99,99 €
An overview of the different approaches to cancer risk assessment of environmental factors - including "-omics" technologies, discussing the strengths and weaknesses of the methods in different fields. The main focus is on the carcinogenic effects of ionizing and non-ionizing radiation, demonstrating the difficulties in accurately assessing those factors that may or may not pose a significant cancer risk. The book extends the view to a broader context of risk assessment, highlighting various aspects of risk management. Written by leading experts in the field, this is a resource for policy makers and professionals in health risk assessment, and public health workers, as well as oncologists and researchers in academia.
This title is also available as a mobile App from MedHand Mobile Libraries. Buy it now from Google Play or the MedHand Store.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 701
Veröffentlichungsjahr: 2011
Contents
Cover
Related Titles
Title Page
Copyright
Preface
List of Contributors
Chapter 1: Introduction
References
Part One: Models and Approaches
Chapter 2: Models of Cancer Development: Genetic and Environmental Influences
2.1 Introduction
2.2 Specific Characteristics of Tumors
2.3 Tumorigenesis as a Multistep Process
2.4 Epigenetic Changes in Cancer Development
2.5 miRNAs and Cancer
2.6 Cancer Stem Cells
2.7 Cancer and the Environment
2.8 Systems Analysis of Cancer
2.9 Outlook
References
Chapter 3: Endogenous DNA Damage and Its Relevance for the Initiation of Carcinogenesis
3.1 Introduction
3.2 Types and Generation of Oxidative DNA Modifications
3.3 Repair of Endogenous DNA Modifications
3.4 Basal Steady-State Levels
3.5 Contribution of Endogenous DNA Modifications to Cancer Risk
References
Chapter 4: The IARC Monographs Programme: Cancer Hazard Identification as a First Step in Cancer Risk Assessment and Cancer Prevention
4.1 Introduction
4.2 Formaldehyde, Nasopharyngeal Cancer, and Leukemia: Evolution in Evaluation
4.3 Herbal Medicines, Aristolochia Plant Species, and Aristolochic Acid Nephropathy
4.4 Concluding Remarks
Acknowledgment
References
Part Two: Epidemiological Research
Chapter 5: The Role of Epidemiology in Cancer Risk Assessment of Nonionizing Radiation
5.1 Introduction
5.2 Brief Outline of Common Epidemiological Study Designs
5.3 Criteria for Evaluating the Plausibility of Epidemiological Findings
5.4 Bias and Errors in Epidemiological Studies
5.5 Compatibility between Study Findings and Time Trends in the Occurrence of Disease
5.6 Discussion
References
Chapter 6: The Role of Epidemiology in Cancer Risk Assessment of Ionizing Radiation
6.1 Introduction
6.2 Japanese Atomic Bomb Survivors
6.3 Medical Exposures
6.4 Occupational Exposures
6.5 Environmental Exposures
6.6 Conclusions
References
Part Three: Animal Studies
Chapter 7: Animal Studies on RF EMF Cancer Effects
7.1 Introduction
7.2 Exemplary Carcinogenicity Studies Testing the Possible Health Effects Related to Mobile Telephones and Base Stations (PERFORM-A)
7.3 Research Gaps
7.4 Proposed Research Strategy
7.5 Summary
References
Chapter 8: Animal Studies in Carcinogen Identification: The Example of Power Frequency (50/60 Hz) Magnetic Fields
8.1 Introduction
8.2 Strengths and Limitations of Epidemiology Studies of EMF as a Cancer Hazard
8.3 Strengths and Limitations of Experimental Studies of EMF as a Cancer Hazard
8.4 Role of Mechanistic Studies in EMF Hazard Assessment
8.5 Oncogenicity Studies of EMF
8.6 Conclusions
References
Part Four: Genotoxicity Studies
Chapter 9: Chromosomal Aberrations in Human Populations and Cancer
9.1 Introduction
9.2 Chromosomal Aberrations and Their Spontaneous Frequencies in Human Peripheral Lymphocytes
9.3 Micronuclei
9.4 Sister Chromatid Exchanges
9.5 Age Dependency of CA, MN, and SCE
9.6 Origin of CA in HPL
9.7 Ionizing Radiation and Chromosomal Aberrations
9.8 CA and Cancer in Human Populations
References
Chapter 10: Cytogenetic Studies in Mammalian Somatic Cells Exposed to Radio Frequency Radiation: A Meta-Analysis
10.1 Introduction
10.2 Materials and Methods
10.3 Results
10.4 Cytogenetic Endpoints as Biomarkers for Cancer Risk
10.5 Perspective from Meta-Analysis and Conclusions
References
Part Five: Omics: A New Tool for Cancer Risk Assessment?
Chapter 11: Genomics and Cancer Risk Assessment
11.1 Introduction
11.2 Tissue Material
11.3 Analysis Technologies
11.4 Outlook for Individualized Cancer Treatment
References
Chapter 12: Transcriptomics and Cancer Risk Assessment
12.1 Introduction
12.2 Sample Preparation, Technical Issues, and Data Analysis
12.3 Conclusions
References
Chapter 13: Proteomics and Cancer Risk Assessment
13.1 Introduction
13.2 Sample Preparation and Storage: A Challenge in Clinical Settings
13.3 Caveats and Hurdles in Protein Analysis Using Cancer Specimens and Clinical Samples
13.4 Separation and Fractionation of Protein Mixtures as a Prerequisite to Proteomic Analyses and Protein Quantification
13.5 Identification of Proteins by Mass Spectrometry
13.6 Array-Based Proteome Technology in Cancer Research
13.7 The Present and the Future: Proteomics for Individualized Cancer Therapy
References
Part Six: Current Use of Omics Studies for Cancer Risk Assessment
Chapter 14: Omics in Cancer Risk Assessment: Pathways to Disease
14.1 Introduction
14.2 “Omics” Data in Cancer Risk Assessment
14.3 High-Throughput Screening
14.4 Discussion
References
Chapter 15: What Have “Omics” Taught Us about the Health Risks Associated with Exposure to Low Doses of Ionizing Radiation
15.1 Introduction
15.2 Pre-“Omics”
15.3 Functional Genomics
15.4 Gene Expression Profiling for Nontargeted Effects Induced by Exposure to Ionizing Radiation
15.5 Gene Expression Profiling for Adaptive Responses Induced by Exposure to Ionizing Radiation
15.6 In Vivo Gene Profiling after Irradiation
15.7 Radiation-Induced Oscillatory Signaling
15.8 Proteomic Profiling after Exposure to Ionizing Radiation
15.9 Metabolomic Profiling after Exposure to Ionizing Radiation
15.10 Conclusions
Acknowledgment
References
Chapter 16: Transcriptomics Approach in RF EMF Research
16.1 Introduction
16.2 Transcriptomics in RF EMF Research
16.3 Discussion
References
Chapter 17: Proteomics Approach in Mobile Phone Radiation Research
References
Part Seven: Challenges for Risk Management
Chapter 18: Evaluating the Reliability of Controversial Scientific Results
18.1 Introduction
18.2 Detection of Scientific Misconduct
18.3 Committee on Publication Ethics
18.4 Conclusions
References
Chapter 19: Comparative Risk Assessment with Ionizing and Nonionizing Radiations
19.1 Introduction
19.2 Review of Different Radiation Types
19.3 Discussion
References
Chapter 20: Communicating about Uncertainties in Cancer Risk Assessment
20.1 Introduction
20.2 The Concept of Uncertainty
20.3 Reasons for Communicating Uncertainties
20.4 Findings on Communicating Uncertainties
20.5 Explaining Inconclusive Evidence
20.6 Conclusions
References
Chapter 21: The Precautionary Principle and Radio Frequency Exposure from Mobile Phones
21.1 Introduction
21.2 Background on the Precautionary Principle
21.3 Pros and Cons of the Precautionary Principle
21.4 Applying the Precautionary Principle to Radio Frequency Electromagnetic Fields
21.5 Conclusions
References
Index
Related Titles
Hsu, C.-H., Stedeford, T. (eds.)
