Paolo VineisImperial College

Towards the Exposome

Manchester 10 September 2013

Environmental PAFs for cancer (Global burden of disease, WHO)

2

SM RappaportData from Ezzati et al., “Comparative Quantification of Mortality and Burden of Disease Attributable to Selected Risk Factors,” Global Burden of Disease and Risk Factors, Chapter 4, WHO, 2006.

A self‐fulfilling prophecy: are we underestimating the role of the environment in gene‐environment interaction research?

( P Vineis Int J Epidemiol 2004) 

According to estimates, the common genotyping method Taqman has 96% sensitivity and 98% specificity, thus allowing little error in classification. On the contrary, sensitivity in environmental exposure assessment is quite often lower than 70% and specificity even lower.

Genotype is stable, measured accurately (sens, spec=90-100%), frequency of alleles is high

Environmental exposures are changing (life-course events), often measured inaccurately, frequency may be too low

Some environmental exposures can be studied by epidemiology with confidence , i.e. measurement error is relatively low and has little impact on estimates (e.g. smoking). Advancement in exposure 

assessment due e.g. to GIS techniques for air pollution.

When measurement error is too high we need biomarkers (e.g. number of sexual partners, OR for cervical cancer around 2; HPV strains, OR 

around 100‐500).

Discoveries that support the original model of molecular epidemiology

Marker linked to exposure or disease ExposureInternal doseUrinary metabolites (NNK, NNN) Nitrosocompounds in tobaccoBiologically effective doseDNA adducts PAHs , aromatic compoundsAlbumin adducts AFB 1Hemoglobin adducts Acrylamide, Styrene,

1,3-Butadiene

Preclinical effect Exposure and/or cancerChromosome aberrations Lung, Leukemia,

Benzene HPRT PAHs, 1,3-ButadieneGlycophorin A PAHsGene expression Cisplatin

Genetic susceptibilityPhenotypic markers DNA repair capacity in head

and neck cancerSNPsNAT2, GSTM BladderCYP1A1 Lung

Vineis and Perera, 2007

Exposome - definition

The exposome concept refers to the totality of environmental exposures from conception onwards, The internal exposome is based on measurements in biological material of complete sets of biomarkers of exposure, using repeated biological samples especially during critical life stages.

Biomarkers which can be measured in this context cover a wide range of molecules, ranging from xenobiotics and their metabolites in blood (metabolomics) to covalent complexes with DNA and proteins (adductomics).

The term omics generally refers to the rigorous study of a complete set of biological and non-biological molecules with high-throughput techniques (Rappaport and Smith 2010).

7SM Rappaport

Prospective cohorts provide an ideal context in which to bring together the best laboratory science, epidemiology, biostatistics 

and bioinformatics to investigate cancer risk factors. 

To realize this potential, however, requires a commitment to develop and adapt laboratory tools for application to the bio‐

specimens being collected. 

In addition, emphasis is needed on the collection and processing of biological specimens in a manner, as far as is predictable, 

consistent with the future laboratory analyses and avoid biases at the time of sampling.

Challenges:

1. precious and limited biobanked material, not easily released by PIs2. single (spot) biological samples3. usually blood, not urine (which may be better e.g. for metabolomics)4. no cohorts allow life‐course epidemiology5. in‐depth exposure assessment is limited by feasibility (for cancer you need large sample sizes)6. lab measurements and omics have the same limitations related to sample size and feasibility7. biostatistical approaches and  causal interpretation8. ethical issues

The “meet‐in‐the‐middle” approach

Schematic representation of the implementation of the ‘meet-in-the middle’ approach (Chadeau-Hyam et al, Biomarkers 2011).

Main finding in pilot study from EPIC-Italy on coloncancer - Role of gut microbiota? Concept of “guthealth”

No markers found in association with breast cancer, 8 signalsfound in association with colon cancer (Chadeau-Hyam et al,

2011)

Dietary fibers intake was found to be associated to four putativemarkers out of 235 (with corresponding p-values ranging from

0.003 to 0.02).

One marker indicates a possible link with gut microbialfermentation of plant phenolics in the colon (Nicholson et al.,2005, Phipps et al., 1998, Aura, 2007), a process also plausiblylinked to higher dietary fibers exposure and lower colon cancerrisk.

