hazard assessment of chemical carcinogenicity in vitro ......hazard assessment of chemical...

Max-Planck-Institute for Molecular Genetics Human Genome Meeting 2012 Sydney, 14.03.2012 Max-Planck-Institute for Molecular Genetics Human Genome Meeting 2012 Sydney, 14.03.2012

Hazard assessment of chemical

carcinogenicity in vitro – replacing

animal testing in toxicology

Ralf Herwig

Max Planck Institute for Molecular Genetics

Ihnestr. 73, 14195 Berlin

[email protected]

mailto:[email protected]

• Introduction • Toxicogenomics for carcinogenic hazard assessment – recent results (FP6 carcinoGENOMICS project)

• Computational network analysis – interpreting genomic data at the network level (FP7 diXa project)

• Outlook and summary

Structure

42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin

Research Interests

Participation in the sequencing of the human – and other – genomes

High-throughput sequencing – individualisation of genomic information (1000 Genomes Project, Int. Cancer Genome Cons.)

Epigenetics – identifying modifiers

of tumour formation and progression

Systems biology – network analysis

and model development

Predictive toxicology

Animal genetics

Max Planck Institute for Molecular Genetics


Toxicity testing

• REACH (June 2007) compliance necessitates the assessment of toxicity for

a huge number of chemicals (30,000-68,000) by 2018

• involve a large number of animal testing (13-54 million)

• tests in animals involve a rather long period of time (e.g. two-year rodent

bioassay for carcinogenesis)

• toxicity pathways might be different in rodents and human leading, for

example, to false positive predictions (several FDA approved drugs are

rodent carcinogens)

• use of alternative in vitro assays is under development in many laboratories

that are hopefully faster and closer to the human in vivo situtaion


1. Develop robust computational methods for analyzing genome data (biomarkers, patterns)

2. Cross-validate the different bits of genomic information and integrate them in order to focus on cancer-relevant pathways

3. Connect these data with computational models that are able to predict the toxic effects of chemicals, ideally from human in vitro assays

Tasks for the bioinformatician


carcinoGENOMICS FP6 project

• EU project with 20 academic

and industrial partners

• Goal: replacement of animal

testing of carcinogenecity of

chemical compounds with in

vitro systems (liver, lung,

kidney, ES-cell derived

hepatocytes)

• test three classes of

compounds (genotoxic

carcinogens, non-genotoxic

carcinogens and non-

carcinogens) on in vitro

systems using Affymetrix

microarrays

http://www.carcinogenomics.eu


http://www.carcinogenomics.eu/

diXa – Europe-wide data integration system for chemical safety

• EU project with 7 academic and industrial partners

• development of a joined platform

• data collection and pathway-based analysis (ToxDB)

• liver toxicity modelling

• international regulatory activities





Structure


Comparison of in vitro models - liver


Introducing a stem cell-based hepatocyte system to toxicology

hES-Hep™002 cell system (hESC cell line SA002) P Björquist, G Brolén, Cellectis AB Gothenburg, Sweden.

Jensen J, et al. J Cell Physiol. 2009 Jun;219(3):513-9.

Experimental design:

Hepatic marker ICC screens


Metabolic competence

• variable expression of CYP family genes • 4148 of 8745 (47%) expressed genes are associated with „metabolic process“ • e.g. biotransformation of BAP is visible

in hES-Hep: CYP1A1 (P=0.006, fold = 3.57) CYP1B1 (P=0.0004, fold = 6.30) AHR (P=0.002, fold = 1.93)

Expression of phase I-III genes:


Discriminative response genes

- GC-RMA normalization

- compute log2-ratios within each

experiment (treated vs untreated)

- built an ANOVA model

- identified 592 genes (P

Dominant GTX responses at gene level

- ANOVA judges whether one of the three groups is different from the others

- this is in most cases the GTX group

- GTX response is dominating the outcome of response genes





Structure


Scoring pathway response with gene expression data

• relation between pathways and compounds is measured through gene

expression vectors

x11 x12 ... x1N

x21 x22 ... x2N

...

xK1 xK2 ... xKN

Pathways (K = 1695)

Genes (N = 18,394)

xij = 1 / Pathway_size

X =

y11 y12 ... y1M

y21 y22 ... y2M

...

yN1 yN2 ... yNM

z11 z12 ... z1M

z21 z22 ... z2M

...

zK1 zK2 ... zKM

Genes (N = 18,394)

Compounds (M = 15)

yij = | log2(R) | * | log10(P) |

Pathway 1 Pathway 2

Pathway K

Gene 1 Gene 2

Gene N

Pathway 1 Pathway 2

Pathway K

Pathways (K = 1695)

Compounds (M = 15)

zij = 1/Pathwayi *

SUMn | log2(Rnj) | * | log10(Pnj) |


Pathway response in hES-Hep

• pathway response can discriminate

specific groups of compounds or

toxicity classes

• pathway response scores can be normalized in order to compare different compounds

• pathway response can help interpreting effects of single compounds

• compounds can be correlated at the pathway level


Pathway scoring reflects dose

-60

-40

-20

0

20

40

60

80

100

120

140

-40 -20 0 20 40 60 80 100 120

middle dose

hig

h d

ose


Tox class specific pathway responses

GTX and NGTX specific response • apoptotic response through FASL higher in GTX than in NGTX (P=0.005) • PPAR signalling higher in NGTX than in GTX (P=0.009)

• e.g. WYE is a PPAR-a ligand proto-typical for peroxisome proliferators

• mechanisms of PPAR-mediated hepatocarcinogenesis have been shown in rodents and there is an ongoing debate whether this mechanism is also operative in human.