Cancer Risk Assessment
Chemical Carcinogenesis, Hazard Evaluation, and Risk Quantification
2010
ISBN: 978-0-470-23822-6
Wiedemann, P. M., Schütz, H. (eds.)
The Role of Evidence in Risk Characterization
Making Sense of Conflicting Data
2008
ISBN: 978-3-527-32048-6
The Editors
Prof. Dr. Günter Obe
Ret. from University Duisburg-Essen
Present address:
Gershwinstrasse 33
14513 Teltow
Germany
Dr. Burkhard Jandrig
Max-Delbrück-Center
for Molecular Medicine (MDC)
Robert-Rössle-Str. 10
13125 Berlin
Germany
Prof. Dr. Gary E. Marchant
S. Day O.Connor College of Law
Arizona State University
Tempe, AZ 85287-796
USA
Dipl. Päd. Holger Schütz
Research Center Jülich, Inst.
of Neuroscience and Medicine (INM-8)
52425 Jülich
Germany
Prof. Dr. Peter M. Wiedemann
Karlsruher Institut für Technologie (KIT)
TAB – Büro für Technikfolgen-
Abschätzung beim Deutschen Bundestag
Neue Schönhauser Straße 10
10178 Berlin
Germany
Cover
Microscopic Cancer Cell # PhotoDisc/Getty Images
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty can be created or extended by sales representatives or written sales materials. The Advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Card No.: applied for
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
Bibliographic information published by the Deutsche Nationalbibliothek
The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de.
© 2011 Wiley-VCH Verlag & Co. KGaA,
Boschstr. 12, 69469 Weinheim, Germany
Wiley-Blackwell is an imprint of John Wiley & Sons, formed by the merger of Wiley's global Scientific, Technical, and Medical business with Blackwell Publishing.
All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.
ISBN: 978-3-527-32753-9
ePDF ISBN: 978-3-527-63462-0
ePub ISBN: 978-3-527-63463-7
Mobi ISBN: 978-3-527-63464-4
oBook ISBN: 978-3-527-63461-3
Preface
Both the modern biomedical research such as genomics and proteomics and the rapid advances in high-throughput screening molecular technologies have revolutionized the knowledge about functional and regulatory genomics, which is beginning to make an immense impact on our understanding of human health and disease. These developments have also brought great hope to improve cancer risk assessment, even to solve scientific controversies about cancer risk claims, such as the debate whether electromagnetic fields from mobile telephony cause cancer in humans.
During the past few years, we were able to focus on this question in an integrated multidisciplinary research project on the implications of modern biomedicine on risk assessment (IMBA), sponsored by the Helmholtz Association of German Research Centres. As a health technology assessment project, IMBA analyzed how new developments in biomedicine, which are often summarized under the term “toxicogenomics,” will transform the present risk management framework. IMBA looked into a wide range of scientific and social challenges that deserve careful attention, particularly on issues related to risk assessment, risk perception, and risk communication.
In 2008, we organized an international workshop in Berlin as part of the IMBA project. The aim of the workshop was to compare the potential of genomics and traditional approaches used in cancer risk assessment, particularly genotoxicity studies, with regard to their potential to inform assessment of unclear risks, that is, risks where evidence is insufficient for a conclusive risk assessment. The unclear risks chosen for discussion were radio frequency electromagnetic fields. Topics such as the validity and reliability of genotoxic research for cancer risk assessment, the prospects of toxicity testing and risk assessment, and the implications for policy making were critically reviewed and evaluated by experts in the fields of ionizing and nonionizing radiation, genotoxicity, molecular medicine, and epidemiology.
The discussions during the workshop motivated us to plan a publication on these topics. Further impetus came from the ongoing societal debate on the health implications of electromagnetic fields, which seems not to be solved but stimulated by new molecular biomarker studies and high-throughput technologies in this field. We think that in a climate of excitement about the promises of molecular medicine, it is crucial to explore the validity of molecular biomarkers and evaluate their added value for risk assessment. We hope that this book will contribute to effective interdisciplinary communication and collaboration in the fields of molecular biology, cancer research, risk assessment, and public health policy.
We are grateful to all authors of the book for investing their valuable time in writing their contributions and participating in the review process in order to make the book valuable for all readers. Last but not least, we appreciative the support of the Helmholtz Association of German Research Centres.
Günter Obe, Burkhard Jandrig,
Gary E. Marchant, Holger Schütz,
Peter M. Wiedemann
Berlin, December 2010
List of Contributors
Robert A. Baan
WHO–International Agency for Research on Cancer
The IARC Monographs Programme
150, cours Albert Thomas
69372 Lyon Cedex 08
France
Gabriele Berg-Beckhoff
Unit for Health Promotion Research
Institut of Public Health
University of Southern Denmark
Niels Bohrs Vej 9
6700 Esbjerg
Denmark
Maria Blettner
Johannes Gutenberg-University of Mainz
Institute of Medical Biostatistics,
Epidemiology, and Informatics
Obere Zahlbacher Straße 69
55101 Mainz
Germany
Jochen Buschmann
Fraunhofer Institute for Toxicology and Experimental Medicine
Department of Toxicology & Environmental Hygiene
Nikolai-Fuchs-Strasse 1
30625 Hannover
Germany
Vincent J. Cogliano
Acting Director, Integrated Risk
Information System (IRIS)
National Center for Environmental Assessment
U.S. Environmental Protection Agency
1200 Pennsylvania Ave NW (8601P)
Washington DC 20460
USA
Clemens Dasenbrock
Fraunhofer Institute for Toxicology and Experimental Medicine
Department of Toxicology & Environmental Hygiene
Nikolai-Fuchs-Strasse 1
30625 Hannover
Germany
Marco Durante
GSI Helmholzzentrum für Schwerionenforschung
Biophysics Department
Planckstrasse 1
64291 Darmstadt
Germany
and
Technical University of Darmstadt
Department of Condensed Matter Physics
Hochschulstraße 3
46289 Darmstadt
Germany
Bernd Epe
University of Mainz
Institute of Pharmacy and Biochemistry
Staudingerweg 5
55128 Mainz
Germany
Markus Fußer
University of Mainz
Institute of Pharmacy and Biochemistry
Staudingerweg 5
55128 Mainz
Germany
Burkhard Jandrig
Max Delbrück Center for Molecular Medicine
Robert-Rössle-Str. 10
13125 Berlin
Germany
Wolfgang Kemmner
Max Delbrück Center for Molecular Medicine
Experimental and Clinical Research
Center (ECRC)
Research Group Surgical Oncology
Robert-Rössle-Str. 10
13125 Berlin
Germany
Jürgen Kiefer
Universität Giessen
Am Dornacker 4
35435 Wettenberg
Germany
Alexander Lerchl
Jacobs University Bremen
School of Engineering and Science,
Research II
Campus Ring 6
28759 Bremen
Germany
Dariusz Leszczynski
STUK – Radiation and Nuclear Safety
Authority
Laippatie 4
00881 Helsinki
Finland
David C. Lloyd
Health Protection Agency
Chilton
Didcot OX11 0RQ
UK
Gary E. Marchant
Arizona State University Sandra
Day O.Connor College of Law
P.O. Box 877906
Tempe, AZ 85287-796
USA
David L. McCormick
IIT Research Institute
10 West 35th Street
Chicago, IL 60616
USA
Meike Mevissen
University of Bern Vetsuisse Faculty
Department of Clinical Research and Veterinary Public Health
Division Veterinary Pharmacology and Toxicology
Länggassstrasse 124
3012 Bern
Switzerland
William F. Morgan
Pacific Northwest National Laboratory
Cell Biology and Biochemistry
P.O. Box 999, MSIN P7-56
Richland, WA 99354
USA
Günter Obe
Ret. from University Duisburg-Essen
Present address:
Gershwinstrasse 33
14513 Teltow
Germany
Christopher J. Portier
National Center for Environmental
Health/Agency for Toxic Substances
and Disease Registry
Centers for Disease Control
and Prevention
1600 Clifton Road
Atlanta, GA 30333
Thomas J. Prihoda
University of Texas Health Science Center
Department of Pathology
San Antonio, TX 78229
USA
Brigitte Schlehofer
German Cancer Research Centre
Unit of Environmental Epidemiology
Im Neuenheimer Feld 280
69120 Heidelberg
Germany
Alexander Schramm
Universitätsklinikum Essen, Pädiatrie III
Onkologisches Labor
Hufelandstr. 55
45122 Essen
Germany
Holger Schütz
Research Center Jülich
Institute of Neuroscience and Medicine (INM-8)
52425 Jülich
Germany
Joachim Schüz
International Agency for Research on Cancer (IARC)
Section of Environment and Radiation
150, cours Albert Thomas
69372 Lyon Cedex 08
France
Michal R. Schweiger
Max-Planck Institute for Molecular Genetics
Department of Vertebrate Genomics
Ihnestrasse 63–73
