Measuring the exposome: A powerful basis for evaluating environmental exposures and cancer risk

Review Article

Measuring the Exposome: A Powerful Basis forEvaluating Environmental Exposures and Cancer Risk

Christopher P.Wild,* Augustin Scalbert, and Zdenko Herceg

International Agency for Research on Cancer, 150 cours Albert Thomas,Lyon, France

Advances in laboratory sciences offer much inthe challenge to unravel the complex etiologyof cancer and to therefore provide an evidence-base for prevention. One area where improvedmeasurements are particularly important to epi-demiology is exposure assessment; this require-ment has been highlighted through the conceptof the exposome. In addition, the ability toobserve genetic and epigenetic alterations inindividuals exposed to putative risk factors alsoaffords an opportunity to elucidate underlyingmechanisms of carcinogenesis, which in turnmay allow earlier detection and more refinedmolecular classification of disease. In this con-text the application of omics technologies tolarge population-based studies and their associ-ated biobanks raise exciting new avenues ofresearch. This review considers the areas of

genomics, transcriptomics, epigenomics andmetabolomics and the evidence to date thatpeople exposed to well-defined factors (forexample, tobacco, diet, occupational exposures,environmental pollutants) have specific omicsprofiles. Although in their early stages of devel-opment these approaches show promising evi-dence of distinct exposure-derived biologicaleffects and indicate molecular pathways thatmay be particularly relevant to the carcinogenicprocess subsequent to environmental and life-style exposures. Such an interdisciplinaryapproach is vital if the full benefits of advancesin laboratory sciences and investments in large-scale prospective cohort studies are to be real-ized in relation to cancer prevention. Environ.Mol. Mutagen. 54:480-499, 2013. VC 2013Wiley Periodicals, Inc.

Key words: exposure assessment; omics; transcriptomics; epigenomics; metabolomics

INTRODUCTION

Laboratory analyses are increasingly integral to epide-

miological studies of the causes and prevention of cancer.

This interdisciplinary approach has many potential advan-

tages, notably the improvement of exposure assessment,

examination of risk within biomarker-defined subgroups

(e.g., genetic polymorphisms or immune status), diagnos-

tic and prognostic biomarkers, elucidation of mechanisms

of carcinogenesis and provision of short-term outcomes in

intervention studies [Wild et al., 2008]. The field of

molecular cancer epidemiology was launched in the mid-

1980s with the measurements at the individual level of

DNA adducts resulting from environmental chemical

exposures [Perera et al., 1982; Umbenhauer et al., 1985].

These were followed by chromosomal alterations, DNA

and protein modifications (e.g., 32-P postlabelling, Comet

assay, DNA, and protein adducts), somatic mutations

(e.g., HPRT) and genotyping and phenotyping of carcino-

gen metabolism and DNA repair enzymes.

Grant sponsor: Eurocanplatform: A European Platform for Translational

Cancer Research; Grant number: 260791.

Grant sponsor: Exposomics: Enhanced exposure assessment and omic

profiling for high priority environmental exposures in Europe; Grant

number: 308610.

Grant sponsor: Application of new technologies and methods in nutrition

research—the example of phenotypic flexibility—NutriTech; Grant num-

ber: 289511.

Grant sponsor: French National Cancer Institute (INCa) (Biomarkers of

B vitamins, Epigenome, genetic polymorphisms, and breast cancer risk

in the European Prospective Investigation into Cancer and Nutrition

(EPIC) Study).

*Correspondence to: Christopher P. Wild, International Agency for

Research on Cancer, 150 cours Albert Thomas, Lyon, France. E-mail:

[email protected]

Received 28 January 2013; provisionally accepted 6 March 2013; and in

final form 7 March 2013

DOI 10.1002/em.21777

Published online 16 May 2013 in

Wiley Online Library (wileyonlinelibrary.com).

VC 2013Wiley Periodicals, Inc.

Environmental andMolecular Mutagenesis 54:480^499 (2013)

These assays generally had limited throughput and lim-

itations in terms of sensitivity, specificity, scope, and the

amounts of biological material required. Notable excep-

tions were biomarkers of infection-related cancers, for

example, hepatitis B virus or Helicobacter pylori, where

the sensitivity, specificity and high-throughput, combined

with an ability to measure past exposure led to marked

advances in understanding the etiology of liver and stom-

ach cancers respectively.

It was the advent of the polymerase chain reaction

which first permitted larger scale studies, albeit still fo-

cusing on one or two specific polymorphisms in at most,

a few hundred cases and controls. A probable unintended

consequence of the success of these early genotyping

assays was a diminished focus, and therefore progress, on

developing biomarkers of exposure to environmental and

lifestyle risk factors. Nevertheless, there were significant

findings relating adducts of aflatoxins to liver cancer, pol-

ycyclic aromatic hydrocarbons to lung cancer and aro-

matic amines to bladder cancer [Poirier, 2004].

Either side of the turn of the last century a major

series of technical advances were made, at least partially

stimulated by efforts to sequence the human genome.

The so-called “omics” technologies permit the rapid,

parallel analysis of hundreds or thousands of genetic

polymorphisms as well as profiles of gene transcripts,

proteins and metabolites, all conducted on small volumes

of biological material. This has been complemented by

exciting steps in understanding epigenetics, to yield

similar high-throughput approaches to studying the epige-

nome. Collectively these developments offer huge poten-

tial to cancer epidemiology in its attempts to study causes

and prevention. However, whilst this potential is evident,

its realization has been relatively slow with a strong bias

toward application in clinical and basic cancer research

[Wild, 2009].

Nevertheless, over the last few years the “omics” tech-

nologies have begun to find some application in popula-

tion-based studies. These approaches to biomarker

development and validation promise to capture new infor-

mation on both exposure and the subsequent early biologi-

cal effects. Whilst the work is at a relatively early stage,

the current manuscript aims to examine through selected

examples four areas (genomics, epigenomics, transcriptom-

ics, and metabolomics) to assess what progress has been

made to date and what priorities should be established for

the future. This is done specifically in light of the develop-

ing concept of the human exposome and the need to

improve exposure assessment for environmental and life-

style risk factors (see Fig. 1 for the overall context).

Fig. 1. Characterizing the exposome. The exposome comprises every

exposure to which an individual is subjected over a lifetime. Exposures

arise from two broad categories: external and internal sources. The external

exposures include different environmental and lifestyle factors (e.g.,

chemicals, infectious agents, diet, tobacco, alcohol), and the internal expo-

sures include endogenous processes (e.g., metabolism, hormones, inflamma-

tion, gut microflora). The exposome is characterized through the application

of a wide range of tools (among which omics represent just one approach).

Environmental and Molecular Mutagenesis. DOI 10.1002/em

The Exposome and Cancer Risk 481

THE EXPOSOME

The exposome comprises every exposure to which an

individual is subjected, from conception to death [Wild,

2005]. In its complexity, it requires consideration of both

the nature of those exposures and their changes over

time. For ease of description, three broad categories of

exposures have been considered: internal, specific external

and general external [Wild, 2012a,b]. More specifically,

the internal exposome comprises processes such as metab-

olism, endogenous circulating hormones, body morphol-

ogy, physical activity, gut microbiota, inflammation, and

aging. The specific external exposures include diverse

agents, for example radiation, infections, chemical con-

taminants and pollutants, diet, lifestyle factors (e.g.,

tobacco, alcohol), occupation and medical interventions.

The wider social, economic and psychological influences

on the individual make up the third part of the exposome,

including for example: social capital, education, financial

status, psychological stress, urban-rural environment and

climate. There is clearly overlap in these three domains

and sometimes difficulty in placing a particular exposure

in one domain or another, but the description serves to

illustrate the full breadth of the exposome.

Temporal variation in exposure is a critical feature of

the exposome. This is already complex when one consid-

ers the changes associated with development in the womb

through the different post-natal, childhood and adolescent

patterns of exposure and on into the adult with changes

of residence, occupation, psychosocial conditions and life-

style, and so forth. However, it is increasingly evident

that heritable epigenetic alterations resulting from expo-

sures of parents and even grandparents may influence the

child’s phenotype [Carone et al., 2010; Skinner et al.,

2011] and therefore, theoretically at least, may be consid-

ered as a component of the putative trans-generational

exposome. Despite the complexity, if the broad domains

described above are combined and integrated over time

then a picture of a comprehensive exposome emerges.

In contrast to the genome, where an individual’s data

may be determined more and more frequently as a basis

for clinical decision-making (personalized or stratified

medicine), the exposome will probably be realized in a

different manner. Specifically, it is more likely to be used

in epidemiological studies to establish risk factors at the

population level as a basis for public health decisions.

Essentially the exposome will translate to a more compre-

hensive, integrated measure of environmental and lifestyle

exposures in subjects participating in epidemiological

studies. These studies, for practical purposes, still limit

themselves predominantly to questions about exposure-

disease relations during specific time periods, as opposed

to lifelong study of a specific set of individuals. For

example, the exposome would find application in cohorts

of different subjects recruited during childhood,

adolescence or adulthood and assess exposures during dif-

ferent, defined periods of life. It is less likely that there

would be an opportunity to characterize the complete

exposome of an individual throughout their lifetime and

associate this to disease outcomes. Therefore, whilst the

concept of the exposome is undoubtedly helpful in

executing more comprehensive exposure assessment, it is

not an entity expected to be determined frequently for a

single individual. Notwithstanding this view, a compre-

hensive personal omics profiling has recently been dem-

onstrated, illustrating some of the potential for assessing

health states and disease risk over a longer period of time

[Chen et al., 2012].

In discussing and evaluating the exposome, it is impor-

tant to distinguish the methodology (exposomics) from

the underlying phenomenon to be measured (exposome).

It is also important not to substitute a very narrow set of

methods (e.g., “omics”) for exposomics (of which

“omics” are just one approach). Therefore, whilst this pa-

per does focus rather narrowly on the “omics” methodolo-

gies, it is important to stress that other technologies,

including geospatial monitoring, personal bio-monitors,

hand-held computers and mobile phones, and so forth, as

well as other more targeted biomarkers all have contribu-

tions to make in capturing the full spectrum of exposures

of interest to epidemiologists. In addition, omics profiles

will not exclusively, nor perhaps even predominantly,

reflect exposure. A significant component will reflect nor-

mal physiology and cellular function so that while within

the omics profiles there will be RNA sequences or metab-

olites that reflect exposures, the majority probably will

not.

Although diverse tools are needed to address the full

range of the three exposome domains mentioned above,

an attractive promise of modern laboratory technology is

the capture of a wide-range of exposures in a single mea-

surement. In this respect “omics” may provide measures

of already identified exposures or agents but also afford

an opportunity to take an agnostic approach, yielding bio-

markers which show interindividual variation but which

have not yet been related to specific environmental or

lifestyle factors, thus allowing the investigators to work

backwards from the biomarkers to putative risk factors.

The integration of the known and unknown components

of an exposure profile is a primary goal of the new gener-

ation of methods to assess the exposome.

EXPOSURES ANDGENOMICCHANGES

The discovery that the TP53 tumor suppressor gene is

mutated in some 50% of all human tumors, via a diverse

array of point mutations, afforded the possibility of muta-

tion spectra yielding clues to causation [Hollstein et al.,

1991]. The tumor promised to reveal its own etiologic

secrets.


482 Wild et al.

There have been successes, most remarkably in the

form of the frequent TP53 codon 249 AGG to AGT trans-

version observed in hepatocellular carcinomas associated

with exposure to aflatoxins. In addition, tandem CC to

TT transitions at pyrimidine dimers linked to UV light

exposure in nonmelanoma skin cancer and the frequent G

to T transversions in tobacco-associated cancers provided

further proof of concept [Pfeifer and Besaratinia, 2009].

Most recently aristolochic acid can be added to this group

with characteristic AT to TA transversions in upper uri-

nary tract tumors [Chen et al., 2012].

Despite these successes, overall progress in assigning

somatic mutations in human tumors to risk factors has

been somewhat disappointing. The question now is

whether a new generation of studies using whole-genome

and related large-scale DNA sequencing approaches will

permit comparison of mutation spectra across the whole

or selected portions of the genome with defined environ-

mental or lifestyle exposures. By definition this strategy

is limited to discovery of mutagenic carcinogens and is

complemented by the approaches described later in this

chapter for factors acting through alternative, epigenetic

mechanisms.

An early example of whole tumor genome sequencing

concerned a small cell lung cancer (SCLC) cell line by

comparison with genomic DNA derived from an EBV-

transformed lymphoblastoid line from the same patient.

This analysis revealed over 20,000 somatic single nucleo-

tide variants (point mutations) [Pleasance et al., 2010].

The manuscript title linked the mutation pattern to

tobacco smoking, but it was noted that the smoking his-

tory of the patient was not recorded. Despite this limita-

tion, the strong link between SCLC and smoking as well

as the high percentage of G to T transversions (and other

mutations typical of tobacco exposure) was consistent

with the hypothesis that this spectrum represented the fin-

gerprint of tobacco spread across the full genome of the

cancer. Furthermore, there was a remarkable consistency

between this spectrum and that seen in the accumulated

data from the IARC TP53 mutation data base for SCLC,

where tobacco smoking status was recorded [Pfeifer and

Hainaut, 2011]. A second report by Pleasance et al.

[2010] took the same approach to examine mutations in a

melanoma cell line. In this case the mutational pattern

was consistent with a strong effect of sunlight, in line

with earlier data on TP53 mutation spectra.

Subsequent to these first descriptions there have been a

number of major reports on whole genome sequencing of

human tumors from different organs, notably coming

from the Cancer Genome Atlas (http://cancergenome.nih.-

gov/). However, these studies have not systematically or

extensively examined the mutation spectra in relation to

exposure to specific agents. This is a missed opportunity,

because sequencing tumors for which epidemiological

data on risk factors is available would not have been

more technically demanding or expensive. In addition, in

the few cases where this has been done there are exciting

indications that “second generation mutation spectra” can

offer more than the TP53 analyses to date.

