Transcript How should we interpret BH criteria for hazard identification?

Paolo Vineis
Imperial College London and HuGeF Foundation
Exposomics:
a hybrid study design, and causal
interpretation
Centre Investigator’s seminar 26 November 2014
Conceptual framework:
- Life course
- Integration of experiment and observation
- Improved exposure assessment (reduction
of uncertainty)
- Systematic use of omics
- Meet in the middle
Critical stages of life
RAPTES
PISCINA
20
SAPALDI
A
EPICESCAPE
Age
30
ALSPAC
10
PISCINA
OXFORD
ST
INMA
0
RHEA
50
PICCOLI+
Birth
60
MCC
Mid- and late-life
EPICURO
The logical structure of EXPOsOMICS
• Randomized trials (Oxford St, TAPAS2) provide stronger
evidence on short-term effects (Paul Cullinan)
• Updated metabolomic database provides priors for Bayesian
analysis (Augustin Scalbert)
• Pathway analysis, dynamic models and DAGs to reinforce
causality (Marc Chadeau-Hyam)
• Integration of cross-sectional (PEM and fresh blood – John
Gulliver) with longitudinal approaches (archived samples and
LUR with back-extrapolation) to identify best omic candidates
(examples from Piscina and ALSPAC)(Michelle Plusquin,
Karin Van Veldhoven, Florence Guida)
4
Phase 1 – biological samples
Samples
(in brackets methylation)
Fresh
Archived
Oxford Street
TAPAS 2
Piscina
INMA (PEM)
EPIC-ESCAPE, East Anglia,
SAPALDIA (ALTS-PEM)
60x2 (-)
60x2 (-)
60x2 (-)
35x2 (70)
sent to labs
sent to labs
40 each x2
(320)
probably only
100 archived
Total Phase 1
750
100
Total to be analysed Phase 1
850
EPIGENETICS
(390)
(100)
Phase 2 Adults
200/200 asthma adults SAPALDIA and East Anglia
300/300 CVD EPIC
200/200 colon cancer MCC
Total 700/700 (all undergoing epigenetics)
Phase 2 Children
500 with continuous measurements
Total 1900 samples in Phase 2 (plus 10% quality controls)
Phase 2
Children cohorts (excluding ALSPAC)
Current plan for Phase 2 – based on continuous measurements
200 children with neurodevelopmental problems at age 4 from
Rhea/INMA
200 birth weight and growth curves from EnvironAge
100 birth weight and growth curves from Piccoli+
(200 birth weight and growth curves in Rhea/INMA to be decided)
Plus 10% quality control samples
In principle: cord blood (and/or blood at age 4)
In addition ALSPAC has 1,000 children with metabolomics, methylome
and air pollution exposure assessment (including 500 with asthma)
Exposure comparisons (UFP)
UFP MEASUREMENTS AT HOMES (UB)
Backpack
outdoors
Backpack indoors
AND OTHER LOCATIONS (work, leisure etc.)