Second study nested in EPIC on colon cancer

•194 cases, 194 controls (age and sex‐matched) from EPIC‐Italy

•134 non‐polar metabolites measured using LC‐MS (Imperial Lab) – e.g. lipids

•Investigation of metabolite levels in relation to colorectal cancer risk

•Association of metabolites with colorectal cancer risk factors (in particular obesity and dietary factors)

Metabolomics and Colon Cancer Risk: Multivariate Model

Metabolite Class/Pathway OR1 (95% CI)Decanoylcholine Fatty Acid 2.44 (0.009)PC(18:4(6Z,9Z,12Z,15Z)/P‐16:0

Phospholipid 0.63 (0.047)

PC(22:4(7Z,10Z,13Z,16Z)/22:5(7Z,10Z,13Z,16Z,19Z))

Phospholipid 0.55 (0.016)

PE(20:5(5Z,8Z,11Z,14Z,17Z)/P‐16:0)

Phospholipid 0.62 (0.039)

10,11‐dihydro‐leukotriene B4

Arachidonic Acid 2.76 (0.02)

Tetracosanoic Acid Arachidonic Acid 0.43 (0.04)1OR for 1-log unit change in metabolite level

Choline Metabolism

Nature 2008

Supervised analysis defined profiles robustly associated with known dietary factors

AHRR

1x10‐5

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Specificity

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MI - TestMI - Validation

Exposure marker - AHRR methylation is strongly associated with former smoking (first marker of past smoking). (Shenker et al, Human Molecular Genetics 2013; Epidemiology, in press)

Never Former Current

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Cotinine vs Smoking Status

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Basic components of the EXPOsOMICS project 

1. Select and integrate subjects, samples and data from 3 types of existing studies that collectively reflect all life stages from conception to old age (WP2):Experimental Short‐Term Studies  (STS) including Oxford Street studyMother‐Child Cohorts  (MCO)Adult Long‐Term Studies  (ALTS)

2. Measure the external exposome component for air and water contaminants by performing extensive, repeated Personal Exposure Monitoring (PEM) (WP3‐4). 

3. Measure the internal exposome in fresh and archived samples (WP5‐7). Fresh blood samples will be collected from the individuals undergoing PEM, i.e. individuals in STS and those from a representative subsamples from MCO and ALTS. 

A conceptual model of life-course disease risk

Population studies of chronic diseases have traditionally recruited middle-aged subjects. However, there is strong evidence that (a) the risk of disease isinfluenced by early exposures, including in utero; (b) life-stages include criticalperiods (during which changes in exposure have long-term effects on diseaserisks or related, intermediate markers) and sensitive periods (during which anexposure has stronger effect on development and, hence, disease risk than atother times).

The idea of a sequence of critical and sensitive periods leads to the concept of"chain of risk", i.e. the interplay of early exposures and late exposures. To usethis concept in practice implies having access to multiple life-stages inexposure assessment and epidemiological studies, and repeated measurementsof biomarkers at different time windows. This approach requires an inter-generational epidemiological study design and novel statistical analyses.

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ALSPACEPIC‐ESCAPE

PICCOLI+

Critical stages of life

Mid‐ and late‐life

60

Age

Birth

PISCINA

INMA

RHEA

PISCINA RAPTES

OXFORD ST

MCC

SAPALDIA

EPICURO

EXPOsOMICs App‐GPS position‐Accelerometer (user activity)‐Altimeter‐Compass‐user I/O (questionnaire)‐Download of  logged data (above) via USB‐Application setup

SmartPhoneMicroAeth(BC)

UFP Sensor

USB hub

USB

USB

Sensor Pack

‐ “Dedicated” smart phone in a pouch on sensor pack to enable user input/output (i.e. we do not intend to “leverage” the user’s personal phone, at this stage). ‐Rechargable Li battery pack supplies power to instruments and smartphone via USB hub for 36 hr autonomy. ‐Each Sensor and Smartphone log data independently (synched in time during initial setup).

USB

RechargeableBattery Pack (use  5V o/p.)

PEM device

Oxford Street: ‘active’ exposure

Hyde Park: ‘control’ exposure

• gentle walking for two hours (6km)• Oxford Street and Hyde Park

– random order– separated by >3 weeks

• contemporary measurements of exposures

exposures; ‘cross‐over’

PM10‐2.5PM2.5‐0.1PM0.1black carbonultrafine particlesNO2/xtemperaturerelative humidity. 

Need for new biostatistical tools and causal interpretation

- repeat samples and intra-individual variation- validation of omics: Hebels et al, EHP- quality controls (e.g. nuisance parameters: Chadeau-Hyam et al, submitted)- “cross-omics”- longitudinal models of causality

Chadeau-Hyam M, et al. Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers.Environ Mol Mutagen. 2013 Aug;54(7):542-57.

Hebels et al. Performance in omics analyses of blood samples in long-term storage: opportunities for the exploitation of existing biobanks in environmental health research. Environ Health Perspect. 2013 Apr;121(4):480-7.

Vineis P, et al. Advancing the application of omics-based biomarkers in environmental epidemiology. Environ Mol Mutagen. 2013 Aug;54(7):461-7.

Longitudinal HMM for smoking‐induced lung cancer (Chadeau‐Hyam et al., Epidemiology in press)

• Exposome concept: risk of chronic diseases is not only driven by the exposure level itself, but also by its evolution in time and by potential temporal patterns in the exposure history.

Include a temporal component in causal inferences

• Definition of a longitudinal compartmental model (SIR-type)

S: healthy; I: growing and undiagnosed tumor; R: diagnosed;M: other cause mortality. S and I are hidden (SUI is observed)

S I RPS-I PI-R

m m M

The end