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
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
• 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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
• 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
Comparison of in vitro models - liver
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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:
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
• 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
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) |
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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.
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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.
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
ConsensusPathDB - why interaction integration?
Overlap in genes associated with apoptosis in four most commonly used interaction databases
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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.
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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.
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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.
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
• Introduction • Toxicogenomics for carcinogenic hazard assessment – recent results (carcinoGENOMICS and diXa)
• Computational network analysis – interpreting genomic data at the network level
• Outlook and summary
Structure
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
Toxicogenomics and high-throughput sequencing
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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.
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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
42. Seminar über Versuchstiere und Tierversuche, 11.09.2013, BfR, Berlin
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 !
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