UFP MEASUREMENTS AT HOMES (UT)
Backpack
outdoors
Backpack indoors
PEM
PEM
UFP LUR models
developed in EXPOsOMICS
Reference site
Oxford Street Study and EXPOsOMICS 5.5 ml Blood Protocol
Blood Processing Protocol for Exposome Studies
Peripheral Blood
(~5.5 ml)
EDTA Tube
(4 ml)
Serum Tube
(1.5 ml)
Allow to clot
0.8 ml
1.5 ml
1.5 ml
Centrifugation
Whole blood +
RNAlater
0.5 ml
0.5 ml
0.25 ml
0.125 ml
-80 °C
-80 °C
-80 °C
-80 °C
Adductomics (1)
Adductomics (2)
Metabolomics
Proteomics
Plasma
2 x 0.4 ml
6 x 0.5 ml
-80 °C
-80 °C
miRNA (2)*
mRNA
miRNA (1)
Buffy coat (1)
-80 °C
Buffy coat (2)
-80 °C
Epigenetics (1)
Epigenetics (2)
* Excess plasma not required for miRNA analysis may be
stored for potential adductomics use
Destinations
• Adductomics (1) – King’s College, London
• Adductomics (2) – Berkeley
• Metabolomics – IARC, Lyon
• Proteomics – Utrecht
• mRNA/miRNA (1)/miRNA (2) – Maastricht
• Epigenetics (1) – IARC, Lyon
• Epigenetics (2) – Athens
The “meet-in-the-middle”
concept
Advancing the application of omics-based biomarkers
in environmental epidemiology. Vineis P et al Environ
Mol Mutagen. 2013 Aug;54(7):461-7
The idea of meet-in-the-middle has several roots:
- the need to find biological plausibity to epidemiological
observations
- to create a “chain of causality” through intermediate
events
One of the major philosophers of causality in biology
(Wesley Salmon) argued that simple statistical
associations are not enough to establish causation, but we
need to identify a chain of intermediate events called “the
propagation of a mark”
TIME IS THE KEY ISSUE!
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
23 July 2013, London, UK
From exposure characterization (metabolomic profiles) to early disease markers
(14 to 18 translocations in follicular lymphoma)
Heat map depicting the associations between 10
environmental pollutants and 750 features
according to P-value
Methylation in ex-smokers (Guida et al,
HMG accepted)
These findings give origin to several important hypotheses:
(a) hematopoietic stem cells are involved, since the
persistence of altered gene methylation goes much beyond
the half-life of mature white blood cells;
(b) methylation changes in key genes (like AHRR) are likely
to confer selective advantage to cells and lead to clonal cell
selection.
What we call “hypomethylation” is a change in the
proportion of cells that are unmethylated at a certain CpG
site compared to those that are methylated. Methylation at
the single cell-single CpG site level is either 0 or 1.
Therefore, stem cells preserve a “memory” of past
exposures in the form of a greater proportion of cells with
unmethylated CpG sites vs methylated CpG sites.
We speculate that exposure to toxic agents selects a clone
of cells that are unmethylated in a CpG involved in the
activation of a pathway reactive to environmental insults.
This imprinting remains in the memory of stem cells and is
comparable to immunological memory (?)
The studies we conducted in smokers were in white blood
cells, i.e. “memory” involved hematopoietic stem cells.
However, we also investigated the lung tissue of smokers
and non-smokers (Shenker et al, 2011).
Methylation levels in the AHRR gene probes were
significantly decreased (P < 0.001) and expression
increased (P=0.0047) in the lung tissue of current smokers
compared with non-smokers. This was further validated in
a mouse model of smoke exposure with similar results.
Cumulative risk of lung cancer mortality among men in the United Kingdom who smoke,
according to the age when they stopped smoking. [Figure adapted from the original by
permission of the British Medical Journal (56)].
Vineis P et al. JNCI J Natl Cancer Inst 2004;96:99-106
© Oxford University Press
Back to meet-in-the-middle
The meet-in-the-middle concept is very rudimentary.
The next challenge is how to incorporate markers in a time sequence with
mathematical models - 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,2014)
- “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.
But the real challenge is that of emergence of a new property, as
described by the literature on complexity, i.e. how exogenous
molecules (e.g. pollutants) modify endogenous molecules (DNA,
RNA, proteins) and the latter make a difference in cells; then how
cells with such changes emerge as having selective advantage or
influencing surrounding or distant cells, etc., up to the level of the
organism and the population.
“In philosophy, systems theory, science, and art,
emergence is conceived as a process whereby larger
entities, patterns, and regularities arise through interactions
among smaller or simpler entities that themselves do not
exhibit such properties.
Emergence is central in theories of integrative levels and of
complex systems. For instance, the phenomenon life as
studied in biology is commonly perceived as an emergent
property of interacting molecules (…)”. (Wikipedia)