Paolo Vineis

Imperial College London

HuGeF Foundation, Torino

The role of the ”omics-techniques” in HBM, current and future applications

BERLIN 19 April 2016

HBM and epidemiology have different goals. Omics and the exposome have been so far considered in the

context of epidemiological research, i.e. search for causes of disease (causality context) rather than in a

public health/monitoring context

Gallo V, Egger M, McCormack V, Farmer PB, et al. (2011) STrengthening the Reporting of OBservational studies in Epidemiology –

Molecular Epidemiology (STROBE-ME): An Extension of the STROBE Statement. PLoS Med 8(10): e1001117.

doi:10.1371/journal.pmed.1001117

http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001117

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-throughpu

techniques (Rappaport and Smith 2010).

DATA-DRIVEN DISCOVERY (EWAS)

Molecular epidemiology

Systems biology

Genomics , epigenomics, transcriptomics & experiments

Identify sources & measure exposures

Exposure biology

KNOWLEDGE-DRIVEN APPLICATIONS

Causality and prevention

Drug development

Diagnosis, prognosis and treatment

Disease stage and response to therapy

Biomarkers of exposure

Candidate biomarkers

Diseased vs. healthy (case-control studies) Untargeted designs

Diseased vs. healthy (prospective cohorts) Targeted designs

Discriminating features

Chemical identification

Biomarkers of disease

Serum exposome

S. Rappaport, Biomarkers, 2012,

17(6), 48: 3-9

Advantages of Metabolomics • Minimally or non-invasive

• Can be performed rapidly in an automated fashion

• Suitable for use on a population-wide level

• Proven effectiveness in understanding exposure response, drug response

and disease process in humans

• Low in cost compared to other –omics technologies

BUT:

High potential for variation from experimental or environmental influence

High intra-individual variation

Large sample sizes needed to detect moderate effect sizes

Supervised analysis

defined profiles robustly

associated with known

dietary factors

Nature 2008

Metabolomic profiles as exposure biomarkers in Envirogenomarkers (FP7 funded)

• 10 POPs or heavy metals measured in blood in two prospective cohorts (n=1,800)

• Large number of metabolite features associated with exposure levels

- Higher for POPs than heavy metals

• Results robust for confounder adjustment and

correction for multiple testing

• Substantial crossover in top metabolites for exposures with similar properties

• Exposures highly correlated

• >80% signals replicated in both cohorts

0102030405060708090

100

Cad

miu

m (µ

g/L)

Lead (µ

g/L)

DD

E (pg/m

l)

PC

B1

38

(pg/m

l)

PC

B1

18

(pg/m

l)

PC

B1

53

(pg/m

l)

PC

B1

80

(pg/m

l)

PC

B1

70

(pg/m

l)

HC

B (p

g/ml)

PC

B1

56

(pg/m

l)

No

. me

tab

olit

e f

eat

ure

s

<0.05

<0.01

<0.001

Bonferroni

Cad

miu

m (

n=3

6)

Lead

(n

=36

)

DD

E (n

=65

)

PC

B1

38

(n=6

9)

PC

B1

18

(n=7

6)

PC

B1

53

(n=7

9)

PC

B1

80

(n=8

6)

PC

B1

70

(n=8

7)

HC

B (

n=9

2)

Lead (n=36) 5

DDE (n=65) 9 23

PCB138 (n=69) 8 27 54

PCB118 (n=76) 11 29 60 62

PCB153 (n=79) 10 28 57 69 66

PCB180 (n=86) 10 32 52 63 65 72

PCB170 (n=87) 10 32 53 64 64 73 84

HCB (n=92) 9 29 63 67 68 75 74 76

PCB156 (n=92) 11 33 56 64 68 74 83 84 79

No. significant features by exposure

Crossover between significant features (p<0.05)

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

20

30

0 50

ALSPAC EPIC-

ESCAPE

PICCOLI+

Critical stages of life and cohorts in Exposomics

Mid- and late-life

60

Age

Birth

PISCINA

INMA

RHEA

PISCINA RAPTES

OXFORD ST

MCC

SAPALDIA

EPICURO

PISCINA study

• Ongoing experimental study of swimmers in pools

• Exposure to disinfection by-products (DBPs) and their short-term effects

• Extensive identification of DBPs in swimming pool water and air as well as biological samples (exhaled breath, blood and urine) and identify:

- Mutagenicity

- Genotoxicity

- Short-term respiratory health

effects

Metabolomic analysis - PCA

Untargeted metabolomics of blood samples, using UHPLC-QTOF mass

spectrometer

N= 3,761 metabolomic compounds measured in 100% of the samples

The Oxford Street study: integration of exposure data (Paul Cullinan, Imperial College

London), metabolomics (Augustin Scalbert, Dinesh Kumar, IARC), albumin

adductomics (George Preston, King’s College London) and other omics

Serum albumin adducts as biomarkers of exposure

Rappaport, Williams et al., Toxicol. Lett., 2012, 213, 83-90

• Stephen Rappaport’s group (UC Berkeley) have been profiling adducts of human serum albumin.

Oxford Street 2: preliminary data (13 of 59 subjects)

72.1190.11108.11126.11144.11162.11180.11198.11

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Ad

de

d m

ass

/ D

a A

dd

uct

am

ou

nt

/ ar

bit

rary

un

its

Sample ID

Epigenetic Variation related to exposures (smoking)

N F C

DNA methylation in blood identifies “Former Smokers”

Molecular quantification of past exposure

= measured individual risk Shenker et al, Epidemiology 2013

Shenker et al, HMG 2013

Hypomethylation of the AHRR and F2RL3 genes is associated with lung cancer: genome-wide study in three prospective

cohorts (Fasanelli et al, Nature Comm. 2015)(also replicated in MCCS and EPIC-Heidelberg)

Hypomethylation of the AHRR and F2RL3 genes is associated with lung cancer: genome-wide study in two prospective

cohorts (Polidoro, Fasanelli, Ponzi)(also replicated in MCCS)

NOWAC

cases controls OR 95% CI p-value

AHRR cg05575921

Unadjusted 124 122 0.37

0.31-0.54 3.33x10-11

Adjusted 124 122 0.39 0.24-0.61 2.55x10-5

Never 11 54 0.60 0.07-5.19 0.04375

Former 41 33 0.23 0.10-0.56 0.001

Current 72 35 0.46 0.24-0.88 0.019

F2RL3 cg03636183

Unadjusted 124 122 0.46

0.31-0.56 3.86x10-10

Adjusted 124 122 0.51 0.35-0.73 4.19x10-4

Never 11 54 1.07 0.29-4.00 0.916

Former 41 33 0.25 0.35-0.55 0.001

Current 72 35 0.55 0.32-0.94 0.030

Rappaport, Vineis, Scalbert et al., Environ. Health Perspect., 2014, 122, 8, 769-774

From the literature we obtained human blood concentrations of 1,561 small

molecules and metals derived from foods, drugs, pollutants, and

endogenous processes. Blood concentrations spanned 11 orders of

magnitude and were indistinguishable for endogenous and food chemicals

and drugs, whereas those of pollutants were 1,000 times lower.

Method

dynamic

range

Thank you

19