;8:224.

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example analysis

A dependency graph approach for analysis of differential gene expression profiles.A Bernthaler, I Mühlberger, R Fechete, P Perco, A Lukas, B MayerMolecular Biosystems 2009; June 3 ahead of print

Rosenwald et al., 2001Rosenwald et al., 2002Rosenwald et al., 2002Rosenwald et al., 2001Zhan et al., 2002

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and expanding annotation

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in brief

Bioinformatics as support / driver for biomarker and target discovery:

• Clearcut experimental study designs

• Thorough local data management

• Integrate the data sources out there with your own (Omics) data

• Make use of the public domain analysis workflows

• Do not stick with statistics but go for functional context in candidate selection

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some tools & databases

For data preparation and statistics: R-Bioconductor, MeV, etc.

For coregulation:oPOSSUM, CORG, CONFAC, etc.

For protein interactions:OPHID, INTACT, etc.

For pathways:KEGG, PANTHER, GO, etc.

For reference data repositories: arrayExpress, GEO, SMD, Oncomine, Swiss2D Page, etc.

For annotation:GeneCards, iHOP, etc.

For viewing:Cytoscape, etc.

For context analysis:STRING

For target characterization:PSORT, PROSITE, SABLE, PSIPRED, etc.

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acknowledgements

@emergentec

Paul Perco and teamJohannes Soellner and teamMartin Haiduk and team

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data.Perco P, Rapberger R, Siehs C, Lukas A, Oberbauer R, Mayer G, Mayer BElectrophoresis 2006; 27(13):2659-2675.

A dependency graph approach for analysis of differential gene expression profiles.Bernthaler A, Mühlberger I, Fechete R, Perco P, Lukas A, Mayer BMolecular Biosystems 2009; June 3 ahead of print

OVCAD

SYNLET

predictIV

Prepared for Date Status SME Forum September 3, 2009 non-confidential emergentec, 2009 ::::::::...

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