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

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

Institute for Environmental Medicine

(Institutet för miljömedicin, IMM)

The fields of Cancer Biology, Toxicology and Alternative Methods

Development Go Hand-in-Hand

Cancer Biology

Toxicology

Alternative Methods

Basic mechanisms for

development of cancer

Treatment and

diagnosis of cancer

Biological components and

mechanisms of toxicity

Biomarkers and predictive

models for toxicity

Kohonen P, Ceder R, Smit I, Hongisto V, Myatt G, Hardy B, Spjuth O, Grafström R. Basic Clin. Pharmacol. Toxicol. 115:50-8, 2014

Differentially expressed

proteins in tumour vs.

normal state

Differently expressed

transcripts within protein-

enriched GO-categories

Protein-enriched GO-

categories

INPUT

GO-based

transcript

signature

(Signature B)

Protein-

derived

signature

(Signature A)Gene Set Analysis Tool

(topGO)

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)

Systems biology biomarker generation from combining in vitro and in vivo data

Microarray training set

with normal and tumour

tissues (TCGA data set)

Signature D

Signature E

Signature F

REFINEMENT

Signature Evaluation Tool (SET), K-Nearest

Neighbours (KNN) classification

Proteomics

analysis

Transcriptomics

analysis

Culture of tumour vs. normal cells under a standardized condition

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)

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Construction of the Head and Neck Cancer

Biomarker Resource to Sort Published Information

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

Doxorubicin (Human hepatocytes)

Transcriptomics profiles Protocols and SOPs, upload investigation data in ISA-TAB format

ToxBank Data Warehouse (data curation and retrieval)

comparative toxicogenomics database

Disease Name Disease ID1. Cardiovascular Diseases MESH:D0023182. Digestive System Diseases MESH:D0040663. Neoplasms MESH:D0093694. Neoplasms by Histologic Type MESH:D0093705. Neoplasms by Site MESH:D0093716. Nervous System Diseases MESH:D009422

Pathway meta-analysis using KEGG pathways (InCroMap software)

Pathways1. 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

Differentially

expressed genes

(R/Bioconductor) 1.Doxorubicin (0.999) 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

Data analysis for predictive toxicogenomics and elucidation of toxicity pathways

3h 12h 24h 48h6h

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*

Omics for Definition of Transformation Phenotype and Toxicity

Mechanisms in Formaldehyde-Exposed Human Epithelial Cells

1h

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

Analysing the transformed phenotype

Enriched molecular and cellular

functions identified from Ingenuity

Pathway Analysis

Genomic pertubations identified in

26 Cancer Studies in the cBIO

Cancer Genomics Portal

Carcinogenesis – a Multistep Process

Harris CC. Cancer Res, 51(18 suppl): 5023S-5044S, 1991

3h 12h 24h 48h6h

1mM HCHO

exposure

for 1 h

SVpgC2a*

SVpgC3a**

*Transformation required 40

consecutive 1h exposures

**Resistant to formaldehyde toxicity

relative to the parental line

Transformation*

Omics for Definition of Transformation Phenotype and Toxicity

Mechanisms in Formaldehyde-Exposed Human Epithelial Cells

1h

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

“Connectivity mapping, grouping/read across”:

formaldehyde omics data implicates inhibited

DNA repair

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

Levels of formaldehyde-Induced DNA Protein Crosslinks

Required for Transformation of Human Cells and Rat Nasal

Tumors: Effect of a Single Exposure

(FORMALDEHYDEIN AIR)

HCHOHCHO

HCHO

CULTURED HUMAN CELLSNUCLEUS

RESPIRATORY

EPITHELIUM

FORMALDEHYDE IN AIR

MUCUS

RESPIRATORY EPITHELIUM

HCHOHCHO

HCHO

NUCLEUS

FORMALDEHYDE

IN SOLUTION

IN VIVO

IN VITRO

≈105 DPX/cell

≈105 DPX/cell

(6 ppm, 3h)

(1mM, 1h)

Human cells exposed to

transforming concentration

Genomics-Based Assessment of Health Adverse Effects

Exemplified by Formaldehyde Studies

Rats exposed by inhalation to

tumor-inducing concentration

Nasal instillation-exposed rats

Traditional pathology-related

biomarkers e.g., DNA protein

crosslinks

Novel molecular biomarkers

e.g., GO-categories, single

genes

Formaldehyde solution

Formaldehyde solution

Formaldehyde aerosol

Kohonen et al. Basic Clin Pharmacol Toxicol. 115:50-8 2014

Future: from high-throughput screening of many agents to

genomic profiling analysis of the selected few

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

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