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 followingt 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 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 CisplatinGenetic susceptibilityPhenotypic markers DNA repair capacity in head

and neck cancerSNPsNAT2, GSTM BladderCYP1A1 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-two-stage

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').

10

20

30

0 50

ALSPACEPIC-ESCAPE

PICCOLI+

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

Mid- and late-life

Childhood

Early Adulth

ood Adolescence

Adulthood60

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

Blood Processing Protocol for Exposome Studies

Peripheral Blood (45 ml)

EDTA Tubes (10 ml x 2)

Serum Tube (7 ml x 2)

PAXgene(2.5 ml x 2)

Centrifugation

Plasma Buffy Coat RBC

Ficoll

PBMC

Centrifugation

Serum Clot

Adductomics, Bioassays,Chemicals

DNASequencing

-80 °C

-80 °C

-80 °C

DNA

-80 °C-80 °C

-80 °C

RNA

mRNA & miRNA Arrays

Protein

G/RBC

1.5ml x 6 1.5ml x 6

1.0ml x 6 7ml x 2

0.5ml x 2

0.5ml x 2

0.2ml x 2

-80 °C

Trace Element Tube (6 ml x 1)

-80 °C

Metals0.5ml x 2

RNAProtector

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