Paolo Vineis
Imperial College

Towards the Exposome
Manchester 10 September 2013
Environmental PAFs for cancer (Global 
burden of disease, WHO)

2
SM Rappaport

Data from Ezzati et al., “Comparative Quan...
A self‐fulfilling prophecy: are we underestimating the role 
of the environment in gene‐environment interaction 
research?...
Some environmental exposures can be studied by epidemiology with 
confidence , i.e. measurement error is relatively low an...
Discoveries that support the original model of molecular epidemiology
Marker linked to exposure or disease
Internal dose
U...
Exposome - definition
The exposome concept refers to the totality of environmental
exposures from conception onwards, The ...
SM Rappaport

7
Prospective cohorts provide an ideal context in which to bring 
together the best laboratory science, epidemiology, biosta...
Challenges:
1. precious and limited biobanked material, not easily released 
by PIs
2. single (spot) biological samples
3....
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 colon
cancer - Role of gut microbiota? Concept of “gut
health”
No markers f...
Second study nested in EPIC on colon 
cancer
•194 cases, 194 controls (age and sex‐matched) from EPIC‐Italy
•134 non‐polar...
Metabolomics and Colon Cancer Risk:
Multivariate Model
Metabolite
Decanoylcholine
PC(18:4(6Z,9Z,12Z,1
5Z)/P‐16:0
PC(22:4(7...
Choline Metabolism
Nature 2008
Supervised analysis
defined profiles robustly
associated with known
dietary factors
AHRR
1x10‐7
1x10‐5
1.0
0.0

MI - Validation

1.0

0.8

0.6

0.4

0.2

0.0

1500

2500
MI - Test

0 500

ng/mL

0.6
0.4
0.2

Sensitivity

0.8
...
Basic components of the EXPOsOMICS project 
1. Select and integrate subjects, samples and data from 3 types of existing 
s...
A conceptual model of life-course disease risk

Population studies of chronic diseases have traditionally recruited middle...
Critical stages of life

RAPTES

PISCINA

20

SAPALDIA

EPIC‐
ESCAPE

Age

30

ALSPAC

10

PISCINA

INMA

OXFORD ST

0

RH...
PEM device
Sensor Pack
MicroAeth
(BC)

UFP Sensor

SmartPhone

EXPOsOMICs App

USB

USB

USB

Rechargeable
Battery Pack 
(...
Oxford Street: ‘active’ exposure
Hyde Park: ‘control’ exposure
exposures; ‘cross‐over’

•
•

gentle walking for two hours (6km)
Oxford Street and Hyde Park
– random order
– separated by...
Need for new biostatistical tools and causal interpretation
- repeat samples and intra-individual variation
- validation o...
Longitudinal HMM for smoking‐induced lung cancer (Chadeau‐Hyam et 
al., Epidemiology in press)
• Exposome concept: risk of...
The end
K.3 Vineis
Upcoming SlideShare
Loading in …5
×

K.3 Vineis

184
-1

Published on

Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
184
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
12
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