A prime example comes from head and neck cancers.

These tumors are strongly linked to prior exposure to

tobacco and alcohol but also, particularly in high-income

countries, with infection by mucosal human papilloma

viruses (HPV). Stransky et al. performed whole-exome

sequencing on 74 tumors. HPV-positive tumors had

around half the mutation rate of the ones which were

HPV-negative, albeit with an overall 40-fold variation. G

to T transversion mutations were common, with more

mutations in total in those tumors with the higher fraction

of G to T mutations (implying a general effect of tobacco

mutagens) [Stransky et al., 2011]. Agrawal et al. [2011]

took a similar exome-sequencing approach and in 32

tumors showed a mean of 19 mutations per tumor, with a

range of 2–78. Again there were far fewer mutations in

HPV-associated tumors and on average about twice as

many in those from tobacco users. However, in this series

there was no enrichment for G to T transversions. This

may be because in the larger study from Stransky et al.

[2011] there were a significant proportion of laryngeal

tumors; these are less likely to be linked to HPV infection

and thus have higher mutation rates and G to T transver-

sion frequencies than tumors from other sites.

There are a few other tumors where specific risk fac-

tors can be implied, for example, for liver flukes and

cholangiosarcoma [Ong et al., 2012] and hepatitis viruses

and hepatocellular carcinoma [Totoki et al., 2011; Fuji-

moto et al., 2012; Guichard et al., 2012]. In the study by

Fujimoto et al., cases associated with hepatitis B and C

viruses and alcohol were studied by whole genome

sequencing and demonstrated some differences in base

substitution mutation patterns by risk factor. However,

study designs to date have often not included unexposed

groups or considered the issues of bias or confounding.

In principle, the ability to conduct whole genome

sequencing or alternative targeted strategies such as

exome sequencing, RNA-seq and ChIP-seq offers many

new possibilities to obtain clues to causation (Table I). It

is not yet clear which approach would be best adapted to

studying etiology. There is also a need for careful evalua-

tion of the heterogeneity of tumors and the impact of

admixtures of stromal or other nontumor cells, particu-

larly as the ability to conduct sequencing on single cells

becomes more widely available.

Initially, it would be valuable to conduct some more

detailed proof-of-principle analyses using well-character-

ized tumors from individuals with documented exposure

to established risk factors. It would also be of interest to

see if the same risk factor is associated with similar muta-

tional fingerprints in different target organs, for example,

HBV in liver cancer and non-Hodgkins lymphoma



http://cancergenome.nih.gov/

http://cancergenome.nih.gov/

(NHL). Such studies could compare the whole genome

with other approaches mentioned and also permit compar-

ison with TP53 spectra, which remains the most fre-

quently mutated human gene sequence in many whole

tumor genome sequencing studies.

Subsequently it would be of enormous value to exam-

ine tumors for which relatively little has been established

about causation e.g. prostate, kidney, brain, haematologi-

cal tumors, colorectal and to do so using specimens from

patients within prospective cohort studies, where exposure

information had been collected some years prior to onset

of disease, for example, the EPIC study [Bingham and

Riboli, 2004]. This would allow agnostic exploration of

novel mutation patterns in relation to a panoply of envi-

ronmental and lifestyle factors. The observation that spe-

cific tumor types do have different somatic mutation

patterns when analyzed through whole genome sequenc-

ing encourages this strategy.

Geographic and temporal variations in incidence of dif-

ferent cancers were formative in establishing the influence

of environment and lifestyle on risk [Peto, 2001]. Exploit-

ing such inherent variation to compare mutation spectra

for the same tumor in different regions or at different

time periods, related to the same or different putative risk

factors, could provide novel insights to cancer causation.

For example, comparison of mutational spectra for colo-

rectal cancers in United States with India and Brazil, two

countries experiencing the cancer transition, would reveal

much about possible shared risk factors and the modula-

tion of their effects by co-exposures or genetic back-

ground. Introduction of a putative risk factor to society

could be assessed in relation to altered mutational

patterns (or indeed epigenetic alterations) over time, for

example, gliomas in relation to mobile phone use, to

complement studies of overall changes in cancer

incidence.

The above proposals relate to analysis of genetic mate-

rial obtained from the tumor. Equally exciting is the pos-

sibility to make such analyses on circulating tumor DNA,

which would possibly allow the early detection of a can-

cer risk [Nogueira da Costa and Herceg, 2012]. This has

been demonstrated for the specific G to T TP53 mutations

for aflatoxins [Gouas et al., 2012] and such examples

could also be used as proof-of-principle for whole ge-

nome sequencing in exposed individuals using blood or

urine samples. Clearly here there would be questions

about the stability of such changes in relation to past

exposures and how the tumor and cell-free DNA in the

circulation compare.

EXPOSURES AND TRANSCRIPTOMICCHANGES

Transcriptomics has become a powerful tool for study-

ing genome-wide responses to environmental exposures

in experimental models and human populations. The term

“transcriptome” refers to the totality of RNA molecules

produced in a single cell or cell population. It is widely

accepted that the transcriptome is dynamic and that it

reflects the organism’s immediate and genome-wide

response to environmental exposure and endogenous cues.

The development of powerful technologies and functional

assays together with the annotation of coding and noncod-

ing transcripts has enabled a steady improvement in the

understanding of the functional transcriptome and has

provided the means to interpret the functional consequen-

ces of transcriptome changes associated with environmen-

tal exposures. These advances have demonstrated that

studies of global transcriptome profiles may provide

insights into the involvement of specific genes and molec-

ular pathways in response to environmental stressors

[McHale et al., 2010].

Most previous studies investigating transcriptome

changes in response to environmental exposures relied on

peripheral blood (such as white blood cells), although

some studies looked at tissue biopsies. Genome-wide

approaches, based on microarrays and more recently on

next-generation sequencing (NGS), have been used to

gain a comprehensive assessment of the gene expression

signature associated with exposures. These include tran-

scriptome profiling in relation to various nutritional fac-

tors [Pagmantidis et al., 2008; Rudkowska et al., 2011;

van Dijk et al., 2012; Vedin et al., 2012], lifestyle factors

[Connolly et al., 2004; Zieker et al., 2005; Idaghdour

et al., 2008], and stress [Kawai et al., 2007]. The results

from these studies demonstrated that the transcriptome is

a dynamic entity that is highly responsive to environmen-

tal exposures. For example, specific environmental expo-

sures were shown to alter the expression of as much as

30% of the transcriptome in specific blood cells

TABLE I. Opportunities for Mutation Spectra Comparisons Using Whole Genome Sequencing, Exome-seq, RNA-seq or ChIP-seq

Tumours and normal DNA from patients well-characterized for specific exposures e.g. within prospective cohort studies

Tumours for which there is little known about etiology obtained from patients with well-characterized exposure histories e.g. prostate, kidney, brain,

haematological cancers, colorectal

Comparison of the same tumour types from different regions, with the same or different risk factors, for example, colorectal, breast cancers in high-

income versus low-income countries

Comparison of different tumour types associated with the same risk factors e.g. liver and NHL with HBV; lung and ovarian cancer with asbestos

Comparison of the same tumour types over time, in relation to introduction of putative risk factors or observed alterations in incidence e.g. liver can-

cer in the high income countries; testicular cancer in Nordic countries; mobile phones and gliomas


484 Wild et al.

[Idaghdour et al., 2008], although the transcription levels

of the vast majority of genes remained remarkably stable

within the individual over time [Eady et al., 2005]. Many

environmental chemical compounds have been shown to

alter the transcriptome, although only a few agents have

been examined in exposed human populations. These

include benzene, diesel exhaust, arsenic, tobacco smoke,

metal fumes, dioxin, and acrylamide (Table II), which are

discussed below.

Several studies have investigated the impact on the

transcriptome of environmental exposure to benzene, an

aromatic hydrocarbon found in crude oils and a widely

recognized risk factor for several human malignancies

[IARC, 2012a,b]. New insights into how the transcrip-

tome is modified by benzene exposure have come from

work by the groups of Smith and Rothman [McHale

et al., 2010]. Forrest et al. [2005] examined the effect of

benzene exposure on the transcriptome of peripheral

blood mononuclear cells (PBMCs) from a population of

shoe factory workers and found differentially expressed

genes. A subsequent analysis of PBMCs from a popula-

tion with well-characterized occupational exposure to

benzene using two microarray platforms (Affimetrix and

Illumina) revealed a large number of differentially

expressed genes to be associated with benzene exposure

[McHale et al., 2009]. Gene pathway and ontology analy-

sis demonstrated over-representation of genes involved in

apoptosis and immune/inflammatory response [McHale

et al., 2009]. Furthermore, global gene expression analysis

of PBMCs from workers exposed to low levels of ben-

zene identified a specific gene signature and changes in

genes involved in immune response pathways, suggesting

that chronic benzene exposure, even at levels below the

current US occupational standard, perturbs many genes,

biological processes, and pathways [McHale et al., 2011].

A more recent study showed leukaemia-related chromo-

somal changes in hematopoietic progenitor cells from

workers exposed to benzene [Zhang et al., 2012]; there-

fore, it remains to be established whether the transcrip-

tome changes in PBMCs may be directly or indirectly

caused by loss or gain of chromosomes in blood progeni-

tor cells of exposed individuals.

Several studies used microarray techniques to investi-

gate the impact of tobacco smoking on the transcriptome

of blood cells [Lampe et al., 2004; van Leeuwen et al.,

2007; Hackett et al., 2012; Wright et al., 2012] and air-

way epithelial cells [Spira et al., 2004; Beane et al.,

2007; Steiling et al., 2009; Beane et al., 2011; Tilley

et al., 2011]. These studies showed that it is possible to

distinguish between individuals exposed and unexposed

to tobacco smoke on the basis of the transcriptome and

identified specific biological pathways associated with

tobacco smoking. Particularly informative was the tran-

scriptome study on monozygotic twin pairs discordant for

smoking, where differences in genetic background are not

present. This study revealed specific genes that are repro-

ducibly differentially expressed in blood cells from smok-

ers [van Leeuwen et al., 2007]. The vast majority of the

genes associated with smoking tended to be involved in

inflammatory and oxidative stress pathways. Importantly,

many of the differentially expressed genes in smokers

returned to the expression levels in nonsmokers within

weeks after smoking cessation, although some distinct

genes remained changed for years thereafter [Beane et al.,

2007; Zhang et al., 2008]. Together, these studies have

demonstrated that it is possible to distinguish between

individuals exposed and unexposed to tobacco smoke on

the basis of transcriptome profiles. The observations also

imply it may be possible to distinguish current from past

exposure to tobacco smoke and other exposures. In addi-

tion, tobacco exposure-specific changes can be detected

not only in the airway epithelium (the target tissue) but

also in peripheral leukocytes.

Arsenic exposure has also been found to induce signifi-

cant changes in the transcriptome of human cells. Arsenic

is a known carcinogen, and chronic exposure to arsenic in

drinking water has been associated with increased risk of

various human neoplasms, including cancer of the lung,

skin, liver, and kidney [Argos et al., 2006; Ghosh et al.,

2008; IARC, 2012a,b]. The transcriptome changes in

exposed individuals include the genes involved in differ-

ent pathways. Argos et al. [2006] examined the effect of

chronic arsenic exposure on the transcriptome of periph-

eral blood lymphocytes from individuals in the Health

Effects of Arsenic Longitudinal Study. The authors found

a large number of genes (468) that were differentially

expressed between participants with and without arsenical

skin lesions. Gene ontology analysis revealed that the

genes differentially expressed in individuals with arseni-

cal skin lesions compared with exposed individuals with-

out such lesions are involved in RNA metabolism,

hydrolase activity, ribonucleoprotein complex, translation,

cellular protein catabolism, amino acid activation, trans-

port and transporter activity, and glycoprotein metabo-

lism, consistent with the notion that arsenic has multiple

targets and acts through complex mechanisms of action.

Fry et al. examined transcriptome profiles in the cord

blood of newborns whose mothers were exposed to ar-

senic during pregnancy and identified expression signa-

tures that were highly predictive of prenatal arsenic

exposure [Fry et al., 2007]. Pathway analysis revealed

that arsenic exposure modulated the gene transcripts

involved in stress, inflammation, and cell death, demon-

strating a strong impact of a mother’s arsenic exposure

on fetal transcriptome.

A transcriptome analysis of PBMCs from dioxin-

exposed human subjects showed modest alterations of

gene expression [McHale et al., 2007]. Among differen-

tially expressed genes, the authors identified several his-

tone-encoding genes and genes involved in cell



TABLEII.

Om

ics

Stu

die

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Exp

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bee

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Agen

t/ex

posu

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cell

san

alyze

dO

mic

spla

tform

Gen

epat

hw

ays

Ref

eren

ce

Tra

nsc

ripto

me

stu

die

s

Ben

zene

PB

MC

sA

ffym

etri

xH

GU

133

Imm

une

resp

onse

,cy

tokin

esF

orr

est

etal

.[2

005]

Ben

zene

PB

MC

sH

um

anR

ef-8

Bea

dC

hip

san

dA

ffym

etri

x

Hum

anU

133

Apopto

sis,

lipid

met

aboli

smM

cHal

eet

al.