14195 Berlin
Germany
Marianne B. Sowa
Pacific Northwest National Laboratory
Cell Biology and Biochemistry
P.O. Box 999, MSIN P7-56
Richland, WA 99354
USA
Reuben Thomas
National Institute of Environmental Health Sciences
Laboratory of Toxicology and Pharmacology
Environmental Systems Biology
P.O. Box 12233, MD B2-08
Research Triangle Park, NC 27709
USA
Bernd Timmermann
Max-Planck Institute for Molecular Genetics
Ihnestrasse 63–73
14195 Berlin
Germany
Vijayalaxmi
University of Texas Health Science Center
Department of Radiology
San Antonio, TX 78229
USA
Richard Wakeford
The University of Manchester
Dalton Nuclear Institute
Pariser Building – G Floor
P.O. Box 88, Sackville Street
Manchester M60 1QD
UK
Peter M. Wiedemann
Karlsruher Institut für Technologie (KIT)
TAB – Büro für Technikfolgen-Abschätzung beim Deutschen Bundestag
Neue Schönhauser Straße 10
10178 Berlin
Germany
Chapter 1
Introduction
Cancer is one of the leading causes of human mortality. Over the past 30 years, the global burden of cancer has more than doubled. According to the recent World Cancer Report, published by the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), in 2008 there were 7 million deaths from cancer. Affected by the still growing and aging world population, this figure is expected to increase to 17 million annually by 2030 [1]. While many environmental cancer risk factors, such as exposures to ionizing radiation or tobacco smoke, alcohol consumption, or excessive sun exposure, have been established [2], assessments of cancer hazards and risks are difficult and often highly uncertain. Of the more than 900 agents that have been evaluated by IARC, only 12% have been classified as being clearly carcinogenic to humans [3]. And even if an agent has been identified as a carcinogen, the risk it poses to a given population is often hard to estimate. The reasons for these difficulties are manifold. First of all, there are different types of cancer that differ in their etiology. Another reason – and that is the focus of this book – is that cancer causation is hard to investigate. Experimental studies in humans are for obvious ethical reasons not possible, thus cancer risk assessment has to rely on indirect evidence.
At present, assessments of carcinogenicity are based on three pillars: epidemiological studies in humans, studies in experimental animals, and genotoxicity studies. Epidemiological studies aim at identifying the causes of cancer by studying the covariation between exposure to an agent and cancer incidence. Although there is a long debate on if and when epidemiology actually can provide causal evidence [4], there is little disagreement that epidemiological studies are the most important source of knowledge for cancer risk assessment [2, 5]. In studying the carcinogenicity of agents, epidemiological studies have to rely on given exposures to the respective agents, for instance, radon emanating from the soil or electromagnetic fields emitted from mobile communication devices. These conditions are usually not under control of the investigators, and although epidemiologists have developed an elaborate methodology to match specific study demands [6], problems such as bias and confounding frequently limit the conclusiveness of their results.
Compared to epidemiology, animal studies have the advantage of permitting experimental designs, where (at least in principle) everything can be controlled. This allows the most stringent test of a causal relationship between the exposure to an agent and an adverse effect. At least for chemical agents, there is a kind of “gold standard” that is used for carcinogenicity testing, which includes 2-year studies with rodents [7]. However, these studies are time consuming and expensive, limiting the number of agents that are tested [2]. Beside ethical considerations regarding the use of animals in research, the appropriateness of animal models for investigating and predicting human diseases has been disputed [8]. It should also be noted that this gold standard is not so well established for some physical agents. For example, many animal studies investigating the potential carcinogenicity of radio frequency electromagnetic fields (RF EMF) use only one type of animals and often for a short period [9]. An important limitation of using animal studies for carcinogenicity testing is that the experimental results always have to be extrapolated to humans, which is of course acknowledged in evaluations of evidence for cancer risk assessment [2, 5].
Basically, the same holds for genotoxicity studies, where experimental findings also have to be evaluated with regard to their implications for humans. Their value lies in the fact that cancer results primarily from genetic changes in single cells. Therefore, agents that are able to damage cellular DNA lead to mutations and then possibly to cancer. For instance, people exposed to ionizing radiation have both an elevated cancer risk and elevated frequencies of chromosomal aberrations in their peripheral lymphocytes, showing the mutagenic activity of ionizing radiation. Mutations are initiating events for the development of cancer and therefore testing of various agents for their possible mutagenicity is an important part of cancer risk assessment [10].
Over the past years, new technologies have been developed that promise new insight into cancer risk assessment by focusing on the role of the genome for understanding cancer initiation and development [11, 12]. These so-called omics technologies include genomics for DNA variations, transcriptomics for messenger RNA, proteomics for peptides and proteins, and metabolomics for intermediate products of metabolism. Technological breakthroughs allow simultaneous examination of thousands of genes, transcripts, proteins, and metabolites with high-throughput techniques and analytical tools to extract information. These new technologies are expected to provide a highly sensitive detection of low-dose effects, more reliable extrapolation of risk estimates across doses, routes, and species, and valuable insight into the mechanism of action of toxicants. Overall, the ability to classify chemicals and other stressors based on their effects at omics level would permit the development of new testing strategies in cancer risk assessment. At present, genomics- and transcriptomics-based approaches are most promising, while metabolomics, though in principle quite potent, is quite nascent in its development, as present techniques and the methodology are far away from inspecting the whole metabolome. High-throughput screening technologies have their own technical limitations and uncertainties. The transcriptome and proteome are highly dynamic and change rapidly and dramatically in response to perturbations or even during normal cellular events. The modern screening technologies still have the problem of reproducibility and variability between studies and are prone to produce false positive results [13, 14].
An important aspect here is quality control of scientific investigations. Although in general not limited to the omics field, the huge amount of data produced with microarray experiments and the extensive data processing required for analysis make open data accessibility to allow independent reevaluation of findings an important claim, which is increasingly acknowledged in the scientific community [15, 16]. Another aspect of quality control is how to evaluate the reliability of controversial scientific results. As said before, it is difficult to rule out errors in high-throughput screening research. Even more complicated is the proper dealing with fraud suspicions. Although fraud in science is by no means a new phenomenon, recent scandals in highly prestigious scientific journals have also called the public's attention to this issue [17]. Thus, the highly welcome new approaches to cancer risk assessment also call for the establishment of rules that allow a careful evaluation of study results. Furthermore, better risk communication is required for informing health professionals, the media, and the general public about the meaning of omics findings for risk assessment [18]. A particular problem here is if and when uncertainties in risk assessment should be communicated to a nonexpert audience. On a more general level, the question arises how these uncertainties should be addressed in risk management. This is likely to intensify the current debate about the application of the precautionary principle. Of course, these problems are not specific to omics; however, apart from providing new knowledge for risk assessment, omics is also likely to introduce new uncertainties [19–21].
