How to optimize the study design
1. Theory and biological plausibility
Paolo Vineis
Firenze 19 June 2013
Scientific questions can be rather complex or
sophisticated, and to assess causality you need
“biological plausibility”, e.g. “is it plausible in terms of
background biological knowledge that mobile phones
cause cancer?”
A way to address these issues is to incorporate
biomarkers/omics into epidemiological studies
More sophisticated questions?
Example
Armitage and Doll in 1954 proposed a multistage model
based on the observation that the incidence rate of most
epithelial tumors rises with a power of age (5-6th power).
They hypothesized:
- that cancer is not due to age itself but to prolonged duration
of exposure to carcinogens
- that for a life-long exposure an increase with a power of 6
means that there are 6 stages in carcinogenesis (6 mutations?)
- for discontinued exposures the model becomes more
complex
Notice: all based on simple data on age distribution!
I(t)= r1r2 … r(n-1) (t-w)n-1
where r is the transition rate from a stage to the following
t is age
w is the time mecessary to last-stage cells to give rise to a
clinically overt cancer
As an approximation:
I(t)=K t n-1
(n-1) refers to the transition rates
The relationship with age holds true for most epithelial
cancers (exponential of age: 6 for oesophagus, stomach,
pancreas, bladder, rectum, colon), but not for lung and breast
(cohort phenomena)
THE BASIC IDEA IS THAT IT IS NOT AGE BUT
DURATION OF EXPOSURE THAT COUNTS
EXPERIMENTS BY IVERSEN: TREATMENT OF MICE WITH
DMBA
(CARCINOGENESIS, 1991)
A SINGLE DOSE OF 51.2 MICROGRAMS GAVE A TUMOR
RATE OF 40%, WHILE THE SAME DOSE DIVIDED INTO 50
DOSES OF 1 MICROGRAM GAVE A 100% RATE
Biological question: what is the role of mutations and what
is the role of «promotion», cell proliferation, clonal
expansion ...?
Lessons
(a) different mathematical models are compatible with the
evidence on age-specific cancer incidence
(b) different biological models (e.g. involving clonal expansion
or stem cell death) are compatible with epidemiologic evidence
(c) however, it is likely that selection of mutated clones AND of
clones with mutator phenotype (explain) is involved
(d) to answer these questions we need biomarkers!
Incorporation of biomarkers/omics into
epidemiological research: design issues
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
Internal dose
Urinary metabolites (NNK, NNN)
Biologically effective dose
DNA adducts
Albumin adducts
Hemoglobin adducts
Exposure
Preclinical effect
Chromosome aberrations
Exposure and/or cancer
Lung, Leukemia,
Benzene
PAHs, 1,3-Butadiene
PAHs
Cisplatin
HPRT
Glycophorin A
Gene expression
Genetic susceptibility
Phenotypic markers
SNPs
NAT2, GSTM
CYP1A1
Nitrosocompounds in tobacco
PAHs , aromatic compounds
AFB 1
Acrylamide, Styrene,
1,3-Butadiene
DNA repair capacity in head
and neck cancer
Bladder
Lung
Vineis and Perera, 2007
The best design is the nested case-control study
within a cohort
Many population based cohorts exist in Europe, with related
biobanks, both in adults and children
Total size amounts to several millions people
Largest include EPIC, UK Biobank
Some are specialized (e.g. Sapaldia on air pollution), most are
not
However ...
The measurement of most biomarkers with usual lab techniques
requires large amounts of biological material
E.g. PCB in serum 0.5 ml, 1 straw in EPIC
Bulky DNA adducts 1-5 microg of DNA
Need to explore the possibilities offered by new technologies, so
called «omics»
“Individual and Population Exposomes”
Cell (2008) 134:
714-717
Challenges in using cohorts:
1. precious biobanked material, not easily released by PIs
2. ethical issues
3. single (spot) biological samples
4. usually blood, not urine (which may be better e.g. for metabolomics)
5. no cohorts allow life-course epidemiology
6. in-depth exposure assessment is limited by feasibility (for cancer you need
large sample sizes)
7. lab measurements and omics have the same limitations related to sample
size and feasibility
“Creative study design”, i.e. when you are dealing with
a very complex request
(“mission impossible”)
ENV.2012.6.4-3 Integrating environmental and health
data to advance knowledge of the role of environment
in human health and well-being in support of a
European exposome initiative - FP7-ENV-2012-twostage
The aim will be to exploit available or to-be-developed novel tools and
methods (e.g. remote sensing/GIS-based/spatial analysis, 'omics'-based
approaches, biomarkers of exposure, exposure devices and experimental
models, new tools for combined exposures, novel study designs, burden of
disease methodologies) to integrate and link environmental data with health
data and information, and to apply them to (large-scale) population studies
including new ones if deemed necessary (a concept that was recently
proposed in the literature as 'exposome').
Exposome: Totality of exposure from air, water, diet, lifestyle, behavior,
metabolism, inflammation, oxidative processes, etc. - during all stages of life
Critical stages of life - define
RAPTES
PISCINA
20
SAPALDIA
EPICESCAPE
Age
30
ALSPAC
10
PISCINA
INMA
OXFORD ST
0
RHEA
50
PICCOLI+
Birth
60
MCC
Mid- and late-life
EPICURO
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).
Blood Processing Protocol for Exposome Studies
Peripheral Blood
(45 ml)
EDTA Tubes
(10 ml x 2)
PAXgene
(2.5 ml x 2)
Serum Tube
(7 ml x 2)
Trace Element Tube
(6 ml x 1)
Centrifugation
Centrifugation
Plasma
Buffy Coat
RBC
1.5ml x 6
1.5ml x 6
0.2ml x 2
-80
°C
Serum
Clot
1.0ml x 6
7ml x 2
-80 °C
-80 °C
-80 °C
-80 °C
RNA
Protein
DNA
Metals
Ficoll
-80
°C
-80 °C
PBMC
G/RBC
0.5ml x 2
RNAProtector
0.5ml x 2
0.5ml x 2
-80 °C
mRNA &
miRNA Arrays
Adductomics, DNA
Bioassays,
Sequencing
Chemicals
Lessons on how to write a grant:
- is exposure assessment good?
- are you using validated laboratory tests?
- are you collecting enough biological material? How will it be
split into the labs?
- is the design of the study able to use the MITM concept?
- what about statistical power?
- FEASIBILITY!
- ethics