[2009]

Ben

zene

PB

MC

sIl

lum

ina

Hum

anR

ef-8

V2

Bea

dC

hip

Imm

une

resp

onse

McH

ale

etal

.[2

011]

Ars

enic

Cord

blo

od

Aff

ym

etri

xH

GU

133

Plu

s2.0

arra

ys

Str

ess,

infl

amm

atio

n,

met

alex

posu

re,

apopto

sis

Fry

etal

.[2

007]

Ars

enic

PB

LA

ffym

etri

xH

GU

133A

arra

ys

RN

Aan

dpro

tein

met

aboli

sm,

cell

ula

rtr

ansp

ort

,

signal

tran

sduct

ion

Arg

os

etal

.[2

006]

Dio

xin

PB

MC

sA

ffym

etri

xG

eneC

hip

arra

yC

ellu

lar

gro

wth

/pro

life

rati

on,

glu

cose

met

aboli

sm,

apopto

sis,

DN

Are

pli

cati

on,

reco

mbin

atio

n/r

epai

r

McH

ale

etal

.[2

007]

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onse

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ress

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etab

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life

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sis

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get

al.

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etal

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007]

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oke

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ial

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-seq

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ina

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ativ

est

ress

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ve

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sport

,

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tor

of

nic

oti

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ylc

holi

ne

rece

pto

rs

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ket

tet

al.

[2012]

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cosm

oke

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MC

san

dth

ehum

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monocy

tic

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line

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min

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ants

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tein

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erone

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ing,

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uit

in/p

rote

oso

me

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ght

etal

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012]

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cosm

oke

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blo

od

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ent

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gonucl

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de

mic

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rays

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une

funct

ion

and

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004]

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gy)

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cinogen

met

aboli

sm,

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ativ

est

ress

resp

onse

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sis

van

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enet

al.

[2007]

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cosm

oke

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way

epit

hel

ial

cell

sA

ffym

etri

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eChip

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tic

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etio

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[2004]

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tic

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011]

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cer)

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sten

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ipher

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min

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tion27K

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rote

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ouple

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cepto

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012]

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centa

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min

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finiu

m27K

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ayO

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est

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ret

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[2011]

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oke

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of

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eest

eret

al.

[2011]


486 Wild et al.

proliferation, cell death, and immunological pathways.

Interestingly, these alterations were detected more than

20 years after the exposure, and the magnitude of the

gene expression changes appears to correlate with dioxin

levels. Hochstenbach et al. investigated transcriptome

changes in umbilical cord blood samples in response to

dioxin and acrylamide as well as several other dietary

genotoxic and nongenotoxic carcinogens. In parallel, the

authors measured internal exposure using a reporter gene

assay (dioxin) and hemoglobin adduct levels (acrylamide)

[Hochstenbach et al., 2012]. The results showed differen-

tial transcriptomic responses to dioxin and acrylamide ex-

posure between the sexes. Specifically, dioxin exposure

altered the genes involved in TNF-alpha-NF-kB signaling,

whereas acrylamide exposure induced activation of the

Wnt-pathway in boys [Hochstenbach et al., 2012]. These

findings may provide potential mechanistic explanation

for gender-related differences in susceptibility to child-

hood leukemia.

Wang et al. [2005] performed transcriptome analysis of

whole blood from individuals before and after occupa-

tional exposure to metal fumes and identified differen-

tially expressed genes involved in biological processes

related to inflammatory response, oxidative stress, cell

signalling, cell cycle, and apoptosis. This is a rare exam-

ple of a paired sampling study design with pre-exposed

and post-exposed individuals, allowing small changes in

the transcriptome to be detected using a population-based

cohort. Peretz et al. [2007] analyzed transcriptome pro-

files in PBMCs from individuals exposed to diesel

exhaust and identified expression changes. Diesel exhaust

exposure was associated with transcriptional changes in

inflammation and oxidative stress pathways; however, this

was an exploratory study performed on a small number of

subjects, and further studies are needed to substantiate a

time-dependent effect of the exposure on biological

processes.

The studies described above demonstrated that changes

in the transcriptome may be interpreted as signatures of

exposures and suggested potential mechanisms by which

these agents promote carcinogenesis (Table II). In addi-

tion, exposure-specific transcriptome changes can be

detected not only in target tissue (e.g., the airway epithe-

lium in the case of tobacco smoking) but also in surrogate

tissue (such as peripheral blood cells and cord blood).

Several recent studies, while preliminary, have begun to

characterize the transcriptome associated with specific ex-

posure using the power of massively parallel sequencing

technologies [Hackett et al., 2012; Wright et al., 2012].

Although both microarray and NGS platforms are robust

techniques and good reproducibility of transcriptome pat-

terns between these platforms has been obtained, NGS

has several advantages over expression arrays: NGS has a

greater dynamic range, is compatible with smaller

amounts of RNA, allows the discovery of new transcripts

and splice variants, and has the potential to analyze non-

coding RNAs. One of the challenges of transcriptomic

profiling related to environmental exposures is to address

the variability associated with life stages and ageing as

well as differences arising from the analysis of complex

tissues that contain multiple cell types.

EXPOSURES AND EPIGENETIC CHANGES

The epigenome is defined as the totality of epigenetic

marks present along the DNA sequence of the genome in

a particular cell type. The epigenome plays a critical role

in the establishment and maintenance of cell identity

through a stable propagation of gene activity states from

mother to daughter cells. DNA methylation, histone mod-

ifications, and RNA-mediated gene silencing are the main

epigenetic mechanisms that govern the gene expression

programme over the lifetime of an organism. DNA meth-

ylation, which refers to the attachment of a methyl group

to a cytosine base that is located 50 to a guanosine base

in a CpG dinucleotide, is the most extensively studied

epigenetic mark in both normal and cancer cells. Histone

modifications include a variety of post-translational modi-

fications (acetylation, methylation, phosphorylation, and

ubiquitination) of histones (specialized proteins associated

with genomic DNA to form the DNA–protein complex

known as chromatin). Noncoding RNAs, in the form of

either small RNAs (microRNAs) or long noncoding

RNAs (lncRNAs), play an important role in the stable

and heritable regulation of gene expression programmes.

Epigenetic mechanisms modulate the gene expression

programme in response to environmental exposures, and

it has been suggested that the epigenome functions as an

interface between the genome and the environment [Her-

ceg, 2007; Feil and Fraga, 2011; Herceg and Vaissiere,

2011; Hou et al., 2012]. Although the epigenome is

dynamic owing to the reversible and plastic nature of epi-

genetic states [Biron et al., 2004; Milosavljevic, 2011;

Barouki et al., 2012], an altered epigenome may represent

more stable signatures of environmental exposure than

changes in the transcriptome. The effects of environmen-

tal epimutagens on the epigenome have been either dem-

onstrated experimentally using different animal and

cellular models or inferred from epidemiological studies

[Feil and Fraga, 2011; Herceg and Vaissiere, 2011].

Epigenetic changes in tumor tissues have been exten-

sively investigated, and in some cases links to environ-

mental exposures have been established. For example, a

study of DNA methylation in lung cancer of tobacco

smokers and alcohol drinkers identified gene-specific dif-

ferences in methylation patterns [Vaissiere et al., 2009].

In hepatocellular carcinoma, aberrant DNA methylation

was able to distinguish tumors associated with hepatitis B

and C virus infection and alcohol intake [Hernandez-



Vargas et al., 2010; Lambert et al., 2011]. However, it

remains unclear to what extent exposure-associated epige-

netic changes can be detected in normal, nontransformed

cells (such as peripheral blood cells) and as to their

applicability as biomarkers of exposure and disease risk.

Environmental exposures to different chemical and bio-

logical agents influence epigenetic states, although studies

examining the impact of epimutagens on the epigenome-

wide patterns in human populations are currently limited

to tobacco smoking [Wan et al., 2012], benzene [Bollati

et al., 2007; Ji et al., 2010; Fustinoni et al., 2012], air

pollution [Tarantini et al., 2009; Bollati et al., 2010], and

arsenic [Lambrou et al., 2012; Kile et al., 2012; Smeester

et al., 2011; Bailey et al., 2013] (Table II). The majority

of these studies used blood cells to examine either meth-

ylation levels in a subset of specific genes or repetitive

elements, although recent studies have used microarrays

with genome-wide coverage. The results obtained have

identified potential early “driver” changes and biomarkers

of exposure. They have also suggested a plausible under-

lying mechanism that may play a role in disease develop-

ment. Below, we discuss human epigenomic studies (with

the focus on DNA methylation) of tobacco smoking,

which serve as an example of studies where epigenetic

alterations have been measured in relation to human

exposure.

A study of epigenome-wide placental DNA methylation

(using Illumina Infinium 27K methylation arrays) in rela-

tion to maternal smoking during pregnancy revealed a

large number of CpG sites that were altered among smok-

ers [Suter et al., 2011]. A transcriptome analysis (using

Illumina HT-12 arrays) performed in parallel on the same

samples identified a large set of loci that were differen-

tially expressed. A comparative analysis revealed that the

placenta of smokers exhibited significantly altered expres-

sion of 623 genes and methylation of 1024 CpG sites.

Among these, a significant correlation between transcrip-

tion and methylation was identified in as many as 438

genes, with an enrichment of genes involved in the oxida-

tive stress pathway [Suter et al., 2011]. These findings

suggest that in utero exposure does not induce global or

indiscriminate alterations in the DNA methylome of pla-

centa but rather induces methylation changes at specific

sites, resulting in a significant deregulation of the

transcriptome.

In a study of epigenome-wide methylation in the cord

blood of newborns in relation to maternal smoking during

pregnancy, differential DNA methylation changes in a

specific set of genes were found to be associated with

tobacco exposure [Joubert et al., 2012]. Using the Infin-

ium HumanMethylation450 BeadChip microarray (which

interrogates 473,844 CpG sites), the authors examined

methylation changes in a large series (1,062) of newborn

cord blood samples from a mother–child cohort in rela-

tion to maternal smoking. Cotinine measurement, a

validated and objective biomarker of smoking, was also

performed on the corresponding maternal plasma samples.

A significant association was found between maternal

smoking during pregnancy and cord blood methylation

changes at 26 CpG sites mapping to 10 genes. The main

findings have been replicated in an independent birth

cohort, although a modest sample size (18 samples from

both smoking and nonsmoking mothers) was used for the

replication [Joubert et al., 2012]. Curiously, the CYP1A1and AHRR genes, encoding the proteins known to be

involved in detoxification of compounds from tobacco

smoke, were found to be among the differentially methyl-

ated genes, suggesting a potential epigenetic mechanism

involved in adverse effects associated with in utero expo-

sure to tobacco smoke. Remarkably, an independent study

on adults using the same Infinium 450K BeadChip array

identified one of these genes (AHRR) as being differen-

tially methylated in both lymphoblasts and pulmonary al-

veolar macrophages from smokers [Monick et al., 2012],

suggesting that the same epigenetic changes found in

exposed adults may be present in tissues of newborns

exposed in utero.

A recent study using a randomized intervention trial

investigated the ability of specific diets to alter methyla-

tion levels in DNA extracted from peripheral blood of

heavy smokers [Scoccianti et al., 2011]. The results

revealed that specific diets may modulate tobacco-induced

changes in global DNA methylation levels (as measured

by methylation at long interspersed elements [LINE-1]) in

peripheral blood cells. Similarly, diet and multivitamin

use were found to counteract the presence of aberrant

methylation of specific genes in cells exfoliated from the

aerodigestive tract of current and former smokers [Stidley

et al., 2010]. Although the mechanism remains unclear,

these findings provide evidence that exposure-specific epi-

genetic changes are reversible, consistent with the plastic-

ity and intrinsic reversibility of epigenome states, and

represent potential targets for preventive interventions.

Such short-term interventions also add evidence to the

observational studies that seek to establish an association

between specific patterns of epigenetic (or other omic-

based analyses) and specific exposures.

Studies of microRNA changes have also been carried

out in subjects exposed to tobacco smoke. Schembri et al.

[2009] performed whole-genome microRNA expression

profiling in the bronchial airway epithelium from current

and never smokers and found 28 miRNAs to be differen-

tially expressed, with most being downregulated in smok-

ers. In parallel, the authors performed transcriptome

profiling of the same in vivo samples and identified spe-

cific protein-coding mRNAs that are potentially regulated

by smoking-induced alterations in microRNA levels.

These observations support a role for microRNAs in regu-

lating the response of target tissues to environmental

exposures.


488 Wild et al.

Together, the studies on epigenetic alterations in human

populations, while limited to exposures to a few agents

and whole-genome coverage, indicate that changes in the

epigenome may be exploited as biomarkers of exposure

and that specific changes can be detected in peripheral

blood. In addition, they demonstrate that specific epige-

netic changes associated with in utero exposure can be

detected in the cord blood of newborns. Further studies of

epigenome changes in populations exposed to known and

suspected carcinogens are needed to establish whether

epigenetic changes can serve as reliable and quantitative

biomarkers of exposure and to identify changes that are

causally involved in cancer development. Similar to the

situation for the transcriptome, the cellular heterogeneity

of virtually all human tissues represents an important

challenge for the interpretation of epigenomic data [Her-

ceg and Hernandez-Vargas, 2011]. Ongoing and future

epigenomic studies, including those that use new-genera-

tion microarrays and NGS platforms, are expected to gen-

erate a comprehensive portrait of the “normal” epigenome

in tissues so that exposure-specific fingerprints can be

established.

EXPOSURES ANDMETABOLIC CHANGES

The metabolome is the sum of all low molecular

weight metabolites present in a particular biological sam-

ple. It is highly complex with more than 20,000 metabo-

lites known in humans [Wishart et al., 2013]. A large

fraction of the metabolome can be measured in blood,

urine or tissues by mass spectrometry (MS) or proton nu-

clear magnetic resonance (1H-NMR) spectroscopy. Meta-

bolic profiles obtained using these techniques constitute

the measurable part of the metabolic phenotypes (or

metabotypes). They show large interindividual differences

and are characteristic of an individual at a particular time

of his/her life [Assfalg et al., 2008]. These differences are

also seen at the population level when comparing for

example populations from different regions [Holmes

et al., 2008; Saadatian-Elahi et al., 2009]. Metabotypes

are both genetically and environmentally determined. A

twin study on the variability of the plasma and urine

metabolomes has shown that over 60% of this variability

depends on the environment [Nicholson et al., 2011].