The following chapters of this book provide insight into new developments of cancer risk assessment and their accompanying scientific discussions. While the focus is on cancer and radiation, especially nonionizing radiation, the various chapters provide the reader with a comprehensive view on cancer biology, cancer assessment methods including epidemiology, animal research, and genotoxicity studies as well as omics approaches and applications. Furthermore, it covers the comparative assessment of radiation risks and addresses policy considerations such as risk communication and application of the precautionary principle.
The book is organized in seven parts. Part One gives an overview of the current understanding of cancer development and approaches to cancer risk assessment. Jandrig (Chapter 2) shows that, apart from mutations, other cellular changes have to be taken into account to understand the complex biology of cancer. Epe and Fußer (Chapter 3) describe the various determinants of generation, repair, and steady-state levels of endogenous DNA modifications. Baan and Cogliano (Chapter 4) provide insight into cancer hazard identification as the first step in cancer risk assessment and cancer prevention, as outlined in the IARC Monographs Programme.
The role of epidemiology in cancer risk assessment is addressed in Part Two. Schüz, Berg-Beckhoff, Schlehofer, and Blettner (Chapter 5) consider the particularly challenging possible adverse health effects of exposure to electromagnetic fields (EMF) that have remained a scientific and political controversy until today. Their first example is the relationship between extremely low-frequency (ELF) fields from power lines and the risk of childhood leukemia. Their second example is the relationship between RF EMF, specifically those emitted from mobile phones, and the risk of brain tumors. Wakeford (Chapter 6) presents data for cancer risk assessment of ionizing radiation. Among others, he provides cancer risk figures based on epidemiology from Hiroshima survivors and children exposed during and after the Chernobyl accident.
Animal studies are indispensable for cancer hazard identification and results of this type of research are presented in Part Three. Buschmann and Dasenbrock (Chapter 7) refer to recent advances in animal studies on RF EMF testing the possible carcinogenic effects related to cell phones and base stations. On the basis of a comprehensive discussion of the PERFORM-A project, they demonstrate how existing data gaps relevant for risk assessment can be closed. Pointing to the strengths and limitations of epidemiological cancer studies of ELF fields, McCormick (Chapter 8) shows how laboratory animal research can fill gaps in EMF cancer risk assessment. The author discusses the findings of various types of experimental animal studies and comes to the conclusion that available animal data do not support an elevated cancer risk.
Part Four highlights the importance of studying chromosomal damage, which is a highly reliable endpoint for cancer hazard and risk assessment. Obe, Lloyd, and Durante (Chapter 9) outline current approaches to investigating chromosomal aberrations. They argue that elevated frequencies of chromosomal aberrations in peripheral lymphocytes of human populations are associated with elevated cancer frequencies and allow calculation of cancer risks in persons exposed to ionizing radiation, such as astronauts. Vijayalaxmi and Prihoda (Chapter 10) show how meta-analysis as a tool for statistical data synthesis can be used to systematically summarize evidence from cytogenetic studies in mammalian somatic cells that have been exposed to radio frequency radiation. They conclude that exposure to radio frequency radiation does not increase frequencies of chromosomal aberrations and micronuclei, which are two endpoints for chromosomal damage.
The potential of omics technologies as new tools for cancer risk assessment are discussed in Part Five. Technological breakthroughs allow simultaneous examination of thousands of genes, transcripts, proteins, and metabolites with high-throughput techniques and analytical tools to extract information. Modern screening technologies speed up the discovery process and give a broader insight into biochemical events that follow the exposure to potentially harmful agents, such as chemical substances, ionizing radiation, or electromagnetic fields. The different methodologies and techniques are discussed in this part with respect to actual applications and future developments. Schweiger and Timmermann (Chapter 11) explain the huge potential that whole genome approaches afford for understanding complex genetic diseases such as cancer. They provide an overview of the advancement of genome analysis technologies and illustrate how these are used for investigating the mechanisms underlying cancer development. The authors close with an outlook on how the genomics approach might ultimately lead to an individualized cancer treatment. Kemmner (Chapter 12) outlines the use of transcriptomics, or gene expression profiling, in cancer risk assessment, for instance, with regard to classification of human cancers and prediction of cancer recurrence and metastasis. The author discusses technical challenges of gene expression profiling, such as sample preparation and data analysis, and gives examples of microarray applications in cancer research. Proteomics, the analysis of proteins, and its relevance to cancer risk assessment, is discussed by Schramm (Chapter 13). While proteomics comprises a variety of technical disciplines, its application to cancer risk assessment can be described as a multistep process including sample preparation, separation, quantitation, and protein identification. The author discusses particular challenges of these steps and concludes with an outlook on future developments of proteomics for individualized cancer therapy.
Examples of using omics technologies for risk assessment are described in Part Six. Portier and Thomas (Chapter 14) provide a critical discussion of omics and high-throughput screening strategies concerning cancer risk assessment. First, they discuss the difficulties of traditional cancer risk assessment, in particular with animal studies, and then describe how omics might be used to overcome these problems. They conclude that while there is little doubt that omics will be of major importance for future risk assessment, there is still much research needed, before it finds regulatory approval in risk assessment. Morgan and Sowa (Chapter 15) show how omics might be used for risk assessment of exposure to low-level ionizing radiation. So far, risk assessment had to rely mainly on epidemiological data, for instance, from Japanese A-bomb survivors, but here epidemiology clearly reaches its limits. The authors discuss studies that used gene expression profiling, proteomic profiling, and metabolomic profiling to investigate the effects of low-level ionizing radiation. Their conclusion is that while significant progress has been made in using omics for cancer risk assessment, the future challenge is to integrate the various omics technologies to allow a “systems level” approach. The next two chapters then address how transcriptomics and proteomics can be used for cancer risk assessment of RF EMF. Mevissen (Chapter 16) provides an overview of studies investigating the effects of RF EMF exposure on gene expression. She makes it clear that these studies differ strongly in scientific quality and focus, and are insufficient for drawing conclusions regarding effects the RF EMF exposure has on organisms. A similar picture emerges from the review of proteomics studies that is given by Leszczynski (Chapter 17). So far, only few studies have investigated the effects of RF EMF exposure on the proteome, and many of them have methodological shortcomings.
The last part of the book addresses challenges for risk management. Lerchl (Chapter 18) reports recent examples of apparent scientific misconduct and discusses heuristics that can help detect data fabrication. He also offers some advice how to handle such misconduct appropriately. Kiefer (Chapter 19) offers a comparative risk assessment across the electromagnetic spectrum based on the Bradford Hill criteria. He argues that at present only ionizing radiation fulfils all requirements for cancer hazard identification. Wiedemann and Schütz (Chapter 20) discuss the challenges of communicating about uncertainty in cancer risk assessments to nonexperts. They offer ample evidence that, in contrast to common beliefs, informing about uncertainties might create misperceptions and misunderstandings of risk. Furthermore, they discuss how to explain inconclusive scientific evidence, a task particularly important for hazard assessment. Finally, Marchant (Chapter 21) considers the role of the precautionary principle in risk management. Weighing the pros and cons, he concludes that despite its rhetorical appeal, the precautionary principle remains problematic in its practical application, which in large part is due to the ambiguity and arbitrariness of the principle.
