The Karolinska Institute (KI) is the largest centre for medical education and research in Sweden and the home of the Nobel Prize in Physiology or Medicine. KI consists of 22 departments and 600 research groups dedicated to improving human health through research and higher education. The role of the Kohonen/Grafström team has been to guide the application, analysis, interpretation and storage of so called “omics” technology-derived data within the service-oriented subproject “ToxBank”.

1. Computational systems toxicology:
towards replacement of animal testing
Prof. Roland Grafström
Dr. Pekka Kohonen
Institute for Environmental medicine
(Institutet för miljömedicin, IMM),
Karolinska Institutet, Stockholm, Sweden
Lush Prize 2014 Presentation, London, UK, November 14, 2014

2. 2
Institute for Environmental Medicine
(Institutet för miljömedicin, IMM)
Environmental health risk assessment at IMM
• IMM has about 350 employees, one of the largest
institutes at Karolinska Institutet
• IMM performs research, education, health risk
assessment within the field of environmental
medicine
• IMM has a national responsibility and international
involvement: IMM provides authorities within and
outside of Sweden with expertise, support and
advice regarding environmental health risk
assessments

3. The fields of Cancer Biology, Toxicology and Alternative Methods
Development Go Hand-in-Hand
Cancer Biology
Toxicology
Basic mechanisms for
development of cancer
Treatment and
diagnosis of cancer
Biological components and
mechanisms of toxicity
Biomarkers and predictive
models for toxicity
Alternative Methods
Kohonen P, Ceder R, Smit I, Hongisto V, Myatt G, Hardy B, Spjuth O, Grafström R. Basic Clin. Pharmacol. Toxicol. 115:50-8, 2014

4. Systems biology biomarker generation from combining in vitro and in vivo data
Culture of tumour vs. normal cells under a standardized condition
Differentially expressed
proteins in tumour vs.
normal state
Gene Set Analysis Tool (Signature A)
(topGO)
Protein-enriched GO-categories
Differently expressed
transcripts within protein-enriched
GO-categories
INPUT
Protein-derived
signature
GO-based
transcript
signature
(Signature B)
Differentially expressed
transcripts in tumour vs.
normal state
OUTPUT
Chipster open source
platform for data
analysis
Aberrant molecular
networks and
upstream regulators
Upstream
regulator
signature
(Signature C)
Ingenuity Pathway
analysis tool (IPA)
REFINEMENT
Microarray training set
with normal and tumour
tissues (TCGA data set)
Signature Evaluation Tool (SET), K-Nearest
Neighbours (KNN) classification
Signature D
Signature E
Signature F
Proteomics
analysis
Transcriptomics
analysis
SET, KNN
classification
SET, KNN
classification
VALIDATION
Transcript level
 Public Normal and
Tumor Tissue Data
Sets (TCGA and
smaller data sets)
 The In Silico
Transcriptomics
Database
 The Human Gene
Expression Map
Protein level
 The Human Protein
Atlas
 Clinical studies
IN VITRO IN VIVO
Public data from human normal and tumor tissue
Microarray training set
with normal and TSCC
tissues (TCGA data set)
Microarray training set
with normal and TSCC
tissues (TCGA data set)

5. 5
Construction of the Head and Neck Cancer
Biomarker Resource to Sort Published Information

6. The SEURAT-1 / ToxBank Project
« Safety Evaluation Ultimately Replacing Animal Testing »
SEURAT-1:
 ~ 70 research groups
from European
Universities Public
Research Institutes and
Companies (more than
30% SMEs)
 50 million euro budget
 50% funding from
Cosmetics Europe
(www.seurat-1.eu)
ToxBank supports predictive toxicology research:
 cell and tissue banking information resource
 repository for the selected test compounds
 database of SEURAT-1 “gold” compounds
 dedicated web-based data warehouse with a
standardized input format (ISA-Tab)
 users access compounds, biological materials,
data and models for experimental planning and
integrated analysis of experimental results

7. Data analysis for predictive toxicogenomics and elucidation of toxicity pathways
Doxorubicin
ToxBank Data Warehouse (data curation and retrieval)
(Human hepatocytes)
Transcriptomics profiles Protocols and SOPs, upload investigation data in ISA-TAB format
expressed genes
(R/Bioconductor) 1.Doxorubicin (0.999)
comparative toxicogenomics database
Disease Name Disease ID
1. Cardiovascular Diseases MESH:D002318
2. Digestive System Diseases MESH:D004066
3. Neoplasms MESH:D009369
4. Neoplasms by Histologic Type MESH:D009370
5. Neoplasms by Site MESH:D009371
6. Nervous System Diseases MESH:D009422
Differentially
Pathway meta-analysis
using KEGG pathways
(InCroMap software)
Pathways
1. Cell cycle
2. p53 signaling pathway
3. Oocyte meiosis
4. TNF signaling pathway
5. DNA replication
6. Mismatch repair
7. Fanconi anemia pathway
8. Viral carcinogenesis
9. Rheumatoid arthritis
10. Influenza A
11. Chagas disease (American
trypanosomiasis)
12. Hepatitis B
13. Herpes simplex infection
14. Pyrimidine metabolism
Significance: *=FDR q-value < 0.05
Doses: C=Control, L=Low, M=Middle, H=High; Time: 8hr=8 hours, 24hr=24 hours
2. H-7 (0.999)
3. Mitoxantrone (0.998)
4. Alsterpaullone (0.997)
5. Camptothecin (0.991)
6. Ronidazole (0.87)
7. Medrysone (0.817)
8. Gliclazide (0.777)
9. Ginkgolide A (0.776)
10. Ellipticine (0.746)
11. Etamsylate (0.746)
12. Trioxysalen (0.744)
13. Ethaverine (0.739)
14. Doxazosin (0.738)
15. Amiodarone (0.719)
16. Morantel (0.687)
17. Phthalylsulfathiazole (0.684)
18. Dipyridamole (0.672)
19. Demeclocycline (0.645)
20. Famprofazone (0.643)
= topoisomerase II inhibitor (Mantra 2.0)
Kohonen et al. Basic Clin. Pharmacol. Toxicol. 115:50-8, 2014