K.3 Vineis

  1. 1. Paolo Vineis Imperial College Towards the Exposome Manchester 10 September 2013
  2. 2. Environmental PAFs for cancer (Global  burden of disease, WHO) 2 SM Rappaport Data 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.
  3. 3. 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
  4. 4. 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).
  5. 5. Discoveries that support the original model of molecular epidemiology Marker linked to exposure or disease Internal dose Urinary metabolites (NNK, NNN) Biologically effective dose DNA adducts Albumin adducts Hemoglobin adducts Preclinical effect Chromosome aberrations HPRT Glycophorin A Gene expression Genetic susceptibility Phenotypic markers SNPs NAT2, GSTM CYP1A1 Vineis and Perera, 2007 Exposure Nitrosocompounds in tobacco PAHs , aromatic compounds AFB 1 Acrylamide, Styrene, 1,3-Butadiene Exposure and/or cancer Lung, Leukemia, Benzene PAHs, 1,3-Butadiene PAHs Cisplatin DNA repair capacity in head and neck cancer Bladder Lung
  6. 6. 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).
  7. 7. SM Rappaport 7
  8. 8. 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.
  9. 9. Challenges: 1. precious and limited biobanked material, not easily released  by PIs 2. single (spot) biological samples 3. usually blood, not urine (which may be better e.g. for  metabolomics) 4. no cohorts allow life‐course epidemiology 5. 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 feasibility 7. biostatistical approaches and  causal interpretation 8. ethical issues
  10. 10. The “meet‐in‐the‐middle” approach
  11. 11. Schematic representation of the implementation of the ‘meet-in-the middle’ approach (Chadeau-Hyam et al, Biomarkers 2011).
  12. 12. Main finding in pilot study from EPIC-Italy on colon cancer - Role of gut microbiota? Concept of “gut health” No markers found in association with breast cancer, 8 signals found in association with colon cancer (Chadeau-Hyam et al, 2011) Dietary fibers intake was found to be associated to four putative markers out of 235 (with corresponding p-values ranging from 0.003 to 0.02). One marker indicates a possible link with gut microbial fermentation of plant phenolics in the colon (Nicholson et al., 2005, Phipps et al., 1998, Aura, 2007), a process also plausibly linked to higher dietary fibers exposure and lower colon cancer risk.
  13. 13. 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)
  14. 14. Metabolomics and Colon Cancer Risk: Multivariate Model Metabolite Decanoylcholine PC(18:4(6Z,9Z,12Z,1 5Z)/P‐16:0 PC(22:4(7Z,10Z,13Z, 16Z)/22:5(7Z,10Z,13 Z,16Z,19Z)) PE(20:5(5Z,8Z,11Z,1 4Z,17Z)/P‐16:0) 10,11‐dihydro‐ leukotriene B4 Tetracosanoic Acid 1OR Class/Pathway Fatty Acid Phospholipid OR1 (95% CI) 2.44 (0.009) 0.63 (0.047) Phospholipid 0.55 (0.016) Phospholipid 0.62 (0.039) Arachidonic Acid 2.76 (0.02) Arachidonic Acid 0.43 (0.04) for 1-log unit change in metabolite level
  15. 15. Choline Metabolism
  16. 16. Nature 2008
  17. 17. Supervised analysis defined profiles robustly associated with known dietary factors
  18. 18. AHRR 1x10‐7 1x10‐5
  19. 19. 1.0 0.0 MI - Validation 1.0 0.8 0.6 0.4 0.2 0.0 1500 2500 MI - Test 0 500 ng/mL 0.6 0.4 0.2 Sensitivity 0.8 Cotinine vs Smoking Status Never Former Current Specificity 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)
  20. 20. 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 study Mother‐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. 
  21. 21. A conceptual model of life-course disease risk Population studies of chronic diseases have traditionally recruited middleaged subjects. However, there is strong evidence that (a) the risk of disease is influenced by early exposures, including in utero; (b) life-stages include critical periods (during which changes in exposure have long-term effects on disease risks or related, intermediate markers) and sensitive periods (during which an exposure has stronger effect on development and, hence, disease risk than at other 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 use this concept in practice implies having access to multiple life-stages in exposure assessment and epidemiological studies, and repeated measurements of biomarkers at different time windows. This approach requires an intergenerational epidemiological study design and novel statistical analyses.
  22. 22. Critical stages of life RAPTES PISCINA 20 SAPALDIA EPIC‐ ESCAPE Age 30 ALSPAC 10 PISCINA INMA OXFORD ST 0 RHEA 50 PICCOLI+ Birth 60 MCC Mid‐ and late‐life EPICURO
  23. 23. PEM device Sensor Pack MicroAeth (BC) UFP Sensor SmartPhone EXPOsOMICs App USB USB USB Rechargeable Battery Pack  (use  5V o/p.) ‐GPS position ‐Accelerometer (user activity) ‐Altimeter ‐Compass ‐user I/O (questionnaire) ‐Download of  logged data (above) via USB ‐Application setup USB hub ‐ “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).
  24. 24. Oxford Street: ‘active’ exposure
  25. 25. Hyde Park: ‘control’ exposure
  26. 26. exposures; ‘cross‐over’ • • gentle walking for two hours (6km) Oxford Street and Hyde Park – random order – separated by >3 weeks • contemporary measurements of exposures PM10‐2.5 PM2.5‐0.1 PM0.1 black carbon ultrafine particles NO2/x temperature relative humidity. 
  27. 27. 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):4807. Vineis P, et al. Advancing the application of omics-based biomarkers in environmental epidemiology. Environ Mol Mutagen. 2013 Aug;54(7):461-7.
  28. 28. 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 m PS-I I m PI-R R M S: healthy; I: growing and undiagnosed tumor; R: diagnosed; M: other cause mortality. S and I are hidden (SUI is observed)
  29. 29. The end
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×