References
1. Boyle, P. and Levin, B. (eds) (2008) World Cancer Report 2008, International Agency for Research on Cancer, Lyon.
2. Fontham, E.T.H., Thun, M.J., Ward, E., Balch, A.J., Delancey, J.O.L., and Samet, J.M., on behalf of ACS Cancer and the Environment Subcommittee (2009) American Cancer Society perspectives on environmental factors and cancer. CA Cancer J. Clin., 59, 343–351.
3. IARC (2010) Agents Classified by the IARC Monographs, vols. 1–100, International Agency for Research on Cancer, Lyon. Available at http://monographs.iarc.fr/ENG/Classification/index.php (Accessed July 19, 2010).
4. Rothman, K.J. and Greenland, S. (2005) Causation and causal inference in epidemiology. Am. J. Public Health, 95, S144–S150.
5. International Agency for Research on Cancer (2006) Preamble to the IARC Monographs. IARC Monographs Programme on the Evaluation of Carcinogenic Risks to Humans, International Agency for Research on Cancer. Available at http://monographs.iarc.fr/ENG/Preamble/CurrentPreamble.pdf (Accessed July 26, 2010).
6. Rothman, K.J., Greenland, S., and Lash, T.L. (eds) (2008) Modern Epidemiology, 3rd edn, Lippincott Wilkins & Wilkins, Philadelphia, PA.
7. Fung, V.A., Barrett, J.C., and Huff, J. (1995) The carcinogenesis bioassay in perspective: application in identifying human cancer hazards. Environ. Health Perspect., 103, 680–683.
8. Hackam, D.G. and Redelmeier, D.A. (2006) Translation of research evidence from animals to humans. JAMA, 296, 1731–1732.
9. Dasenbrock, C. (2005) Animal carcinogenicity studies on radiofrequency fields related to mobile phones and base stations. Toxicol. Appl. Pharmacol., 207, 342–346.
10. Parsons, B.L., Myers, M.B., Meng, F., Wang, Y., and McKinzie, P.B. (2010) Oncomutations as biomarkers of cancer risk. Environ. Mol. Mutagen. doi: 10.1002/em.20600.
11. Bishop, W.E., Clarke, D.P., and Travis, C.C. (2001) The genomic revolution: what does it mean for risk assessment? Risk Anal., 21, 983–987.
12. Simmons, P.T. and Portier, C.J. (2002) Toxicogenomics: the new frontier in risk analysis. Carcinogenesis, 23, 903–905.
13. Troester, M.A., Millikan, R.C., and Perou, C.M. (2009) Microarrays and epidemiology: ensuring the impact and accessibility of research findings. Cancer Epidemiol. Biomarkers Prev., 18, 1–4.
14. Vlaanderen, J., Moore, L.E., Smith, M.T., Lan, Q., Zhang, L., Skibola, C.F., Rothman, N., and Vermeulen, R. (2010) Application of omics technologies in occupational and environmental health research: current status and projections. Occup. Environ. Med., 67, 136–143.
15. Kaye, J., Heeney, C., Hawkins, N., de Vries, J., and Boddington, P. (2009) Data sharing in genomics: re-shaping scientific practice. Nat. Rev. Genet., 10, 331–335.
16. Field, D., Sansone, S.-A., Collis, A., Booth, T., Dukes, P., Gregurick, S.K., Kennedy, K., Kolar, P., Kolker, E., Maxon, M., Millard, S., Mugabushaka, A.-M., Perrin, N., Remacle, J.E., Remington, K., Rocca-Serra, P., Taylor, C.F., Thorley, M., Tiwari, B., and Wilbanks, J. (2009) 'Omics data sharing. Science, 326, 234–236.
17. Science (2006) Special Online Collection: Hwang et al. Controversy. Available at http://www.sciencemag.org/sciext/hwang2005/ (Accessed September 10, 2010).
18. McBride, C.M., Bowen, D., Brody, L.C., Condit, C.M., Croyle, R.T., Gwinn, M., Khoury, M.J., Koehly, L.M., Korf, B.R., Marteau, T.M., McLeroy, K., Patrick, K., and Valente, T.W. (2010) Future health applications of genomics: priorities for communication, behavioral, and social sciences research. Am. J. Prev. Med., 38, 556–565.
19. Adelman, D.E. (2005) The false promise of the genomics revolution for environmental law. Harv. Environ. Law Rev., 29, 117–177.
20. Battershill, J.M. (2005) Toxicogenomics: regulatory perspective on current position. Hum. Exp. Toxicol., 24, 35–40.
21. Boverhof, D.R. and Zacharewski, T.R. (2006) Toxicogenomics in risk assessment: applications and needs. Toxicol. Sci., 89, 352–360.
Part One
Models and Approaches
Chapter 2
Models of Cancer Development: Genetic and Environmental Influences
Burkhard Jandrig
2.1 Introduction
The past decade has brought enormous advances in the understanding of the molecular pathogenesis of cancer. Cancer research has generated a prodigious amount of knowledge revealing cancer as a disease characterized by dynamic changes in the genome, at the expression level, and influenced by environmental factors. Deep insight could especially be achieved in the area of cancer genetics where the explosion of sequence and molecular profiling data elucidated the complexity of human malignancies. Several familial cancer genes with high-penetrance mutations could be identified. However, multigenic models suggest that a high proportion of cancers may arise as a consequence of the combined effects of common low-penetrance alleles and rare disease-causing variants that pose moderate cancer risks.
At the genetic level, oncogenes and tumor suppressor genes play prominent roles. Mainly mutations produce oncogenes with dominant gain of function and tumor suppressor genes with recessive loss of function. These genes have been intensely studied in human and animal cancer cells and in experimental models. It turned out that these genes are involved in a molecular machinery regulating proliferation, differentiation, and death. Similar mechanisms govern the transformation of normal cells into malignant cancers.
Tumorigenesis is a multistep process reflected by stochastic genetic alterations that drive a progressive transformation of normal cells via a series of premalignant states into highly malignant derivatives [1]. Genomes of tumor cells are altered at multiple sites, ranging from subtle point mutations to gross changes in chromosome number, size, or structure [2]. Experimental transformation of cultured cells into tumorigenic ones and transgenic animal models of tumorigenesis have repeatedly shown multiple rate-limiting steps, each conferring some kind of growth advantage [3].
Cancer cells often show defects in the so-called regulatory circuits that normally govern cell proliferation and homeostasis. A number of essential circuits dictate malignant growth (Figure 2.1) [4, 5]. Tumors are characterized by acquired novel capabilities such as self-sufficiency in growth signals, insensitivity to growth inhibitory (antigrowth) signals, evasion of programmed cell death (apoptosis), limitless replicative potential, altered cell metabolism, sustained angiogenesis, evasion of immune response, tissue invasion, and metastasis.
Figure 2.1 Specific characteristics (hallmarks) of cancer (adapted from Refs [4, 5]).
2.2 Specific Characteristics of Tumors
Normal cells require stimulatory growth signals before they can switch from a quiescent into a proliferative state. Signaling molecules such as diffusible growth factors, extracellular matrix components, or cell-to-cell adhesion molecules bind to transmembrane receptors leading to conformational changes and activation of signal transduction pathways. Many of the so far known oncogenes have a place in these pathways and foster unregulated growth signaling. Tumor cells often show a greatly reduced dependence on exogenous stimulation by generating many of their own growth signals, thereby creating an autocrine stimulation and disrupting important homeostatic mechanisms [6–8].
Cell surface receptors that transduce growth stimulatory signals into the cell are other targets of deregulation during tumor pathogenesis. In many tumors, growth factor receptors are overexpressed or truncated enabling the cancer cells to become hyperresponsive to growth factors or to elicit ligand-independent signaling [9].