8. Omics for Definition of Transformation Phenotype and Toxicity
Mechanisms in Formaldehyde-Exposed Human Epithelial Cells
3h 6h 12h 24h 48h
1mM HCHO
exposure
for 1 h
SVpgC2a*
SVpgC3a**
*Transformation required 40
consecutive 1h exposures to 1 mM
**Resistant to formaldehyde toxicity
relative to parental line
Transformation*
1h

9. Analysing the transformed phenotype
Enriched molecular and cellular
functions identified from Ingenuity
Pathway Analysis
Name P-value # molecules
Cellular
development
3.5E-10 –
4.83E-03
48
Cellular growth
and proliferation
3.8E-08 –
4.83E-03
44
Cell death and
survival
2.41E-07–
4.83E-03
39
Cellular
movement
2.49E-07–
4.83E-03
30
Cell-to-cell
signaling and
interaction
7.28E-07 –
4.83E-03
27
Genomic pertubations identified in
26 Cancer Studies in the cBIO
Cancer Genomics Portal

10. Carcinogenesis – a Multistep Process
Harris CC. Cancer Res, 51(18 suppl): 5023S-5044S, 1991

11. Omics for Definition of Transformation Phenotype and Toxicity
Mechanisms in Formaldehyde-Exposed Human Epithelial Cells
3h 6h 12h 24h 48h
1mM HCHO
exposure
for 1 h
SVpgC2a*
SVpgC3a**
*Transformation required 40
consecutive 1h exposures
**Resistant to formaldehyde toxicity
relative to the parental line
Transformation*
1h

12. Toxicogenomics for Assessment of Toxicity
Note: Changes > 2-fold
(p<0.01) relative to
control were considered
significant
Genes
Ontologies Note: p<0.01 was set as
threshold for significant
enrichment using the
Gene Ontology Tree
Machine

13. “Connectivity mapping, grouping/read across”:
formaldehyde omics data implicates inhibited
DNA repair

14. Bench-mark Dosing relative Published Animal Data
Biological Process Molecular Function
Time Mean BMD
(ppm)
Number of
categories
contributing to
average BMD
Mean BMD (ppm) Number of
categories
contributing to
average BMD
1h 6.9 2
3h 6.7 18 6.8 7
6h 6.8 32 6.5 13
12h 6.7 38 6.8 16
24h 6.4 11 6.8 11
48h 7.2 9 4.7 1
Average 6.8 ± 0.3 6.4 ± 0.9

15. Levels of formaldehyde-Induced DNA Protein Crosslinks
Required for Transformation of Human Cells and Rat Nasal
Tumors: Effect of a Single Exposure
IN VIVO
IN VITRO
(FORMALDEHYDE
IN AIR)
HCHO
HCHO
HCHO
HCHO
EPITHELIUM
CULTURED HUMAN CELLS
NUCLEUS
RESPIRATORY
FORMALDEHYDE IN AIR
MUCUS
RESPIRATORY EPITHELIUM
HCHO
HCHO
NUCLEUS
FORMALDEHYDE
IN SOLUTION
≈105 DPX/cell
≈105 DPX/cell
(6 ppm, 3h)
(1mM, 1h)

16. Genomics-Based Assessment of Health Adverse Effects
Exemplified by Formaldehyde Studies
Rats exposed by inhalation to
tumor-inducing concentration
Traditional pathology-related
biomarkers e.g., DNA protein
crosslinks
Novel molecular biomarkers
e.g., GO-categories, single
genes
Human cells exposed to
transforming concentration
Nasal instillation-exposed rats
Formaldehyde
solution
Formaldehyde
solution
Formaldehyde
aerosol

17. Future: from high-throughput screening of many agents to
genomic profiling analysis of the selected few
Kohonen et al. Basic Clin Pharmacol Toxicol. 115:50-8 2014

18. Involvement in the European NanoSafety Cluster Projects
 eNanoMapper – “A Database and Ontology Framework for Nanomaterials
Design and Safety Assessment”. Extending the ToxBank framework, the
eNanoMapper proposes a computational infrastructure for toxicological data
management of engineered nanomaterials (ENMs) based on open standards.
Overall aim: to provide an ENM ontology and database applicable to modelling
and risk assessment
 FP7-NANOSOLUTIONS: Biological Foundation for the Safety Classification of
Engineered Nanomaterials (ENM): Systems Biology Approaches to Understand
Interactions of ENM with Living Organisms and the Environment “. Overall aim:
deepened understanding of nano-bio-interactions applicable to connectivity
mapping
 NANoREG; “A Common European Approach to the Regulatory Testing of
Nanomaterials”. Overall aim: reference methods applicable for REACH
regulation of ENMs and centralized data for a nanosafety toolbox

19. General conclusions
 Methods developed in the genomic sciences (particularly cancer biology) are
transforming toxicology from an observational to a mechanistic science
 The 21st Century toxicology approach and the SEURAT project aim to replace
animal experiments by higher throughput, reliable human cell-based methods
 Using such an approach (“systems toxicology”) relies on “omics” measurements
and computational tools to mechanistically characterize cellular effects of
chemicals, and then to apply the data for prediction and modelling of organism
level toxicity