As the most downstream expression of biochemical

pathways, the metabolome integrates all interactions

between biochemical entities in cells or tissues and the

environment. Its characterization thus offers considerable

promise to discover new biomarkers for various exposures

and to determine the cause of diseases strongly associated

with environment or lifestyle.

Metabolomics is the approach in which metabolic pro-

files from samples collected under diverse conditions

(e.g., different exposures, treatments or physiologic states)

are systematically compared. Two different metabolomic

approaches can be distinguished. In untargeted metabolo-

mics, hundreds to thousands of metabolites are detected

in biological samples by 1H-NMR spectroscopy or MS.

Metabolites characteristic of different exposures are iden-

tified using multivariate statistical methods and their

chemical structure established by comparison of their

spectra with those stored in large metabolite databases

[Dunn et al., 2011]. In targeted metabolomics, a limited

number (commonly tens to hundreds) of metabolites

known a priori are quantified, most often by MS which

shows a high sensitivity and specificity for metabolite

detection.

Untargeted metabolomics has been used to identify

metabolites over or underexpressed in individuals with

different exposures (Table III). New biomarkers of food

intake have been identified such as proline betaine for cit-

rus fruits, S-methyl-L-cysteine sulfoxide for cruciferous

vegetables or trimethylamine-N-oxide for fish. The con-

siderable diversity of constituents known in foods (over

13,000 compounds described in various foods) [Wishart

et al., 2013] suggests that many more markers will be

identified in the future using such metabolomic

approaches.

First studies on populations, carried out with the

more robust 1H-NMR spectroscopy techniques also

revealed metabolic profiles characteristic of the expo-

sure to different diets or dietary patterns. These meta-

bolic profiles were principally made of house-keeping

metabolites present at higher levels in urine or blood,

like amino acids or organic acids. More sensitive MS

techniques will be needed to measure the large diver-

sity of food-derived compounds present at too low con-

centrations to be detected by NMR spectroscopy. Two

targeted MS-based metabolomic studies on populations

showed correlations of phospholipids or their constitut-

ing fatty acids with dietary patterns and the consump-

tion of different foods [Saadatian-Elahi et al., 2009;

Altmaier et al., 2011].

Metabolomics can also be used to characterize the

effect of other environmental exposures apart from diet.

Alterations of amino acid or lipid metabolism subsequent

to occupational exposure to welding fumes or to smoking

have been described [Wang-Sattler et al., 2008; Kuo

et al., 2012]. These metabolic changes may reveal bio-

chemical mechanisms linking exposure to disease risk.

However, it is still uncertain to what extent such meta-

bolic changes will allow classification of individuals

according to their exposure to pollutants or contaminants

because of the many possible confounding factors.

Contaminants and their metabolites have been com-

monly measured in urine or plasma and used as bio-

markers of exposure to study associations with disease

outcomes or intermediate endpoints. However, the num-

ber of compounds commonly measured by targeted MS-



TABLEIII.H

um

an

Met

ab

olo

mic

Stu

die

son

the

Exp

oso

me

Exposu

reN

um

ber

of

subje

cts

Type

of

study

Bio

spec

imen

Anal

yti

cal

tech

niq

ue

Met

abolo

mic

sM

etab

oli

tes

alte

red

Ref

eren

ce

Die

t

Cru

cife

rous

veg

etab

les

20

mal

eIn

terv

enti

on

Uri

ne

1H

-NM

RU

nta

rget

edS

-met

hyl-

L-c

yst

eine

sulf

oxid

eE

dm

ands

etal

.[2

011]

Coco

apow

der

10

Inte

rven

tion

Uri

ne

LC

-MS

Unta

rget

edA

lkal

oid

san

dpoly

phen

ols

Llo

rach

etal

.[2

009]

Tea

(bla

ckor

gre

en)

17

mal

eIn

terv

enti

on

Uri

ne/

pla

sma

LC

-MS

Unta

rget

edH

ippuri

cac

idan

d1,3

-

dih

ydro

xyphen

yl-

2-O

-sulf

ate

Van

Dors

ten

etal

.[2

006]

Low

mea

t,hig

hre

dm

eat,

veg

etar

ian

die

ts

10

mal

eIn

terv

enti

on

Uri

ne

1H

-NM

RU

nta

rget

edC

arnit

ine,

trim

ethyla

min

e-N

-oxid

e,ta

uri

ne,

N-a

cety

l-5-h

ydro

xytr

ypta

min

e,

glu

tam

ine

asso

ciat

edto

hig

hm

eat;

p-h

ydro

xy

phen

yla

ceta

tean

d

trim

ethyll

ysi

ne

asso

ciat

edto

veg

etar

ian

die

t

Ste

lla

etal

.[2

006]

Sal

mon,

rasp

ber

ry,

bro

ccoli

and

whole

gra

ince

real

s

24

Inte

rven

tion

Uri

ne

FIA

-MS

Unta

rget

edT

rim

ethyla

min

e-N

-oxid

eas

soci

ated

tosa

lmon

and

caff

eic

acid

and

met

hyle

pic

atec

hin

sulf

ate

este

r

tora

spber

ry

Llo

yd

etal

.[2

011]

Fiv

edif

fere

nt

mea

ls7

Inte

rven

tion

Uri

ne

1H

-NM

RU

nta

rget

edT

artr

ate,

pro

line

bet

aine,

hip

pura

te,

and

4-h

ydro

xyhip

pura

teas

soci

ated

tofr

uit

inta

ke

Hei

nzm

ann

etal

.[2

012]

Low

-an

dhig

hgly

cem

ic

index

hab

itual

die

t

77

over

wei

ght

Inte

rven

tion

Uri

ne

1H

-NM

RU

nta

rget

edF

orm

ate

asso

ciat

edto

hig

hgly

cem

ic

index

die

t

Ras

muss

enet

al.

[2012]

Cit

rus

fruit

299

(IN

TE

RM

AP

study)

Obse

rvat

ional

,

inte

rven

tion

Uri

ne

1H

-NM

RU

nta

rget

edP

roli

ne

bet

aine

asso

ciat

edto

citr

us

fruit

inta

ke

Hei

nzm

ann

etal

.[2

010]

Die

tary

pro

tein

sfr

om

veg

etab

leor

anim

also

urc

es

1,1

83

(IN

TE

RM

AP

study)

Obse

rvat

ional

Uri

ne

1H

-NM

RU

nta

rget

edD

isti

nct

ion

of

chin

ese

subje

cts

acco

rdin

gto

the

die

tary

sourc

e

of

pro

tein

s(v

eget

able

or

anim

al)

Holm

eset

al.

[2008]

Pro

tein

inta

ke

911

(YF

Sst

udy)

Obse

rvat

ional

Pla

sma

1H

-NM

RU

nta

rget

edV

alin

e,phen

yla

lanin

e,ty

rosi

ne

and

glu

tam

ine

Wurt

zet

al.

[2012]

Food

gro

ups

and

die

tary

pat

tern

s

239

mal

e(K

OR

Ast

udy)

Obse

rvat

ional

Pla

sma

LC

-MS

Tar

get

edP

hosp

holi

pid

sas

soci

ated

toth

ety

pe

of

die

t;hig

her

satu

rati

on

and

short

erch

ain

length

asso

ciat

edto

hig

hdie

tary

fiber

inta

ke

Alt

mai

eret

al.

[2011]

Var

ious

food

gro

ups

3,0

03

(EP

ICco

hort

)O

bse

rvat

ional

Pla

sma

GC

-MS

Tar

get

edP

lasm

aphosp

holi

pid

fatt

yac

ids

corr

elat

edto

fish

,oli

ve

oil

and

mar

ger

ine

inta

ke

Saa

dat

ian-E

lahi

etal

.

[2009]

Lac

toveg

etar

ians

and

om

niv

oro

us

die

ts

81

Obse

rvat

ional

Uri

ne

1H

-NM

RU

nta

rget

edA

min

oac

ids

and

org

anic

acid

sX

uet

al.

[2010]

Fiv

edie

tary

pat

tern

s1512

(GE

MIN

AK

AR

cohort

)

Obse

rvat

ional

Pla

sma

1H

-NM

RU

nta

rget

edO

ver

all

met

aboli

cre

sponse

asso

ciat

ed

todie

tary

exposu

re

Per

e-T

repat

etal

.[2

010]

Die

tary

pat

tern

s160

Obse

rvat

ional

Uri

ne/

pla

sma

1H

-NM

RU

nta

rget

edO

-Ace

tylc

arnit

ine

asso

ciat

edto

red

mea

tin

take

and

phen

yla

cety

lglu

ta-

min

eto

veg

etab

lein

take

O’S

ull

ivan

etal

.[2

011]

Occ

up

ati

on

al

exp

osu

re

Wel

din

gfu

mes

Ship

yar

dw

ork

ers

(35

expose

d;

16

unex

pose

d)

Obse

rvat

ional

Uri

ne

1H

-NM

RU

nta

rget

edG

lyci

ne,

tauri

ne,

bet

aine

Kuo

etal

.[2

012]

TABLE1.

(con

tin

ued

).

Exposu

reN

um

ber

of

subje

cts

Type

of

study

Bio

spec

imen

Anal

yti

cal

tech

niq

ue

Met

abolo

mic

sM

etab

oli

tes

alte

red

Ref

eren

ce

Jet-

fuel

exposu

reM

ilit

arbas

ew

ork

ers

(37

expose

d;

114

unex

pose

d)

Obse

rvat

ional

Exhal

edbre

ath

GC

-MS

Tar

get

ed12

vola

tile

hydro

carb

ons

incr

ease

dP

leil

etal

.[2

011]

Sm

okin

g283

(KO

RA

cohort

)O

bse

rvat

ional

Ser

um

LC

-MS

Tar

get

ed23

lipid

met

aboli

tes

alte

red

out

of

198

met

aboli

tes

mea

sure

d

Wan

g-S

attl

eret

al.

[2008]

Ph

ysi

cal

exer

cise

80

mm

axim

alru

n12

Inte

rven

tion

Uri

ne

1H

-NM

RU

nta

rget

ed22

met

aboli

tes

affe

cted

incl

udin

g

bra

nch

edch

ain

amin

oac

ids

and

org

anic

acid

s

Pec

hli

van

iset

al.

[2010]

Moder

atel

yin

tense

runnin

g

13

Inte

rven

tion

Pla

sma

LC

-MS

Unta

rget

edT

ransc

ient

incr

ease

of

med

ium

and

long

chai

nac

ylc

arnit

ines

Leh

man

net

al.

[2010]

Inte

nsi

ve

anae

robic

exer

cise

22

fem

ale

Inte

rven

tion

Uri

ne

1H

-NM

RU

nta

rget

edC

reat

inin

e,la

ctat

e,pyru

vat

e,al

anin

e,

b-h

ydro

xybuty

rate

,ac

etat

e,an

d

hypoxan

thin

e

Enea

etal

.[2

010]

Reg

ula

rphysi

cal

exer

cise

6,2

23

(YF

Sst

udy)

Obse

rvat

ional

Pla

sma

1H

-NM

RU

nta

rget

edIs

ole

uci

ne,

phen

yla

lanin

e,ty

rosi

ne,

tota

lfa

tty

acid

san

dfa

tty

acid

satu

rati

on

Wurt

zet

al.

[2012]

Ob

esit

y

Obes

ity

15

obes

ean

d15

lean

Obse

rvat

ional

Ser

um

LC

-MS

Tar

get

ed12

amin

oac

ids

and

lipid

sO

ber

bac

het

al.

[2011]

Over

wei

ght/

obes

ity

30

over

wei

ght/

obes

e

and

30

lean

Obse

rvat

ional

Pla

sma

LC

-MS

Unta

rget

edL

yso

-phosp

hat

idylc

holi

nes

,tw

o

bra

nch

ed-c

hai

nam

ino

acid

s,tw

o

arom

atic

amin

oac

ids

Kim

etal

.[2

010]

Wei

ght

gai

n21

post

men

opau

sal

wom

en

wit

hbre

ast

cance

rre

ceiv

ing

chem

oth

erap

y

Obse

rvat

ional

Ser

um

1H

-NM

RU

nta

rget

edB

asel

ine

lact

ate

and

alan

ine

pro

gnost

icfo

rw

eight

gai

n

Keu

net

al.

[2009]

Obes

ity

74

obes

ean

d67

lean

subje

cts

Obse

rvat

ional

Ser

um

LC

-MS

Tar

get

edB

ranch

edch

ain

amin

oac

ids

New

gar

det

al.

[2009]

Obes

ity

14

pai

rsof

MZ

twin

shig

hly

dis

cord

ant

for

obes

ity

Obse

rvat

ional

Ser

um

LC

-MS

Unta

rget

edL

yso

phosp

hat

idylc

holi

nes

incr

ease

d

and

phosp

holi

pid

sdec

reas

ed(o

ut

of

331

lipid

sdet

ecte

d)

Pie

tila

inen

etal

.[2

007]

Insu

lin

sensi

tivit

yO

ver

wei

ght

post

men

opau

sal

wom

en

Obse

rvat

ional

Pla

sma

LC

-MS

Tar

get

edL

arge

neu

tral

amin

oac

ids

and

fatt

y

acid

sin

ver

sely

asso

ciat

edto

insu

lin

sensi

tivit

y

Huff

man

etal

.[2

009]

1H

-NM

R,

pro

ton

nucl

ear

mag

net

icre

sonan

cesp

ectr

osc

opy;

LC

-MS

,li

quid

chro

mat

ogra

phy-m

ass

spec

trom

etry

;F

IA-M

S,

flow

inje

ctio

nan

alysi

s-m

ass

spec

trom

etry

;G

C-M

S,

gas

chro

mat

ogra

phy-m

ass

spec

trom

etry

.