In addition, tumor cells can also switch the expression of integrin receptors in favor of progrowth signals [10]. These receptors physically link cells to the extracellular matrix. Successful binding to specific parts of the matrix can influence cell behavior, resistance to apoptosis, or activation of the cell cycle. In addition, signaling between the diverse cell types within a tumor, especially from the stromal cell components of the tumor mass, may enhance the tumor growth potential.
Signals emitted by ligand-activated growth factor receptors and integrins can activate different cytoplasmic pathways, most prominent the ras-raf-MAP kinase pathway. The signal transduction cascades are often linked to other pathways creating a cross-talking connection network.
Tissue homeostasis is mainly maintained by growth inhibitory signals blocking proliferation and by controlling the cell cycle clock. Tumor cells have to switch off such impediments by specifically altered components that govern the transit of the cell through the G1 phase of its growth cycle. At the molecular level, almost all antiproliferative signals function through the retinoblastoma protein (pRb) and its relatives p107 and p130. Disruption of the pRb pathway leads to an insensitivity to antigrowth factors, inter alia activates E2Fs, and thus allows cell proliferation. The pRb signaling circuit can be disrupted in a variety of ways; for example, functional pRb may be lost through mutation of its gene [11] originally defining the concept of tumor suppressor loss in cancer.
Some tumor cells use various strategies to defend themselves from an irreversible switch into postmitotic differentiated states and to avoid terminal differentiation. For example, in colon cancer, mutations both in the Apc gene and in genes that modify or interact with Apc result in an escape of enterocytes in the colonic crypts from going into a terminal differentiated state [12]. Again, this eventually disturbs tissue homeostasis massively.
Furthermore, tumor cell populations are often able to expand in number by avoiding cellular safety devices, for example, multiple cell death mechanisms. Programmed cell death or apoptosis and necrosis are not the only cell death programs involved in the regulation of tissue homeostasis and the removal of unwanted cells [13]. However, the precise mechanisms of apoptotic steps and the cross-talk of physiologic signals are relatively well established. Special proteins check the extra- and intracellular environment for conditions of normality or abnormality and regulate execution pathways. Many of the signals affect the mitochondria, thereby releasing cytochrome c and influencing members of the bcl-2 family. The p53 tumor suppressor protein has a prominent role as a cell cycle checkpoint and in regulating apoptosis [14]. The ultimate effectors of apoptosis are proteolytic caspases. In the end, the chromosomes degrade, the nucleus is fragmented, the cytoplasmic and the nuclear skeletons break down, and the cells are digested.
In tumors, the apoptotic program can be influenced by overexpressed oncogenes and circumvented by inactivated tumor suppressor proteins, which can dramatically affect the dynamics of tumor progression.
Growth signal autonomy, insensitivity to antigrowth signals, and resistance to apoptosis all lead to uncoupling of the cellular growth program from signals in its environment. Nevertheless, normal mammalian cells carry an intrinsic program of a finite replicative potential that limits their multiplication and has to be disrupted in tumors.
Most types of tumor cells cultivated in vitro are immortalized. At some point during the course of the multistep tumor progression, these cells breach the mortality barrier and acquire an unlimited replicative potential. Tumor cells are able to maintain the telomeres at a length above a critical threshold by upregulated expression of the telomerase or through a mechanism known as alternative lengthening of telomeres [15]. Inactivation of the tumor suppressor proteins p53 and Rb, for example, can circumvent senescence and eventually lead to immortalization.
Tumors are characterized by a rampant growth that requires a perpetual supply of oxygen and nutrients. A prerequisite, however, is that the process of angiogenesis is initiated and new blood vessels are developed. Angiogenesis is regulated by a series of positive and negative signals. Most studied are the angiogenesis-initiating vascular endothelial growth factor (VEGF) and fibroblast growth factors (FGFs) that are under complex transcriptional control [16].
Using increasingly sophisticated animal models will make it possible to ascribe specific roles to each of the regulators and to distinguish the molecular mechanisms that control their production and activity.
The above-mentioned specific characteristics of tumors are often accompanied by alterations in cell metabolism. In this case, tumor cells use elevated amounts of glucose as a carbon source for anabolic reactions. An increase in glycolysis that is maintained under conditions of high oxygen tension leads to enhanced lactate production [17] and to acidic conditions in the environment, thereby favoring tumor invasion and suppressing anticancer immune effectors. Enhanced glucose uptake for glycolytic ATP generation or anabolic reactions gives a competitive edge for tumor growth. The hypoxia-inducible transcription factor HIF-1 plays a key role in the metabolic reprogramming of cancer cells by activating genes encoding glucose transporters and glycolytic enzymes [18]. In addition, HIF-1 downregulates E-cadherin required for the maintenance of intercellular contacts within epithelia.
During the development of most types of solid tumors, cells move out, invade adjacent tissues, and travel to distant sites in the body to establish new clusters of cells. These metastases consist of cancer cells and normal supporting cells incorporated from the host tissue.
Several classes of proteins are involved in the invasion and metastatic process, among them cell–cell adhesion molecules (e.g., N-CAM), members of the cadherin families, and integrins, which link cells to extracellular matrix substrates [19]. The most frequent alteration in cell–environment interactions in epithelial cancer involves E-cadherin. E-cadherin function is lost in a majority of carcinomas by mutational inactivation of the E-cadherin or β-catenin genes, transcriptional repression, or proteolysis of the extracellular cadherin domain. In addition, a switch in the expression of N-CAM from a highly to a poorly adhesive form plays a critical role in the processes of invasion and metastasis [20].
Extracellular proteases represent another general player in invasive and metastatic processes. Protease genes are upregulated, protease inhibitor genes are downregulated, and inactive precursor forms of proteases are converted into active enzymes. Cancer cells are able to invade surrounding stroma, permeate blood vessel walls, and infiltrate normal epithelial cell layers by active proteases on the cell surface. However, matrix-degrading proteases are often not produced by the carcinoma cells themselves, but by the adjacent stromal and inflammatory cells. In summary, the activation of extracellular proteases and the altered binding specificities of cadherins, CAMs, and integrins are of pivotal importance for tumors to acquire invasive and metastatic capabilities, to metastasize, and eventually to kill the host.
2.3 Tumorigenesis as a Multistep Process
A rising cancer incidence with increasing age indicates that the formation of tumor is a complex process that usually proceeds over a period of decades. Tumor progression is mainly driven by a sequence of randomly occurring mutations and epigenetic alterations of DNA that affect the genes controlling cell proliferation, survival, and other specific properties associated with the malignant cell phenotype (see above). Normal cells possess multiple independent mechanisms that regulate their growth and differentiation potential; therefore, several separate events are necessary to override these control mechanisms. Many mutations are caused by a repeated exposure to carcinogens that can increase the rate of tumor progression by many orders of magnitude above the spontaneous background rate. Examples of well-studied environmental carcinogens include γ-irradiation, X-ray, UV-B, polycyclic aromatic hydrocarbons, heterocyclic amines, aflatoxin B1, and alkylating agents. The likelihood of developing a detectable tumor is determined by the cumulative exposure to a carcinogenic stimulus rather than the age at which this exposure begins. In addition, viruses might contribute to the cancer phenotype by several mechanisms. Multistep tumor formation can be clearly seen on the basis of histopathological alterations in a variety of organ sites, most prominent in colon cancer, where a carcinoma can sometimes be observed to grow out from an adenomatous polyp.
The accumulation of genetic alterations as colon tumor progression proceeds involves both the activation of oncogenes and the apparent inactivation of at least three distinct tumor suppressor genes (Figure 2.2). Inactivation of the APC gene is one of the first incidents, but the precise order of the subsequent changes may vary from tumor to tumor. Barrett's esophagus, which is a precursor lesion to esophageal carcinoma, is also characterized by a number of alternative genetic paths ranging from the initial metaplasia and dysplasia to cancer clones [21]. However, in other organ sites such as breast or prostate cancer, similar pathways could not be described. Each tumor type seems to have its own genetic bibliography involving its own particular set of mutated oncogenes and tumor suppressor genes.