Peters (2008) Tox Sci 101: 1-3.


Pathway patterns are more stable than gene patterns

Patterns at the pathway level exert a stabilizing effect on gene expression variation and, thus, improve discrimination performance

N=37 pathway patterns

N=592 gene patterns

Yildirimman, et al. (2011) Tox Sci 124:278-290.


ConsensusPathDB - why interaction integration?

Overlap in genes associated with apoptosis in four most commonly used interaction databases


ConsensusPathDB – a meta-database for human functional interactions

• integrated 30 heterogeneous databases

on human functional interactions

• ~155,000 molecular species, >360,000

interactions, 4,700 pre-defined human

pathways

• heterogeneous interactions

• origin of interaction is preserved

• enables graph searches in the integrated

network (e.g. shortest paths, functional

modules)

• visualisation features

• enables over-representation and gene

enrichment analyses

Kamburov, et al. (2009) Nucleic Acids Res 37:D623-D628.

Kamburov, et al. (2011) Nucleic Acids Res 39:D712-D717. http://cpdb.molgen.mpg.de Kamburov, et al. (2013) Nucleic Acids Res 41:D795-D800.


Network analysis functionality update

Molecular concept visualization

- over-representation analysis

- integration of different concepts such as

pathways, network neighborhoods, complexes

- overlap and node size features

Computation of induced network modules

- over-representation analysis

- Berger et al., BMC Bioinformatics, 2007

- significance scoring of enriched network

modules

- parameter for intermediate nodes

Kamburov, Stelzl, Lehrach, Herwig (2013) Nucleic

Acids Res, 41:792-800.


IntScore – improving interaction networks

• implements six different methods for scoring interactions based on network topology or on annotation (+ one aggregate method) • can be used to construct high-quality interaction networks

http://intscore.molgen.mpg.de

Kamburov, Stelzl, Herwig (2012) Nucleic Acids Res 40:W140-146.


http://intscore.molgen.mpg.de/

Summary

• robust and reproducible protocols for omics data processing and analysis are needed and are generated

• molecular interaction databases (e.g. ConsensusPathDB) are useful tools that can be utilized to improve predictive power of assay systems

• network-based analysis of high-throughput data is robustifying the analysis and improves data interpretation

• stem cell-based assays can be used for hazard identification of chemicals


• Introduction • Toxicogenomics for carcinogenic hazard assessment – recent results (carcinoGENOMICS and diXa)

• Computational network analysis – interpreting genomic data at the network level


Structure


Toxicogenomics and high-throughput sequencing


New genetic basis for cancer therapy (and also toxicology?)

Catalogues of germline variations in human populations Catalogues of somatic variations

The 1000 Genomes Project Consortium (2012)

Nature 491:56-65. Dancey et al. (2012) Cell 148:409-420.


BAP prediction network

vanDelft et al. (2012) Toxicol Sci 130:427-439.

High-throughput sequencing and carcinogenicity assessment

• new information (e.g. isoforms, mutations, structural variations) allows a better characterization of the assay system • new application allow system-wide characterization of compound respponses


Network module analysis

NGTX

GTX

NC

-> pathways are natural „data

integrators“ and build the

opportunity to analyze multiple

„omics“ data in a correlated way

Cavill et al. PLOS Comp Biol, 2011;

Kamburov et al. Bioinformatics, 2011.

-> the biological network contains

subnetworks that respond to

specific classes of chemical

treatments

-> these subnetworks define

toxicity read-outs that can be

utilized to construct predictive

computer models for toxicity and,

in general, human health decisions


Acknowledgments

Max-Planck Institute for

molecular Genetics Christopher Hardt

Reha Yildirimman

Matthias Lienhard

Atanas Kamburov

Christoph Wierling

Hans Lehrach

Maastricht University Joost van Delft

Jos Kleinjans

VUB Brussels Mathieu Vinken

Tatyana Doktorova

Vera Rogiers

Cellectis Gothenburg Gabriela Brolen

Petter Björquist

Genedata Hans Gmünder

Timo Wittenberger

University College Dublin Michael Ryan

Greg Slattery

Thanks for Your attention !

hazard assessment of chemical carcinogenicity in vitro ......hazard assessment of chemical...

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