Cancer biology, toxicology and alternative methods development go hand-in-hand.

Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics-based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics-aided, cell-based, high-throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer-driven evaluation of toxicity data into meaningful results. Future 'cancer research-inspired knowledge management' of toxicological data will aid the translation of basic discovery results and chemicals- and materials-testing data to information relevant to human health and environmental safety.

[1]  Pierre R. Bushel,et al.  CEBS—Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data , 2007, Nucleic Acids Res..

[2]  Susan Hester,et al.  Utilizing toxicogenomic data to understand chemical mechanism of action in risk assessment. , 2013, Toxicology and applied pharmacology.

[3]  Bengt Fadeel,et al.  Nanotoxicology: no small matter. , 2010, Nanoscale.

[4]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[5]  C. Austin,et al.  Improving the Human Hazard Characterization of Chemicals: A Tox21 Update , 2013, Environmental health perspectives.

[6]  Lang Tran,et al.  ITS-NANO - Prioritising nanosafety research to develop a stakeholder driven intelligent testing strategy , 2014, Particle and Fibre Toxicology.

[7]  Min Zhang,et al.  A decade of toxicogenomic research and its contribution to toxicological science. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.

[8]  Anne E. Trefethen,et al.  Toward interoperable bioscience data , 2012, Nature Genetics.

[9]  Andreas Zell,et al.  InCroMAP: integrated analysis of cross-platform microarray and pathway data , 2012, Bioinform..

[10]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[11]  T. Golub,et al.  A method for high-throughput gene expression signature analysis , 2006, Genome Biology.

[12]  Liang Zhao,et al.  Mapping the human toxome by systems toxicology. , 2014, Basic & clinical pharmacology & toxicology.

[13]  Shu-Dong Zhang,et al.  Application of connectivity mapping in predictive toxicology based on gene-expression similarity. , 2010, Toxicology.

[14]  Roland C Grafström,et al.  Bioinformatics processing of protein and transcript profiles of normal and transformed cell lines indicates functional impairment of transcriptional regulators in buccal carcinoma. , 2007, Journal of proteome research.

[15]  F. Collins,et al.  Transforming Environmental Health Protection , 2008, Science.

[16]  R. Freshney Comprar Culture of Animal Cells: A Manual of Basic Technique and Specialized Applications | R. Ian Freshney | 9780470528129 | Wiley , 2010 .

[17]  Mathieu Vinken,et al.  The adverse outcome pathway concept: a pragmatic tool in toxicology. , 2013, Toxicology.

[18]  Manuel C. Peitsch,et al.  Systems Toxicology: From Basic Research to Risk Assessment , 2014, Chemical research in toxicology.

[19]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2013 , 2012, Nucleic Acids Res..

[20]  Anna Zhukova,et al.  Modeling sample variables with an Experimental Factor Ontology , 2010, Bioinform..

[21]  Diego di Bernardo,et al.  Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis , 2014, Bioinform..

[22]  Atul J. Butte,et al.  Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges , 2012, PLoS Comput. Biol..

[23]  Emilio Benfenati,et al.  The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing , 2013, Molecular informatics.

[24]  Melvin E. Andersen,et al.  Toxicogenomics for transcription factor-governed molecular pathways: moving on to roles beyond classification and prediction , 2012, Archives of Toxicology.

[25]  Ola Spjuth,et al.  Computational toxicology using the OpenTox application programming interface and Bioclipse , 2011, BMC Research Notes.

[26]  A. Brazma,et al.  Reuse of public genome-wide gene expression data , 2012, Nature Reviews Genetics.

[27]  Bin Chen,et al.  The ChEMBL database as linked open data , 2013, Journal of Cheminformatics.

[28]  Justin Lamb,et al.  The Connectivity Map: a new tool for biomedical research , 2007, Nature Reviews Cancer.

[29]  Nisha S. Sipes,et al.  In vitro and modelling approaches to risk assessment from the U.S. Environmental Protection Agency ToxCast programme. , 2014, Basic & clinical pharmacology & toxicology.

[30]  William C. Hahn,et al.  Towards systematic functional characterization of cancer genomes , 2011, Nature Reviews Genetics.

[31]  Joshua C. Gilbert,et al.  An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules , 2013, Cell.

[32]  Michael S. Ewer,et al.  Cardiotoxicity of anticancer treatments: what the cardiologist needs to know , 2010, Nature Reviews Cardiology.

[33]  L. O’Driscoll,et al.  Three-dimensional cell culture: the missing link in drug discovery. , 2013, Drug discovery today.