Figure 2.2 Accumulation of genetic alterations in colon carcinoma progression (adapted from Ref. [2]).
Oncogene collaboration experiments provided an in vitro model of multistep transformation in vivo and suggested a rationale for the complex genetic steps that accompany and cause tumor formation in human beings. Cancer phenotypes can be achieved by collaborative action of several genes or genetic alterations in experimental transformation of various human cell types in vitro, but does this happen in all human cell types? And does this happen in (all) spontaneously arising human tumors? In humans, it is still not possible to measure the kinetics of individual steps of tumor progression. Therefore, experimental protocols were developed to induce carcinomas in rodents using genetically engineered (ras transgenics, APC knockout) or chemically induced tumor models (azoxymethane/dextran sodium sulfate, 7,12-dimethylbenz[a]anthracene/12-O-tetradecanoylphorbol-13-acetate). In these models, the functional role of genes in tumorigenesis during initiation, promotion, and progression and the intracellular pathways involved can be studied in precise detail. In addition, these models can also offer insight into the mechanisms by which different chemical or physical modulators influence the outcome of early-stage carcinogenesis.
2.4 Epigenetic Changes in Cancer Development
Epigenetics encompasses the interaction of genetic material with its surroundings to produce a phenotype and can be seen as the basis of cellular differentiation. It explains that in a multicellular organism development generates a vast number of cell types with distinct functions and diverse but relatively stable gene expression profiles despite the same genotype of the cells. Epigenetic mechanisms coordinate important biological processes such as reprogramming of genomes during differentiation and development, RNA interference leading to posttranscriptional gene silencing, genomic imprinting, or X-chromosome inactivation [22].
Besides mutations in the DNA, the mechanisms that regulate the interpretation of the genetic code play a growing role in understanding carcinogenesis. Examples of such mechanisms include covalent and noncovalent modifications to the DNA, such as DNA methylation of cytosine dinucleotides (CpG), and to chromosomal proteins such as posttranslational modifications of histones and nonhistones (acetylation, methylation, ubiquitinylation, phosphorylation, sumoylation, or ADP ribosylation). Regulation of gene transcription can thus be affected in the short or long term. Epigenetic processes play an important role in the normal homeostasis of stem and progenitor cells in tissues and disruption can result in cancer. In tumors, DNA is often globally hypomethylated, contributing to genome instability and activation of oncogenes, and locally hypermethylated causing silencing of tumor suppressor genes. In addition, changes in histone modifications and chromatin organization can also be observed. In recent years, methylation of DNA and histone tail modifications have emerged as the most critical players of transcriptional regulation. DNA methylation is conferred by DNA methyltransferases (DNMTs) forming CpG islands especially enriched in gene promoters or the first exon (in about 70% of protein-coding mammalian genes). These CpG islands are normally unmethylated in transcriptionally active genes such as housekeeping or tumor suppressor genes, whereas developmental and tissue-specific genes mostly appear to be methylated and silenced in differentiated tissues. Meanwhile, many tumor suppressor genes have been identified to be hypermethylated in tumorous tissues compared to their normal counterparts, for example, RB1, VHL, CDKN2A, BRCA1, or ST18 [23, 24]. However, the basic mechanisms underlying the aberrant DNA methylation and the selection of genes that become methylated are barely understood. DNMTs are ubiquitously expressed at distinct levels in normal human tissues, but are overexpressed in various tumor types.
Histones are not solely DNA-packaging proteins, but store epigenetic information. Acetylation of histone lysines is generally associated with transcriptional activation, whereas methylation of certain histone residues is associated with transcriptional repression. The presence of the hypoacetylated and hypermethylated histones H3 and H465 silences certain genes with tumor suppressor-like properties, such as p21WAF1, despite the absence of hypermethylation of the CpG island [25]. Expression patterns of histone-modifying enzymes such as histone acetyltransferases distinguish cancer tissues from their normal counterparts and differ according to the tumor type.
DNA methylation and histone modifications interact with each other in the regulation of gene expression. It is generally believed that DNA methylation is the initiating event that marks certain genomic sites for the establishment of a transcriptionally inactive chromatin state [26]. However, there is increasing complexity of the relations between various epigenetic repression systems.
2.5 miRNAs and Cancer
Since the discovery of a class of small noncoding RNAs (called microRNA or miRNA) and their role in tumorigenesis, a multitude of studies have established a lot of evidence that supports the increased and accelerated progression in oncological research. A global reduction in miRNA levels is emerging as a common hallmark of cancer. MicroRNAs are regulatory RNAs of 17–30 nucleotides in length. They perfectly match 3′ untranslated regions of target messenger RNAs (mRNAs) resulting in its degradation or inhibition of mRNA translation. It is the function of the target mRNA that determines a miRNA acting either as a tumor suppressive if directed against protooncogene transcripts or as an oncogenic if directed against tumor suppressor gene transcripts. It should be noted that some miRNAs can have dual oncogenic and tumor suppressive roles depending on the cell type and pattern of gene expression. Prominent members of miRNAs include the let-7 family whose depletion in breast, lung, and colon cancer causes enhanced tumorigenicity [27]. Some miRNAs are key regulators of multiple genes that regulate different processes in cancer biology. Other miRNAs confer increased invasion capacities and promote tumor metastasis. The number of transcripts known to be regulated by miRNAs is growing rapidly.
In cancer, miRNAs are found to be massively deregulated. Several miRNAs reside in chromosomal regions that are either frequently deleted or amplified or affected by copy number variations in tumors. Another mechanism responsible for this deregulation is the epigenetic silencing of miRNA genes. In turn, some miRNAs directly repress enzymes of the epigenetic machinery, including DNA methyltransferases, histone deacetylases, and histone methyltransferases. In addition, a failure of posttranscriptional regulation may also lead to impaired miRNA maturation. The production of mature miRNAs underlies a complex process of subsequent modifications of the primary transcript. After export to the cytoplasm, the precursor miRNA is further processed. Repeatedly, discrepancies between the levels of primary transcript, precursor, and mature miRNA have been reported in tumorous cells.
Signatures of deregulated miRNAs can be useful in subtyping different carcinomas or determining their aggressiveness. Genome-wide profiling of miRNAs in different cancer types identified differentially expressed candidates including predictive miRNAs able to distinguish between normal and tumor tissue [28]. In addition, several correlations between downregulation of certain miRNAs and clinicopathological features such as tumor size, lymph node status, the expression of p53, and others were found. Not only signatures of miRNA expression may be used as tumor markers for diagnosis and patient risk stratification, but deregulated miRNAs may also represent novel targets for anticancer therapies.
2.6 Cancer Stem Cells
The cancer stem cell hypothesis suggests that cancers are derived from a stem cell compartment in a multistep process involving the accumulation of mutations in a variety of oncogenes and tumor suppressor genes. Cancer stem cells (CSCs) are a subset of cancer cells within a tumor that have the ability both to self-renew and to differentiate. CSCs are long-term residents of exposed epithelial tissues and uniquely susceptible to the accumulation of oncogenic lesions by carcinogens. CSCs share many characteristics with carcinoma cells, such as immortality, the absence of contact inhibition, and the ability to undergo self-renewal. It has been estimated that as many as 25% of the cancer cells within certain tumors have the properties of CSCs [29]. The actual proportion of CSCs within a tumor depends on the cell type of origin, stromal microenvironment, accumulated mutations, and stage of malignant progression reached by a tumor. Growing evidence suggests that signaling pathways that regulate self-renewal in normal stem cells are deregulated in cancer-initiating cells, resulting in uncontrolled expansion, aberrant differentiation, and formation of tumors with heterogeneous phenotypes. The existence of CSCs may also help to understand many of the properties of carcinomas, such as their clonal origin, their heterogeneity, and their plasticity. Increased plasticity may be present within tumor populations, enabling bidirectional interconvertibility between CSCs and non-CSCs [30].