TA

BL

EII

I.(c

on

tin

ued

)

based methods rarely exceeds 20 metabolites in a single

analytical run [Centers for Disease Control and Preven-

tion, 2009]. More comprehensive analytical methods for

measurement of larger sets of contaminants and emerging

pollutants still need to be developed in order for Expo-

some-Wide Association Studies to become a reality [Patel

et al., 2010].

Other risk factors, such as lack of physical exercise

and obesity, have been evaluated by metabolomics (Table

III). Changes in the level of various metabolites in blood

or urine were identified after both acute and habitual

exercise. Similarly a number of endogenous metabolites

were associated with overweight, obesity and insulin re-

sistance. Characterization of these metabolic changes may

help to understand the role of these factors in disease

risk. These observations may contribute to establishing

the biological plausibility of an exposure-disease associa-

tion, provided that the same metabolic traits are also

found to be associated to disease outcomes as proposed in

the “meet-in-the-middle” concept [Chadeau-Hyam et al.,

2011]. A good illustration of this concept can be found in

the recent identification of three branched chain amino

acids and two aromatic amino acids as predictors of type

2 diabetes in the prospective Framingham Heart Study

(Table IV) [Wang et al., 2011]. The same metabolites

were also associated with insulin resistance and obesity in

three cross-sectional studies (Table III) [Newgard et al.,

2009; Kim et al., 2010; Wurtz et al., 2012, 2013]. To add

further plausibility to the role of branched chain amino

acids in the etiology of diabetes, it was shown in an ex-

perimental study on rats that dietary supplementation with

these amino acids induces insulin resistance [Newgard

et al., 2009].

There are still few applications of metabolomics to

cohort studies (Table IV). A targeted metabolomic

approach allowed identification of combinations of metab-

olites able to predict cardiovascular events in patients at

risk of cardiovascular diseases [Shah et al., 2012]. Can-

cers are also increasingly seen as metabolic diseases and

application of metabolomics may similarly reveal meta-

bolic profiles predictive of the risk of cancer. Two case–

control studies on breast and gastric cancers nested in the

European Prospective Investigation on Cancer and Nutri-

tion (EPIC) cohort showed that exposure to several

plasma phospholipid fatty acids out of 22 compounds

measured were associated with the risk of breast or gas-

tric cancers [Chajes et al., 2008, 2011]. This suggests that

either different diets or differences in the metabolism of

fatty acid may influence risk for these cancers. Prediction

of disease recurrence with metabolomics has also been

examined. Two retrospective studies showed that meta-

bolic profiles may better predict cancer recurrence than

established cancer antigens such as PSA or CA 27.29 for

prostate or breast cancers [Maxeiner et al., 2010; Asiago

et al., 2010].

FUTURE PERSPECTIVES

Laboratory sciences and omic technologies in particular

offer an unprecedented opportunity to characterize human

exposures to risk factors and to elucidate carcinogenic

mechanisms. Recent progress in omic technologies and

methods (e.g., reduction in sample volume, increased

throughput, decreased costs) has facilitated application in

epidemiological settings where large numbers of samples

are available in limited amounts. Indeed the conjunction

of prospective cohort studies with well-characterized bio-

banks with the types of technologies described in this

manuscript is particularly fortuitous. It is therefore

encouraging that examples presented in this paper indi-

cate that omic profiles can be exploited to identify novel

biomarkers of exposure and clues to causation. These bio-

markers may either be directly related to exposures (e.g.,

an exogenous risk factor and its metabolites measured in

blood) or indirectly through some biological effects trig-

gered by exposures (e.g., altered gene expression). One

exciting feature is the chance to capture a wide range of

exposures through a single measurement.

Despite the promise, measuring the exposome repre-

sents a considerable challenge due the huge diversity of

environmental and lifestyle exposures to which individu-

als are regularly exposed and for which changing patterns

are encountered throughout the lifetime. This challenge is

greater than that previously met to characterize the human

genome, constituted of only four main nucleosides, or the

proteome, made of twenty main amino acids. In fact the

processing and interpretation of data is increasingly rec-

ognized as a potential stumbling block to success [Fort-

ney and Jurisica, 2011]. Major efforts have been made

over the last few years to increase the analytical through-

put of omics laboratory analyses without compromising

analytical coverage and the quality of the data [Fuhrer

et al., 2011]. Software pipelines have been developed to

automatize the analysis of highly complex datasets [Xia

et al., 2009; Martin et al., 2010]. Progress will require

major investments in technology as well as an emphasis

on interdisciplinary research, with essential roles for epi-

demiologists, biostatisticians, bioinformaticians, as well

as laboratory scientists. Nevertheless, the promises are so

great and the value of understanding causes and preven-

tion so high that this area of research should be given far

greater priority, especially with its relevance not only to

cancer but other noncommunicable diseases.

There remain many questions in relation to the use of

omics for exposure assessment, notably with respect to

the relevance to past exposure, the value of measures

made in nontarget tissues (e.g., PMBC) and whether com-

prehensive (e.g., whole genome) or targeted (e.g., RNA

or whole exome sequencing) approaches are best suited

to the task in hand. Biomarkers for long-term exposure

are a priority and past exposure assessment remains one


492 Wild et al.

TABLEIV.P

rosp

ecti

ve

Exp

oso

me-

Wid

eA

ssoci

ati

on

Stu

die

son

Dis

ease

Ris

kan

dIn

term

edia

teE

nd

poin

ts

Outc

om

eS

ubje

cts

Foll

ow

-up

(yea

r)

Bio

-

spec

imen

Anal

yti

cal

tech

niq

ue

Met

abo-

lom

ics

Met

aboli

tes

mea

sure

dM

etab

oli

tes

alte

red

Ref

eren

ce

Type

2dia

bet

es189

case

san

d189

contr

ols

(Fra

min

gham

Hea

rtS

tudy)

12

Pla

sma

LC

-MS

Tar

get

ed>

100

lipid

sL

ipid

sof

low

erca

rbon

num

ber

and

double

bond

conte

nt

asso

ciat

edw

ith

incr

ease

d

risk

;li

pid

sof

hig

her

carb

on

num

ber

and

double

bond

conte

nt

asso

ciat

edw

ith

dec

reas

edri

sk

Rhee

etal

.

[2011]

Type

2dia

bet

es189

case

san

d189

contr

ols

(Fra

min

gham

Hea

rtS

tudy)

12

Pla

sma

LC

-MS

Tar

get

ed61

met

aboli

tes

Isole

uci

ne,

leuci

ne,

val

ine,

tyro

sine

and

phen

yla

lanin

e

asso

ciat

edto

dis

ease

risk

Wan

get

al.

[2011]

Insu

lin

resi

stan

ce

and

dis

lipid

emia

90

non-d

iabet

icsu

bje

cts

(CA

RD

IAst

udy)

20

Ser

um

GC

-MS

Tar

get

ed55

per

sist

ent

org

anic

poll

uta

nts

Sev

eral

org

anoch

lori

ne

pes

tici

des

and

poly

chlo

rinat

edbip

hen

yls

asso

ciat

edw

ith

insu

lin

resi

stan

ce

or

dysl

ipid

emia

Lee

etal

.

[2011]

Fas

ting

glu

cose

and

post

load

glu

cose

618

subje

cts

6.5

Ser

um

1H

-NM

RU

nta

rget

edB

ranch

edch

ain

amin

oac

ids,

phen

yla

lanin

e,al

anin

e,

lact

ate,

pyru

vat

e,an

dty

rosi

ne

Wurt

zet

al.

[2013]

CV

D2,0

23

pat

ients

under

goin

g

card

iac

cath

eter

izat

ion

wit

h526

dea

than

d

myoca

rdia

lin

farc

tion

even

ts

3.1

Pla

sma

LC

-MS

Tar

get

ed69

amin

oac

ids,

acylc

arnit

ines

and

lipid

sre

duce

dto

13

met

aboli

tefa

ctors

5fa

ctors

indep

enden

tly

pre

dic

t

myoca

rdia

lin

farc

tion

dea

th

Shah

etal

.

[2012]

Gas

tric

cance

r238

case

san

d626

contr

ols

(EP

IC-E

UR

OG

AS

Tco

hort

)

3.2

Pla

sma

GC

-MS

Tar

get

ed22

pla

sma

phosp

holi

pid

fatt

yac

ids

Ole

icac

id,

a-l

inole

nic

acid

,an

d

di-

hom

o-g

-lin

ole

nic

acid

Chaj

eset

al.

[2011]

Bre

ast

cance

r363

case

san

d702

contr

ols

(EP

IC-E

3N

cohort

)

7P

lasm

aG

C-M

ST

arget

ed22

pla

sma

phosp

holi

pid

fatt

yac

ids

Tra

ns-

pal

mit

ole

ican

del

aidic

acid

s

Chaj

eset

al.

[2008]



of the critical challenges in cancer epidemiology for

many exposures of interest. It will be essential to evaluate

the reliability of omic measurements over time as only

one sample, collected at baseline, is typically available in

large epidemiological studies. Another key issue concerns

the validation of omic derived biomarkers. Few bio-

markers so far discovered with omic technologies have

been properly validated in population-based studies.

Biases and confounding factors, which may induce simi-

lar biological responses to those triggered by the specific

environmental or lifestyle factors under study, will have

to be carefully considered. Furthermore, an accurate and

transparent reporting of all aspects of study design, meth-

ods and data is needed to allow adequate evaluation of

research findings. In this regard, the Strengthening the

Reporting of Observational studies in Epidemiology

(STROBE) and STROBE–Molecular Epidemiology

(STROBE-ME) guidelines [von Elm et al., 2007; Gallo

et al., 2011] should serve as a basis to improve the

reporting of research findings. This is an exciting time for

cancer epidemiology. Major progress in understanding the

complex interactions linking the exposome to health

effects may be anticipated. As cancer and other noncom-

municable diseases rise in number worldwide [Wild,

2012a,b] the tools of laboratory science must be applied

as effectively as possible to understanding their causes

and prevention.

REFERENCES

Agrawal N, Frederick MJ, Pickering CR, Bettegowda C, Chang K, Li

RJ, Fakhry C, Xie TX, Zhang J, Wang J, Zhang N, El-Naggar

AK, Jasser SA, Weinstein JN, Trevino L, Drummond JA, Muzny

DM, Wu Y, Wood LD, Hruban RH, Westra WH, Koch WM,

Califano JA, Gibbs RA, Sidransky D, Vogelstein B, Velculescu

VE, Papadopoulos N, Wheeler DA, Kinzler KW, Myers JN.

2011. Exome sequencing of head and neck squamous cell carci-

noma reveals inactivating mutations in NOTCH1. Science

333:1154–1157.

Altmaier E, Kastenmuller G, Romisch-Margl W, Thorand B, Weinberger

KM, Illig T, Adamski J, Doring A, Suhre K. 2011. Question-

naire-based self-reported nutrition habits associate with serum

metabolism as revealed by quantitative targeted metabolomics.

Eur J Epidemiol 26:145–156.

Argos M, Kibriya MG, Parvez F, Jasmine F, Rakibuz-Zaman M, Ahsan

H. 2006. Gene expression profiles in peripheral lymphocytes by

arsenic exposure and skin lesion status in a Bangladeshi popula-

tion. Cancer Epidemiol Biomarkers Prev 15:1367–1375.

Asiago VM, Alvarado LZ, Shanaiah N, Gowda GAN, Owusu-Sarfo K,

Ballas RA, Raftery D. 2010. Early detection of recurrent breast

cancer using metabolite profiling. Cancer Res 70:8309–8318.

Assfalg M, Bertini I, Colangiuli D, Luchinat C, Schafer H, Schutz B,

Spraul M. 2008. Evidence of different metabolic phenotypes in

humans. Proc Natl Acad Sci USA 105:1420–1424.

Bailey KA, Wu MC, Ward WO, Smeester L, Rager JE, Garc�ıa-Vargas

G, Del Razo L-M, Drobn�a Z, St�yblo M, Fry RC. 2013. Arsenic

and the epigenome: Interindividual differences in arsenic metabo-

lism related to distinct patterns of DNA methylation. J Biochem

Mol Toxicol 27:106–115.

Barouki R, Gluckman PD, Grandjean P, Hanson M, Heindel JJ. 2012.

Developmental origins of non-communicable disease: Implica-

tions for research and public health. Environ Health 11:42.

Beane J, Sebastiani P, Liu G, Brody JS, Lenburg ME, Spira A. 2007.

Reversible and permanent effects of tobacco smoke exposure on

airway epithelial gene expression. Genome Biol 8:R201.

Beane J, Vick J, Schembri F, Anderlind C, Gower A, Campbell J, Luo

L, Zhang XH, Xiao J, Alekseyev YO, Wang S, Levy S, Massion

PP, Lenburg M, Spira A. 2011. Characterizing the impact of

smoking and lung cancer on the airway transcriptome using

RNA-Seq. Cancer Prev Res 4:803–817.

Bingham S, Riboli E. 2004. Diet and cancer–The European prospective

investigation into cancer and nutrition. Nat Rev Cancer 4:206–

215.

Biron VL, McManus KJ, Hu N, Hendzel MJ, Underhill DA. 2004. Dis-

tinct dynamics and distribution of histone methyl-lysine deriva-

tives in mouse development. Dev Biol 276:337–351.