[34]  O. Kallioniemi,et al.  High-Throughput Cell-Based Screening of 4910 Known Drugs and Drug-like Small Molecules Identifies Disulfiram as an Inhibitor of Prostate Cancer Cell Growth , 2009, Clinical Cancer Research.

[35]  Kevin C. Dorff,et al.  The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models , 2010, Nature Biotechnology.

[36]  E. Lundberg,et al.  Towards a knowledge-based Human Protein Atlas , 2010, Nature Biotechnology.

[37]  S. Leivonen,et al.  Systematic analysis of microRNAs targeting the androgen receptor in prostate cancer cells. , 2011, Cancer research.

[38]  Kenji Mizuguchi,et al.  Toxygates: interactive toxicity analysis on a hybrid microarray and linked data platform , 2013, Bioinform..

[39]  Melvin E. Andersen,et al.  New directions in toxicity testing. , 2011, Annual review of public health.

[40]  H. Yamada,et al.  The Japanese toxicogenomics project: application of toxicogenomics. , 2010, Molecular nutrition & food research.

[41]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[42]  Alexander Golbraikh,et al.  Integrative chemical-biological read-across approach for chemical hazard classification. , 2013, Chemical research in toxicology.

[43]  Hilda Witters,et al.  A European perspective on alternatives to animal testing for environmental hazard identification and risk assessment. , 2013, Regulatory toxicology and pharmacology : RTP.

[44]  David M. Reif,et al.  Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System , 2012, International journal of molecular sciences.

[45]  X. Lu,et al.  LTMap: a web server for assessing the potential liver toxicity by genome‐wide transcriptional expression data , 2014, Journal of applied toxicology : JAT.

[46]  P. Bushel,et al.  The evolution of bioinformatics in toxicology: advancing toxicogenomics. , 2011, Toxicological sciences : an official journal of the Society of Toxicology.

[47]  K. Iljin,et al.  Differentiation-promoting culture of competent and noncompetent keratinocytes identifies biomarkers for head and neck cancer. , 2012, The American journal of pathology.

[48]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[49]  Roland C Grafström,et al.  The Application of Normal, SV40 T-antigen-immortalised and Tumour-derived Oral Keratinocytes, under Serum-free Conditions, to the Study of the Probability of Cancer Progression as a Result of Environmental Exposure to Chemicals , 2007, Alternatives to laboratory animals : ATLA.

[50]  A E Nel,et al.  Implementation of alternative test strategies for the safety assessment of engineered nanomaterials , 2013, Journal of internal medicine.

[51]  Raymond K. Auerbach,et al.  The real cost of sequencing: higher than you think! , 2011, Genome Biology.

[52]  R. Shoemaker The NCI60 human tumour cell line anticancer drug screen , 2006, Nature Reviews Cancer.

[53]  Andreas Luch,et al.  Wind of Change Challenges Toxicological Regulators , 2012, Environmental health perspectives.

[54]  Amar Koleti,et al.  Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS) , 2014, Journal of biomolecular screening.

[55]  Laura N. Vandenberg,et al.  Hormones and endocrine-disrupting chemicals: low-dose effects and nonmonotonic dose responses. , 2012, Endocrine reviews.

[56]  Saura C. Sahu,et al.  Handbook of systems toxicology , 2011 .

[57]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[58]  Jaeyun Sung,et al.  Molecular signatures from omics data: From chaos to consensus , 2012, Biotechnology journal.

[59]  Melvin E. Andersen,et al.  Incorporating New Technologies Into Toxicity Testing and Risk Assessment: Moving From 21st Century Vision to a Data-Driven Framework , 2013, Toxicological sciences : an official journal of the Society of Toxicology.

[60]  Pantelis Sopasakis,et al.  Collaborative development of predictive toxicology applications , 2010, J. Cheminformatics.

[61]  P. Fenner-Crisp,et al.  Advancing human health risk assessment: Integrating recent advisory committee recommendations , 2013, Critical reviews in toxicology.

[62]  S. Nishizuka,et al.  Reverse-phase protein lysate microarrays for cell signaling analysis , 2008, Nature Protocols.

[63]  S. Ramaswamy,et al.  Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.

[64]  M. Fielden,et al.  Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. , 2005, Journal of biotechnology.

[65]  Martin Fritts,et al.  ISA-TAB-Nano: A Specification for Sharing Nanomaterial Research Data in Spreadsheet-based Format , 2013, BMC Biotechnology.