Multipotency of lineage differentiation is likely to be a frequent but not an essential property of CSCs. CSCs with high initiating and tumor growth driving capacity give rise to more differentiated nontumorigenic progeny. Selection pressure can act at this level. Acquired additional genetic alterations can be beneficial for special clones. The genomes of populations of tumor cells often become increasingly unstable. Consequently, the resulting tumor mass is composed of an increasing number of distinct sectors, each dominated by a genetically distinct subclone. As the tumor progresses, genetic and epigenetic alterations may result in the emergence of a self-renewing metastatic CSC that can enter the blood stream and seed a secondary tumor in a distinct organ.
2.7 Cancer and the Environment
The behavior of tumor cells can also be influenced by various environmental factors that play a dominant role in the majority of sporadic cancers (see Chapters 4 and 6). Cancer development is not only due to exogenous or endogenous carcinogens but also due to their interactions with genes that are involved in the detoxification of these carcinogens, repair of DNA damage, and control of cell signaling and cell cycle. The cells in the body are constantly exposed to carcinogens from the macroenvironment (chemical carcinogens, radiation, viruses, etc.) or microenvironment (reactive oxygen species such as superoxide anions, hydroxyl radicals, etc.).
The microenvironment surrounding the tumor cell (stromal fibroblasts, adipocytes, and endothelial cells, as well as the extracellular matrix) and the immune system are known to play important roles in cancer progression. There are a lot of reciprocal interactions between cancer cells and their microenvironment or niche. Lesions in the surrounding mesenchymal tissue can enormously modulate the risk of epithelial malignancy.
The epithelial to mesenchymal transition plays an important role in metastasis and is defined as the switch from nonmotile, polarized epithelial cells to motile, nonpolarized mesenchymal cells, with the potential to migrate from a primary tumor to distant organs. Loss of E-cadherin and a simultaneous gain of mesenchymal N-cadherin allow cells to lose adhesive affinity for other epithelial cells and become more migratory and invasive [31]. In addition to transcriptional repressors such as Snail or Twist, one of the most potent inducers of an epithelial to mesenchymal transition is the transforming growth factor-β (TGF-β).
A key environmental stressor associated with tumor progression is cell oxygen deficiency or hypoxia. Induction of the hypoxia-inducible factor (HIF) family of transcription factors regulates cellular processes including glucose metabolism, angiogenesis, cell proliferation, and tissue remodeling in response to low oxygen levels. Insufficient oxygen limits tumor cell division while at the same time causing malignant cells to switch to anaerobic metabolism, increasing genetic instability, promoting angiogenesis, and inducing cell adaptations allowing for more invasive behavior [32].
Chronic inflammatory conditions are often associated with tumor development. Dysregulation of tissue repair, for example, can lead to abnormalities in the inflammatory response and ultimately tumorigenesis. Inflammation in carcinomas involves different processes such as an influx of proinflammatory cytokines including tumor necrosis factor-α and TGF-β, cytotoxic mediators, proteases, matrix metalloproteinases, interleukins, and interferons and produces potent lymphangiogenic growth factors allowing tumor growth and metastatic spread to the lymph nodes [33]. Tumor cells themselves produce cytokines that attract neutrophils, macrophages, lymphocytes, and dendritic cells. Tumor-associated macrophages produce proangiogenic growth factors such as VEGF contributing to tumorigenic growth and metastatic potential.
In multistage epithelial carcinogenesis in the skin driven by transgenic expression of HPV16, humoral antibodies are produced against extracellular matrix components [34]. Stromal accumulation of autoantibodies in premalignant skin regulates recruitment, composition, and bioeffector functions of leukocytes in tumor tissue, which in turn promotes progression and subsequent carcinoma development.
In vitro, individual tumor lines have RNA expression patterns that clearly define them from other lines even when grown in different environments. However, gene expression remains constant in individual subcutaneous tumors as the tumors increase in size [35, 36]. In the past few years, models have been developed that rely on Cre recombinase-mediated deletion of floxed sequences to activate an oncogene or inactivate a tumor suppressor gene in only a subset of cells of a tissue at a defined point in time. Cells bearing such mutation are surrounded by nonmutated competitors and may better reflect the situation during spontaneous tumorigenesis.
2.8 Systems Analysis of Cancer
Tumors are not only highly heterogeneous with respect to cell type and tissue origin but also involve dysregulation of multiple pathways controlling fundamental cell processes such as proliferation, differentiation, migration, and apoptosis. The activities of molecular networks that execute metabolic or cytoskeletal processes and regulate them by signal transduction are altered in a complex manner by diverse genetic mutations in concert with the environmental context. It is therefore necessary to develop actionable understanding of this multivariate dysregulation. High-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic, and metabolomic profiling are used for characterization of tumor cells and surrounding tissues at the molecular level (see Chapters 11–13). Even tumors of a particular tissue type comprise highly heterogeneous sets of mutations in a great number of different genes and a large number of gene products contribute to the tumor cell phenotype.
Signal transduction pathways are organized in networks, an alteration of one pathway can lead to changes in others directly via protein–protein interactions or indirectly via transcriptional or translational influences. These networks connect to components beyond the tumor cells themselves, including other cells in the environment along with the extracellular matrix [37]. Therefore, characterization of a cellular dysregulation will need to be multivariate and quantitative because single molecular biomarkers or qualitative constituent lists will be inadequate. Systems genetic analysis of experimental animal cancer models, in which extensive control over the environment and the initiating lesion is possible, has enormously expanded the understanding of how genetic alterations affect the development of tumors [38]. These artificial cancer models approximate human disease to varying degrees and provide an opportunity to perform interventional and gene–environment interaction studies that are difficult or impossible to perform in human populations. In addition, significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, the impact of particular mutations on signal transduction, and consequences of altered cell behavior in tissue environments. Although individual genes or environmental factors may be a critical component in the pathogenesis of cancer, it is ultimately the modulation of underlying pathways that determines the resultant phenotype.
New techniques are required to examine dysregulated networks and to identify pathways within the context of a cellular network. Studying the interactomes and especially protein–protein and protein–DNA associations provides a framework for analysis of empirical data of various types, such as transcriptomic, phosphoproteomic, and phenotypic assessments [39]. Functional proteomics based on optical, spectroscopic, and microarray methods is one example of a systematic analysis of biochemical networks to provide data for a better understanding of networks and to interpret the action of chemical or physical agents [40].
The development from a single cell with a disturbed network to a metastatic tumor is exceptionally complex. However, there exist several key processes common to most cancers including uncontrolled excessive proliferation, angiogenesis, resistance to apoptosis, and metastasis. By using systems biology modeling techniques, it will be possible to understand each of these processes and how they interact to drive tumor progression.
2.9 Outlook
Genomic, transcriptomic, proteomic, and metabolomic studies have significantly advanced our understanding of carcinogenesis. Despite an impressive progress in the development of methods in all omics fields, substantial hurdles remain. Challenges especially are to bring omics technologies to clinical applications and to use them in risk assessment strategies. Mainly current proteomics technologies are too slow, too complex, and too expensive to be used in a clinical laboratory and, in general, the existence of many different experimental approaches leaves a deficit in standardization. However, great progress is to be expected from further developed array-based methods, optical methods, and microengineering approaches. For instance, the increasing use of advanced bioinformatics and systems biology tools has led to the identification of cancer-associated phosphorylation networks [41] and a further stream of data can be expected to understand the regulatory interplay between individual molecules and in networks. In addition, these investigations may bridge the gap between mechanistic understanding and mainly phenomenological markers (see Chapter 14).