Bollati V, Baccarelli A, Hou L, Bonzini M, Fustinoni S, Cavallo D,

Byun HM, Jiang J, Marinelli B, Pesatori AC, Bertazzi PA, Yang

AS. 2007. Changes in DNA methylation patterns in subjects

exposed to low-dose benzene. Cancer Res 67:876–880.

Bollati V, Marinelli B, Apostoli P, Bonzini M, Nordio F, Hoxha M,

Pegoraro V, Motta V, Tarantini L, Cantone L, Schwartz J, Ber-

tazzi PA, Baccarelli A. 2010. Exposure to metal-rich particulate

matter modifies the expression of candidate microRNAs in pe-

ripheral blood leukocytes. Environ Health Perspect 118:763–768.

Carone BR, Fauquier L, Habib N, Shea JM, Hart CE, Li R, Bock C, Li

C, Gu H, Zamore PD, Meissner A, Weng Z, Hofmann HA,

Friedman N, Rando OJ. 2010. Paternally induced transgenera-

tional environmental reprogramming of metabolic gene expres-

sion in mammals. Cell 143:1084–1096.

Centers for Disease Control and Prevention, Ed. 2009. Fourth National

Report on Human Exposure to Environmental Chemicals. Down-

loaded on 24/01/2013; http://www.cdc.gov/exposurerePort/.

Chadeau-Hyam M, Athersuch TJ, Keun HC, De Iorio M, Ebbels TMD,

Jenab M, Sacerdote C, Bruce SJ, Holmes E, Vineis P. 2011.

Meeting-in-the-middle using metabolic profiling - a strategy for

the identification of intermediate biomarkers in cohort studies.

Biomarkers 16:83–88.

Chajes V, Jenab M, Romieu I, Ferrari P, Dahm CC, Overvad K, Egeberg

R, Tjonneland A, Clavel-Chapelon F, Boutron-Ruault M-C, Engel

P, Teucher B, Kaaks R, Floegel A, Boeing H, Trichopoulou A,

Dilis V, Karapetyan T, Mattiello A, Tumino R, Grioni S, Palli D,

Vineis P, Bueno-de-Mesquita HB, Numans ME, Peeters PHM,

Lund E, Navarro C, Ramon Quiros J, Sanchez-Cantalejo E, Bar-

ricarte Gurrea A, Dorronsoro M, Regner S, Sonestedt E, Wirfaelt

E, Khaw K-T, Wareham N, Allen NE, Crowe FL, Rinaldi S, Sli-

mani N, Carneiro F, Riboli E, Gonzalez CA. 2011. Plasma phos-

pholipid fatty acid concentrations and risk of gastric

adenocarcinomas in the European Prospective Investigation into

Cancer and Nutrition (EPIC-EURGAST. Am J Clin Nutr

94:1304–1313.

Chajes V, Thiebaut ACM, Rotival M, Gauthier E, Maillard V, Boutron-

Ruault MC, Joulin V, Lenoir GM, Clavel-Chapelon F. 2008.

Association between serum trans-monounsaturated fatty acids and

breast cancer risk in the E3N-EPIC study. Am J Epidemiol 167:

1312–1320.

Chen CH, Dickman KG, Moriya M, Zavadil J, Sidorenko VS, Edwards

KL, Gnatenko DV, Wu L, Turesky RJ, Wu XR, Pu YS, Groll-

man AP. 2012. Aristolochic acid-associated urothelial cancer in

Taiwan. Proc Natl Acad Sci USA 109:8241–8246.

Chen R, Mias George I, Li-Pook-Than J, Jiang L, Lam Hugo YK, Chen

R, Miriami E, Karczewski Konrad J, Hariharan M, Dewey Fred-

erick E, Cheng Y, Clark Michael J, Im H, Habegger L, Balasu-

bramanian S, O’Huallachain M, Dudley Joel T, Hillenmeyer S,


494 Wild et al.

Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K,

Boyle Alan P, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-

Carrillo M, Gallardo M, Blasco Maria A, Greenberg Peter L,

Snyder P, Klein Teri E, Altman Russ B, Butte AJ, Ashley Euan

A, Gerstein M, Nadeau Kari C, Tang H, Snyder M. 2012. Perso-

nal Omics Profiling Reveals Dynamic Molecular and Medical

Phenotypes. Cell 148:1293–1307.

Connolly PH, Caiozzo VJ, Zaldivar F, Nemet D, Larson J, Hung SP,

Heck JD, Hatfield GW, Cooper DM. 2004. Effects of exercise on

gene expression in human peripheral blood mononuclear cells. J

Appl Physiol 97:461–1469.

Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL. 2011.

Systems level studies of mammalian metabolomes: The roles of

mass spectrometry and nuclear magnetic resonance spectroscopy.

Chem Soc Rev 40:387–426.

Eady JJ, Wortley GM, Wormstone YM, Hughes JC, Astley SB, Foxall

RJ, Doleman JF, Elliott RM. 2005. Variation in gene expression

profiles of peripheral blood mononuclear cells from healthy vol-

unteers. Physiol Genom 22:402–411.

Edmands WMB, Beckonert OP, Stella C, Campbell A, Lake BG, Lindon

JC, Holmes E, Gooderham NJ. 2011. Identification of human uri-

nary biomarkers of cruciferous vegetable consumption by

metabonomic profiling. J Proteome Res 10:4513–4521.

Enea C, Seguin F, Petitpas-Mulliez J, Boildieu N, Boisseau N, Delpech

N, Diaz V, Eugene M, Dugue B. 2010. (1)H NMR-based metabo-

lomics approach for exploring urinary metabolome modifications

after acute and chronic physical exercise. Anal Bioanal Chem

396:1167–1176.

Feil R, Fraga MF. 2011. Epigenetics and the environment: Emerging

patterns and implications. Nat Rev Genet 13:97–109.

Forrest MS, Lan Q, Hubbard AE, Zhang L, Vermeulen R, Zhao X, Li

G, Wu YY, Shen M, Yin S, Chanock SJ, Rothman N, Smith MT.

2005. Discovery of novel biomarkers by microarray analysis of

peripheral blood mononuclear cell gene expression in benzene-

exposed workers. Environ Health Perspect 113:801–807.

Fortney K, Jurisica I. 2011. Integrative computational biology for cancer

research. Human Genet 130:465–481.

Fry RC, Navasumrit P, Valiathan C, Svensson JP, Hogan BJ, Luo M,

Bhattacharya S, Kandjanapa K, Soontararuks S, Nookabkaew S,

Mahidol C, Ruchirawat M, Samson LD. 2007. Activation of

inflammation/NF-kappaB signaling in infants born to arsenic-

exposed mothers. PLoS Genet 3:e207.

Fuhrer T, Heer D, Begemann B, Zamboni N. 2011. High-Throughput,

Accurate Mass Metabolome Profiling of Cellular Extracts by

Flow Injection–Time-of-Flight Mass Spectrometry. Anal Chem

83:7074–7080.

Fujimoto A, Totoki Y, Abe T, Boroevich KA, Hosoda F, Nguyen HH,

Aoki M, Hosono N, Kubo M, Miya F, Arai Y, Takahashi H,

Shirakihara T, Nagasaki M, Shibuya T, Nakano K, Watanabe-

Makino K, Tanaka H, Nakamura H, Kusuda J, Ojima H, Shimada

K, Okusaka T, Ueno M, Shigekawa Y, Kawakami Y, Arihiro K,

Ohdan H, Gotoh K, Ishikawa O, Ariizumi S, Yamamoto M,

Yamada T, Chayama K, Kosuge T, Yamaue H, Kamatani N,

Miyano S, Nakagama H, Nakamura Y, Tsunoda T, Shibata T,

Nakagawa H. 2012. Whole-genome sequencing of liver cancers

identifies etiological influences on mutation patterns and recurrent

mutations in chromatin regulators. Nat Genet 44:760–764.

Fustinoni S, Rossella F, Polledri E, Bollati V, Campo L, Byun HM,

Agnello L, Consonni D, Pesatori AC, Baccarelli A, Bertazzi PA.

2012. Global DNA methylation and low-level exposure to ben-

zene. La Medicina del lavoro 103:84–95.

Gallo V, Egger M, McCormack V, Farmer PB, Ioannidis JPA, Kirsch-

Volders M, Matullo G, Phillips DH, Schoket B, Stromberg U,

Vermeulen R, Wild C, Porta M, Vineis P. 2011. STrengthening

the Reporting of OBservational studies in Epidemiology –

Molecular Epidemiology (STROBE-ME): An extension of the

STROBE Statement. PLoS Med 8:e1001117.

Ghosh P, Banerjee M, Giri AK, Ray K. 2008. Toxicogenomics of

arsenic: Classical ideas and recent advances. Mutat Res 659:293–

301.

Gouas DA, Villar S, Ortiz-Cuaran S, Legros P, Ferro G, Kirk GD, Lesi

OA, Mendy M, Bah E, Friesen MD, Groopman J, Chemin I, Hai-

naut P. 2012. TP53 R249S mutation, genetic variations in HBX

and risk of hepatocellular carcinoma in The Gambia. Carcinogen-

esis 33:1219–1224.

Guichard C, Amaddeo G, Imbeaud S, Ladeiro Y, Pelletier L, Maad IB,

Calderaro J, Bioulac-Sage P, Letexier M, Degos F, Clement B,

Balabaud C, Chevet E, Laurent A, Couchy G, Letouze E, Calvo

F, Zucman-Rossi J. 2012. Integrated analysis of somatic muta-

tions and focal copy-number changes identifies key genes and

pathways in hepatocellular carcinoma. Nature Genetics 44:694–

698.

Hackett NR, Butler MW, Shaykhiev R, Salit J, Omberg L, Rodriguez-

Flores JL, Mezey JG, Strulovici-Barel Y, Wang G, Didon L,

Crystal RG. 2012. RNA-Seq quantification of the human small

airway epithelium transcriptome. BMC Genom 13:82.

Heinzmann SS, Brown IJ, Chan Q, Bictash M, Dumas M-E, Kochhar S,

Stamler J, Holmes E, Elliott P, Nicholson JK. 2010. Metabolic

profiling strategy for discovery of nutritional biomarkers: Proline

betaine as a marker of citrus consumption. Am J Clin Nutr

92:436–443.

Heinzmann SS, Merrifield CA, Rezzi S, Kochhar S, Lindon JC, Holmes

E, Nicholson JK. 2012. Stability and Robustness of Human Meta-

bolic Phenotypes in Response to Sequential Food Challenges. J

Proteome Res 11:643–655.

Herceg Z. 2007. Epigenetics and cancer: Towards an evaluation of the

impact of environmental and dietary factors. Mutagenesis 22:91–

103.

Herceg Z, Hernandez-Vargas H. 2011. New concepts of old epigenetic

phenomena and their implications for selecting specific cell popu-

lations for epigenomic research. Epigenomics 3:383–386.

Herceg Z, Vaissiere T. 2011. Epigenetic mechanisms and cancer: An

interface between the environment and the genome. Epigenetics

6:804–819.

Hernandez-Vargas H, Lambert MP, Le Calvez-Kelm F, Gouysse G,

McKay-Chopin S, Tavtigian SV, Scoazec JY, Herceg Z. 2010.

Hepatocellular carcinoma displays distinct DNA methylation sig-

natures with potential as clinical predictors. PloS One 5:e9749.

Hochstenbach K, van Leeuwen DM, Gmuender H, Gottschalk RW,

Lovik M, Granum B, Nygaard U, Namork E, Kirsch-Volders M,

Decordier I, Vande Loock K, Besselink H, Tornqvist M, von Ste-

dingk H, Rydberg P, Kleinjans JC, van Loveren H, van Delft JH.

2012. Global gene expression analysis in cord blood reveals gen-

der-specific differences in response to carcinogenic exposure in

utero. Cancer Epidemiol Biomarkers Prev 21:1756–1767.

Hollstein M, Sidransky D, Vogelstein B, Harris CC. 1991. p53 mutations

in human cancers. Science 253:49–53.

Holmes E, Loo RL, Stamler J, Bictash M, Yap IKS, Chan Q, Ebbels T,

De Iorio M, Brown IJ, Veselkov KA, Daviglus ML, Kesteloot H,

Ueshima H, Zhao L, Nicholson JK, Elliott P. 2008. Human meta-

bolic phenotype diversity and its association with diet and blood

pressure. Nature 453:396–400.

Hou L, Zhang X, Wang D, Baccarelli A. 2012. Environmental chemical

exposures and human epigenetics. Int J Epidemiol 41:79–105.

Huffman KM, Shah SH, Stevens RD, Bain JR, Muehlbauer M, Slentz

CA, Tanner CJ, Kuchibhatla M, Houmard JA, Newgard CB,

Kraus WE. 2009. Relationships Between Circulating Metabolic

Intermediates and Insulin Action in Overweight to Obese, Inac-

tive Men and Women. Diabetes Care 32:1678–1683.



IARC. 2012a. IARC Monographs on the Evaluation of Carcinogenic

Risks to Humans, Volume 100C, A Review of Human Carcino-

gens: Arsenic, Metals, Fibres, and Dusts Occupations. Lyon,

France, International Agency for Research on Cancer.

IARC. 2012b. IARC Monographs on the Evaluation of Carcinogenic

Risks to Humans, Volume 100F, A Review of Human Carcino-

gens: Chemical Agents and Related Occupations. Lyon, France,

International Agency for Research on Cancer.

Idaghdour Y, Storey JD, Jadallah SJ, Gibson G. 2008. A genome-wide

gene expression signature of environmental geography in leuko-

cytes of Moroccan Amazighs. PLoS Genet 4:e1000052.

Ji Z, Zhang L, Peng V, Ren X, McHale CM, Smith MT. 2010. A com-

parison of the cytogenetic alterations and global DNA hypome-

thylation induced by the benzene metabolite, hydroquinone, with

those induced by melphalan and etoposide. Leukemia 24:986–

991.

Joubert BR, Haberg SE, Nilsen RM, Wang X, Vollset SE, Murphy SK,

Huang Z, Hoyo C, Midttun O, Cupul-Uicab LA, Ueland PM, Wu

MC, Nystad W, Bell DA, Peddada SD, London SJ. 2012. 450K

Epigenome-Wide Scan Identifies Differential DNA Methylation

in Newborns Related to Maternal Smoking during Pregnancy. En-

viron Health Perspect 120:1425–1431.

Kawai T, Morita K, Masuda K, Nishida K, Shikishima M, Ohta M, Saito

T, Rokutan K. 2007. Gene expression signature in peripheral

blood cells from medical students exposed to chronic psychologi-

cal stress. Biol Psychol 76:147–155.

Keun HC, Sidhu J, Pchejetski D, Lewis JS, Marconell H, Patterson M,

Bloom SR, Amber V, Coombes RC, Stebbing J. 2009. Serum

Molecular Signatures of Weight Change during Early Breast Can-

cer Chemotherapy. Clin Cancer Res 15:6716–6723.

Kile ML, Baccarelli A, Hoffman E, Tarantini L, Quamruzzaman Q, Rah-

man M, Mahiuddin G, Mostofa G, Hsueh YM, Wright RO,

Christiani DC. 2012. Prenatal arsenic exposure and DNA methyl-

ation in maternal and umbilical cord blood leukocytes. Environ

Health Perspect 120:1061–1066.

Kim JY, Park JY, Kim OY, Ham BM, Kim H-J, Kwon DY, Jang Y,

Lee JH. 2010. Metabolic profiling of plasma in overweight/obese

and lean men using ultra performance liquid chromatography and

Q-TOF mass spectrometry (UPLC2Q-TOF MS). J Proteome Res

9:4368–4375.

Kuo C-H, Wang K-C, Tian T-F, Tsai M-H, Chiung Y-M, Hsiech C-M,

Tsai S-J, Wang S-Y, Tsai D-M, Huang C-C, Tseng YJ. 2012.

Metabolomic Characterization of Laborers Exposed to Welding

Fumes. Chem Res Toxicol 25:676–686.

Lambert M-P, Paliwal A, Vaissi€ere T, Chemin I, Zoulim F, Tommasino

M, Hainaut P, Sylla B, Scoazec J-Y, Tost J Jr, Herceg Z. 2011.

Aberrant DNA methylation distinguishes hepatocellular carci-

noma associated with HBV and HCV infection and alcohol

intake. J Hepatol 54:705–715.

Lambrou A, Baccarelli A, Wright RO, Weisskopf M, Bollati V, Amara-

siriwardena C, Vokonas P, Schwartza J. 2012. Arsenic exposure

and DNA methylation among elderly men. Epidemiology

23:668–676.

Lampe JW, Stepaniants SB, Mao M, Radich JP, Dai H, Linsley PS,

Friend SH, Potter JD. 2004. Signatures of Environmental Expo-

sures Using Peripheral Leukocyte Gene Expression: Tobacco

Smoke. Cancer Epidemiol Biomarkers Prev 13:445–453.

Lee D-H, Steffes MW, Sjoedin A, Jones RS, Needham LL, Jacobs DR

Jr. 2011. Low dose organochlorine pesticides and polychlorinated

biphenyls predict obesity, dyslipidemia, and insulin resistance

among people free of diabetes. PLoS One 6:e15977.

Lehmann R, Zhao X, Weigert C, Simon P, Fehrenbach E, Fritsche J,

Machann J, Schick F, Wang J, Hoene M, Schleicher ED, Haring

H-U, Xu G, Niess AM. 2010. Medium chain acylcarnitines

dominate the metabolite pattern in humans under moderate inten-

sity exercise and support lipid oxidation. PLoS One 5:e11519.

Llorach R, Urpi-Sarda M, Jauregui O, Monagas M, Andres-Lacueva C.

2009. An LC-MS-based metabolomics approach for exploring

urinary metabolome modifications after cocoa consumption.

J Proteome Res 8:5060–5068.

Lloyd AJ, Fav�e G, Beckmann M, Lin W, Tailliart K, Xie L, Mathers

JC, Draper J. 2011. Use of mass spectrometry fingerprinting to

identify urinary metabolites after consumption of specific foods.

Am J Clin Nutr 94:981–991.

Martin J, Bruno VM, Fang ZD, Meng XD, Blow M, Zhang T, Sherlock

G, Snyder M, Wang Z. 2010. Rnnotator: An automated de novo

transcriptome assembly pipeline from stranded RNA-Seq reads.

BMC Genomics 11:663; doi:10.1186/1471-2164-11-663.

Maxeiner A, Adkins CB, Zhang YF, Taupitz M, Halpern EF, McDougal

WS, Wu CL, Cheng LL. 2010. Retrospective analysis of prostate

cancer recurrence potential with tissue metabolomic profiles.

Prostate 70:710–717.

McHale CM, Zhang L, Hubbard AE, Smith MT. 2010. Toxicogenomic

profiling of chemically exposed humans in risk assessment. Mutat

Res 705:172–183.

McHale CM, Zhang L, Hubbard AE, Zhao X, Baccarelli A, Pesatori

AC, Smith MT, Landi MT. 2007. Microarray analysis of gene

expression in peripheral blood mononuclear cells from dioxin-

exposed human subjects. Toxicology 229:101–113.

McHale CM, Zhang L, Lan Q, Li G, Hubbard AE, Forrest MS, Vermeu-

len R, Chen J, Shen M, Rappaport SM, Yin S, Smith MT, Roth-

man N. 2009. Changes in the peripheral blood transcriptome

associated with occupational benzene exposure identified by

cross-comparison on two microarray platforms. Genomics

93:343–349.

McHale CM, Zhang L, Lan Q, Vermeulen R, Li G, Hubbard AE, Porter

KE, Thomas R, Portier CJ, Shen M, Rappaport SM, Yin S, Smith

MT, Rothman N. 2011. Global gene expression profiling of a

population exposed to a range of benzene levels. Environ Health

Perspect 119:628–634.

Milosavljevic A. 2011. Emerging patterns of epigenomic variation.

Trends Genet 27:242–250.

Monick MM, Beach SR, Plume J, Sears R, Gerrard M, Brody GH, Phili-

bert RA. 2012. Coordinated changes in AHRR methylation in

lymphoblasts and pulmonary macrophages from smokers. Am J

Med Genet Part B Neuropsychiatr Genet 159B:141–151.

Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF,

Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D,

Ilkayeva O, Wenner BR, Yancy WS, Eisenson H, Musante G,

Surwit RS, Millington DS, Butler MD, Svetkey LP. 2009. A

branched-chain amino acid-related metabolic signature that differ-

entiates obese and lean humans and contributes to insulin resist-

ance. Cell Metabol 9:311–326.

Nicholson G, Rantalainen M, Maher AD, Li JV, Malmodin D, Ahmadi

KR, Faber JH, Hallgrimsdottir IB, Barrett A, Toft H, Krestyani-

nova M, Viksna J, Neogi SG, Dumas M-E, Sarkans U, Silverman

BW, Donnelly P, Nicholson JK, Allen M, Zondervan KT, Lindon

JC, Spector TD, McCarthy MI, Holmes E, Baunsgaard D,

Holmes CC, Mol PC. 2011. Human metabolic profiles are stably

controlled by genetic and environmental variation. Mol Syst Biol

7:525.

Nogueira da Costa A, Herceg Z. 2012. Detection of cancer-specific epi-

genomic changes in biofluids: Powerful tools in biomarker dis-

covery and application. Mol Oncol 6:704–715.

Oberbach A, Blueher M, Wirth H, Till H, Kovacs P, Kullnick Y,

Schlichting N, Tomm JM, Rolle-Kampczyk U, Murugaiyan J,

Binder H, Dietrich A, von Bergen M. 2011. Combined proteomic

and metabolomic profiling of serum reveals association of the


496 Wild et al.

complement system with obesity and identifies novel markers of

body fat mass changes. J Proteome Res 10:4769–4788.

Ong CK, Subimerb C, Pairojkul C, Wongkham S, Cutcutache I, Yu W,

McPherson JR, Allen GE, Ng CC, Wong BH, Myint SS, Rajase-

garan V, Heng HL, Gan A, Zang ZJ, Wu Y, Wu J, Lee MH,

Huang D, Ong P, Chan-on W, Cao Y, Qian CN, Lim KH, Ooi

A, Dykema K, Furge K, Kukongviriyapan V, Sripa B, Wongk-

ham C, Yongvanit P, Futreal PA, Bhudhisawasdi V, Rozen S,

Tan P, Teh BT. 2012. Exome sequencing of liver fluke-associated

cholangiocarcinoma. Nat Genet 44:690–693.

O’Sullivan A, Gibney MJ, Brennan L. 2011. Dietary intake patterns are

reflected in metabolomic profiles: Potential role in dietary assess-

ment studies. Am J Clin Nutr 93:314–321.

Pagmantidis V, Meplan C, van Schothorst EM, Keijer J, Hesketh JE.

2008. Supplementation of healthy volunteers with nutritionally

relevant amounts of selenium increases the expression of lympho-

cyte protein biosynthesis genes. Am J Clin Nutr 87:181–189.

Patel CJ, Bhattacharya J, Butte AJ. 2010. An environment-wide associa-

tion study (EWAS) on type 2 diabetes mellitus. Plos One 5:

e10746.

Pechlivanis A, Kostidis S, Saraslanidis P, Petridou A, Tsalis G, Mougios

V, Gika HG, Mikros E, Theodoridis GA. 2010. (1)H NMR-based

metabonomic investigation of the effect of two different exercise

sessions on the metabolic fingerprint of human urine. J Proteome

Res 9:6405–6416.

Perera FP, Poirier MC, Yuspa SH, Nakayama J, Jaretzki A, Curnen

MM, Knowles DM, Weinstein IB. 1982. A pilot project in mo-

lecular cancer epidemiology: Determination of benzo[a]pyrene–

DNA adducts in animal and human tissues by immunoassays.

Carcinogenesis 3:1405–1410.

Pere-Trepat E, Ross AB, Martin F-P, Rezzi S, Kochhar S, Hasselbalch

AL, Kyvik KO, Sorensen TIA. 2010. Chemometric strategies to

assess metabonomic imprinting of food habits in epidemiological

studies. Chemom Intell Lab Syst 104:95–100.

Peretz A, Peck EC, Bammler TK, Beyer RP, Sullivan JH, Trenga CA,

Srinouanprachnah S, Farin FM, Kaufman JD. 2007. Diesel

exhaust inhalation and assessment of peripheral blood mononu-

clear cell gene transcription effects: An exploratory study of

healthy human volunteers. Inhal Toxicol 19:1107–1119.

Peto J. 2001. Cancer epidemiology in the last century and the next dec-

ade. Nature 411:390–395.

Pfeifer GP, Besaratinia A. 2009. Mutational spectra of human cancer.

Hum Genet 125:493–506.

Pfeifer GP, Hainaut P. 2011. Next-generation sequencing: Emerging les-

sons on the origins of human cancer. Curr Opin Oncol 23:62–68.

Pietilainen KH, Sysi-Aho M, Rissanen A, Seppanen-Laakso T, Yki-Jar-

vinen H, Kaprio J, Oresic M. 2007. Acquired obesity is associ-

ated with changes in the serum lipidomic profile independent of

genetic effects—A monozygotic twin study. PLoS ONE 2:e218.

Pleasance ED, Cheetham RK, Stephens PJ, McBride DJ, Humphray SJ,

Greenman CD, Varela I, Lin ML, Ordonez GR, Bignell GR, Ye

K, Alipaz J, Bauer MJ, Beare D, Butler A, Carter RJ, Chen L,

Cox AJ, Edkins S, Kokko-Gonzales PI, Gormley NA, Grocock

RJ, Haudenschild CD, Hims MM, James T, Jia M, Kingsbury Z,

Leroy C, Marshall J, Menzies A, Mudie LJ, Ning Z, Royce T,

Schulz-Trieglaff OB, Spiridou A, Stebbings LA, Szajkowski L,

Teague J, Williamson D, Chin L, Ross MT, Campbell PJ, Bent-

ley DR, Futreal PA, Stratton MR. 2010. A comprehensive cata-

logue of somatic mutations from a human cancer genome. Nature

463:191–196.

Pleasance ED, Stephens PJ, O’Meara S, McBride DJ, Meynert A, Jones

D, Lin ML, Beare D, Lau KW, Greenman C, Varela I, Nik-Zai-

nal S, Davies HR, Ordonez GR, Mudie LJ, Latimer C, Edkins S,

Stebbings L, Chen L, Jia M, Leroy C, Marshall J, Menzies A,

Butler A, Teague JW, Mangion J, Sun YA, McLaughlin SF,

Peckham HE, Tsung EF, Costa GL, Lee CC, Minna JD, Gazdar

A, Birney E, Rhodes MD, McKernan KJ, Stratton MR, Futreal

PA, Campbell PJ. 2010. A small-cell lung cancer genome with

complex signatures of tobacco exposure. Nature 463:184–190.

Pleil JD, Stiegel MA, Sobus JR. 2011. Breath biomarkers in environ-

mental health science: Exploring patterns in the human expo-

some. J Breath Res 5:046005.

Poirier MC. 2004. Chemical-induced DNA damage and human cancer

risk. Nat Rev Cancer 4:630–637.

Rasmussen LG, Winning H, Savorani F, Ritz C, Engelsen SB, Astrup A,

Larsen TM, Dragsted LO. 2012. Assessment of dietary exposure

related to dietary GI and fibre intake in a nutritional metabolomic

study of human urine. Genes Nutr 7:281–293.

Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E,

Yang E, Farrell L, Fox CS, O’Donnell CJ, Carr SA, Vasan RS,

Florez JC, Clish CB, Wang TJ, Gerszten RE. 2011. Lipid profil-

ing identifies a triacylglycerol signature of insulin resistance and

improves diabetes prediction in humans. J Clin Invest 121:1402–

1411.

Rudkowska I, Ponton A, Jacques H, Lavigne C, Holub BJ, Marette A,

Vohl MC. 2011. Effects of a supplementation of n-3 polyunsatu-

rated fatty acids with or without fish gelatin on gene expression

in peripheral blood mononuclear cells in obese, insulin-resistant

subjects. J Nutr Nutrigenom 4:192–202.

Saadatian-Elahi M, Slimani N, Chajes V, Jenab M, Goudable J, Biessy

C, Ferrari P, Byrnes G, Autier P, Peeters PHM, Ocke M, de Mes-

quita BB, Johansson I, Hallmans G, Manjer J, Wirfalt E, Gonza-

lez CA, Navarro C, Martinez C, Amiano P, Suaerz LR, Ardanaz

E, Tjonneland A, Halkjaer J, Overvad K, Jakobsen MU, Berrino

F, Pala V, Palli D, Tumino R, Vineis P, de Magistris MS,

Spencer EA, Crowe FL, Bingham S, Khaw KT, Linseisen J,

Rohrmann S, Boeing H, Noethlings U, Olsen KS, Skeie G, Lund

E, Trichopoulou A, Oustoglou E, Clavel-Chapelon F, Riboli E.

2009. Plasma phospholipid fatty acid profiles and their associa-

tion with food intakes: Results from a cross-sectional study

within the European Prospective Investigation into Cancer and

Nutrition. Am J Clin Nutr 89:331–346.

Schembri F, Sridhar S, Perdomo C, Gustafson AM, Zhang X, Ergun A,

Lu J, Liu G, Bowers J, Vaziri C, Ott K, Sensinger K, Collins JJ,

Brody JS, Getts R, Lenburg ME, Spira A. 2009. MicroRNAs as

modulators of smoking-induced gene expression changes in

human airway epithelium. Proc Natl Acad Sci USA 106:2319–

2324.

Scoccianti C, Ricceri F, Ferrari P, Cuenin C, Sacerdote C, Polidoro S,

Jenab M, Hainaut P, Vineis P, Herceg Z. 2011. Methylation pat-

terns in sentinel genes in peripheral blood cells of heavy smok-

ers: Influence of cruciferous vegetables in an intervention study.

Epigenetics 6:1114–1119.

Shah SH, Sun J-L, Stevens RD, Bain JR, Muehlbauer MJ, Pieper KS,

Haynes C, Hauser ER, Kraus WE, Granger CB, Newgard CB,

Califf RM, Newby LK. 2012. Baseline metabolomic profiles pre-

dict cardiovascular events in patients at risk for coronary artery

disease. Am Heart J 163:844–850.e841.

Skinner MK, Manikkam M, Guerrero-Bosagna C. 2011. Epigenetic

transgenerational actions of endocrine disruptors. Reprod Toxicol

31:337–343.

Smeester L, Rager JE, Bailey KA, Guan X, Smith N, Garcia-Vargas G,

Del Razo LM, Drobna Z, Kelkar H, Styblo M, Fry RC. 2011.

Epigenetic changes in individuals with arsenicosis. Chem Res

Toxicol 24:165–167.

Spira A, Beane J, Shah V, Liu G, Schembri F, Yang X, Palma J, Brody

JS. 2004. Effects of cigarette smoke on the human airway epithe-

lial cell transcriptome. Proc Natl Acad Sci USA 101:10143–

10148.



Steiling K, Kadar AY, Bergerat A, Flanigon J, Sridhar S, Shah V,

Ahmad QR, Brody JS, Lenburg ME, Steffen M, Spira A. 2009.

Comparison of proteomic and transcriptomic profiles in the bron-

chial airway epithelium of current and never smokers. PloS One

4:e5043.

Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC, Powell J,

van der Ouderaa F, Bingham S, Cross AJ, Nicholson JK. 2006.

Susceptibility of human metabolic phenotypes to dietary modula-

tion. J Proteome Res 5:2780–2788.

Stidley CA, Picchi MA, Leng S, Willink R, Crowell RE, Flores KG,

Kang H, Byers T, Gilliland FD, Belinsky SA. 2010. Multivita-

mins, folate, and green vegetables protect against gene promoter

methylation in the aerodigestive tract of smokers. Cancer Res

70:568–574.

Stransky N, Egloff AM, Tward AD, Kostic AD, Cibulskis K, Siva-

chenko A, Kryukov GV, Lawrence MS, Sougnez C, McKenna A,

Shefler E, Ramos AH, Stojanov P, Carter SL, Voet D, Cortes

ML, Auclair D, Berger MF, Saksena G, Guiducci C, Onofrio RC,

Parkin M, Romkes M, Weissfeld JL, Seethala RR, Wang L, Ran-

gel-Escareno C, Fernandez-Lopez JC, Hidalgo-Miranda A,

Melendez-Zajgla J, Winckler W, Ardlie K, Gabriel SB, Meyerson

M, Lander ES, Getz G, Golub TR, Garraway LA, Grandis JR.

2011. The mutational landscape of head and neck squamous cell

carcinoma. Science 333:1157–1160.

Suter M, Ma J, Harris A, Patterson L, Brown KA, Shope C, Showalter

L, Abramovici A, Aagaard-Tillery KM. 2011. Maternal tobacco

use modestly alters correlated epigenome-wide placental DNA

methylation and gene expression. Epigenetics 6:1284–1294.

Tarantini L, Bonzini M, Apostoli P, Pegoraro V, Bollati V, Marinelli B,

Cantone L, Rizzo G, Hou L, Schwartz J, Bertazzi PA, Baccarelli

A. 2009. Effects of particulate matter on genomic DNA methyla-

tion content and iNOS promoter methylation. Environ Health

Perspect 117:217–222.

Tilley AE, O’Connor TP, Hackett NR, Strulovici-Barel Y, Salit J, Amor-

oso N, Zhou XK, Raman T, Omberg L, Clark A, Mezey J, Crys-

tal RG. 2011. Biologic phenotyping of the human small airway

epithelial response to cigarette smoking. PloS One 6:e22798.

Totoki Y, Tatsuno K, Yamamoto S, Arai Y, Hosoda F, Ishikawa S,

Tsutsumi S, Sonoda K, Totsuka H, Shirakihara T, Sakamoto H,

Wang L, Ojima H, Shimada K, Kosuge T, Okusaka T, Kato K,

Kusuda J, Yoshida T, Aburatani H, Shibata T. 2011. High-resolu-

tion characterization of a hepatocellular carcinoma genome. Na-

ture Genetics 43:464–469.

Umbenhauer D, Wild CP, Montesano R, Saffhill R, Boyle JM, Huh N,

Kirstein U, Thomale J, Rajewsky MF, Lu SH. 1985. O(6)-meth-

yldeoxyguanosine in oesophageal DNA among individuals at

high risk of oesophageal cancer. Int J Cancer 36:661–665.

Vaissiere T, Hung RJ, Zaridze D, Moukeria A, Cuenin C, Fasolo V,

Ferro G, Paliwal A, Hainaut P, Brennan P, Tost J, Boffetta P,

Herceg Z. 2009. Quantitative analysis of DNA methylation pro-

files in lung cancer identifies aberrant DNA methylation of spe-

cific genes and its association with gender and cancer risk

factors. Cancer Res 69:243–252.

van Dijk SJ, Feskens EJ, Bos MB, de Groot LC, de Vries JH, Muller M,

Afman LA. 2012. Consumption of a high monounsaturated fat

diet reduces oxidative phosphorylation gene expression in periph-

eral blood mononuclear cells of abdominally overweight men and

women. J Nutr 142:1219–1225.

Van Dorsten FA, Daykin CA, Mulder TPJ, Van Duynhoven JPM. 2006.

Metabonomics approach to determine metabolic differences

between green tea and black tea consumption. J Agric Food

Chem 54:6929–6938.

van Leeuwen DM, van Agen E, Gottschalk RW, Vlietinck R, Gielen M,

van Herwijnen MH, Maas LM, Kleinjans JC, van Delft JH. 2007.

Cigarette smoke-induced differential gene expression in blood

cells from monozygotic twin pairs. Carcinogenesis 28:691–697.

Vedin I, Cederholm T, Freund-Levi Y, Basun H, Garlind A, Irving GF,

Eriksdotter-Jonhagen M, Wahlund LO, Dahlman I, Palmblad J.

2012. Effects of DHA-rich n-3 fatty acid supplementation on

gene expression in blood mononuclear leukocytes: The OmegAD

study. PloS One 7:e35425.

von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vanden-

broucke JP, Initiative S. 2007. The strengthening the reporting of

observational studies in epidemiology (STROBE) statement: Guide-

lines for reporting observational studies. PLoS Med 4:1623–1627.

Wan ES, Qiu W, Baccarelli A, Carey VJ, Bacherman H, Rennard SI,

Agusti A, Anderson W, Lomas DA, Demeo DL. 2012. Cigarette

smoking behaviors and time since quitting are associated with

differential DNA methylation across the human genome. Human

Mol Genet 21:3073–3082.

Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis

GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA,

Mootha VK, Florez JC, Souza A, Melander O, Clish CB,

Gerszten RE. 2011. Metabolite profiles and the risk of developing

diabetes. Nat Med 17:448–453.

Wang Z, Neuburg D, Li C, Su L, Kim JY, Chen JC, Christiani DC. 2005.

Global gene expression profiling in whole-blood samples from indi-

viduals exposed to metal fumes. Environ Health Perspect 113:233–

241.

Wang-Sattler R, Yu Y, Mittelstrass K, Lattka E, Altmaier E, Gieger C,

Ladwig KH, Dahmen N, Weinberger KM, Hao P, Liu L, Li Y,

Wichmann HE, Adamski J, Suhre K, Illig T. 2008. Metabolic profil-

ing reveals distinct variations linked to nicotine consumption in

humans—First results from the KORA study. PLoS One 3:e3863.

Wild CP. 2005. Complementing the genome with an exposome: The outstanding

challenge of environmental exposure measurement in molecular epidemi-

ology. Cancer Epidemiol Biomarkers Prevent 14:1847–1850.

Wild CP. 2009. Environmental exposure measurement in cancer epide-

miology. Mutagenesis 24:117–125.

Wild CP. 2012a. The exposome: From concept to utility. Int J Epidemiol

41:24–32.

Wild CP. 2012b. The role of cancer research in noncommunicable dis-

ease control. J Natl Cancer Institute 104:1051–1058.

Wild C, Vineis P, Garte S, editors. 2008. Molecular epidemiology of

chronic diseases. Chichester (UK): Wiley.

Wishart D, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y,

Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J,

Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu

V, Greiner R, Scalbert A. 2013. HMDB 3.0—The human metabolome

database in 2013. Nucleic Acids Res 41(Database issue):D801–D807.

Wright WR, Parzych K, Crawford D, Mein C, Mitchell JA, Paul-Clark

MJ. 2012. Inflammatory transcriptome profiling of human mono-

cytes exposed acutely to cigarette smoke. PloS One 7:e30120.

Wurtz P, Soininen P, Kangas AJ, Ronnemaa T, Lehtimaki T, Kahonen

M, Viikari JS, Raitakari OT, Ala-Korpela M. 2013. Branched-

chain and aromatic amino acids are predictors of insulin resist-

ance in young adults. Diabetes Care 36:648–655.

Wurtz P, Makinen V-P, Soininen P, Kangas AJ, Tukiainen T, Kettunen

J, Savolainen MJ, Tammelin T, Viikari JS, Ronnemaa T, Kaho-

nen M, Lehtimaki T, Ripatti S, Raitakari OT, Jarvelin M-R, Ala-

Korpela M. 2012. Metabolic Signatures of Insulin Resistance in

7,098 Young Adults. Diabetes 61:1372–1380.

Xia J, Psychogios N, Young N, Wishart DS. 2009. MetaboAnalyst: A

web server for metabolomic data analysis and interpretation.

Nucl. Acids Res 37(Web Server issue):W652–W660.

Xu J, Yang S, Cai S, Dong J, Li X, Chen Z. 2010. Identification of bio-

chemical changes in lactovegetarian urine using 1H NMR spec-

troscopy and pattern recognition. Anal Bioanal Chem 396:1451–

1463.


498 Wild et al.

Zhang L, Lan Q, Ji Z, Li G, Shen M, Vermeulen R, Guo W, Hubbard

AE, McHale CM, Rappaport SM, Hayes RB, Linet MS, Yin S,

Smith MT, Rothman N. 2012. Leukemia-related chromosomal

loss detected in hematopoietic progenitor cells of benzene-

exposed workers. Leukemia 26:2494–2498.

Zhang L, Lee JJ, Tang H, Fan YH, Xiao L, Ren H, Kurie J, Morice RC,

Hong WK, Mao L. 2008. Impact of smoking cessation on global

gene expression in the bronchial epithelium of chronic smokers.

Cancer Prev Res 1:112–118.

Zieker D, Fehrenbach E, Dietzsch J, Fliegner J, Waidmann M, Nieselt

K, Gebicke-Haerter P, Spanagel R, Simon P, Niess AM, Northoff

H. 2005. cDNA microarray analysis reveals novel candidate

genes expressed in human peripheral blood following exhaustive

exercise. Physiol Genomics 23:287–294.



Measuring the exposome: A powerful basis for evaluating environmental exposures and cancer risk

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