Presentation on theme: "How to optimize the study design 1. Theory and biological plausibility Paolo Vineis Firenze 19 June 2013."— Presentation transcript:
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
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)= r 1 r 2 … 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 Exposure Internal dose Urinary metabolites (NNK, NNN) Nitrosocompounds in tobacco Biologically effective dose DNA adductsPAHs, aromatic compounds Albumin adducts AFB 1 Hemoglobin adducts Acrylamide, Styrene, 1,3-Butadiene Preclinical effect Exposure and/or cancer Chromosome aberrations Lung, Leukemia, Benzene HPRT PAHs, 1,3-Butadiene Glycophorin A PAHs Gene expression Cisplatin Genetic susceptibility Phenotypic markers DNA repair capacity in head and neck cancer SNPs NAT2, GSTM Bladder CYP1A1 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 ALSPAC EPIC- 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 Adulthood Adolescence Adulthood 60 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 SmartPhone MicroAeth (BC) UFP Sensor USB hub 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 Rechargeable Battery 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 SerumClot Adductomics, Bioassays, Chemicals DNA Sequencing -80 °C DNA -80 °C RNA mRNA & miRNA Arrays Protein G/RBC 1.5ml x 6 1.0ml x 67ml x 2 0.5ml x 2 0.2ml x 2 -80 °C Trace Element Tube (6 ml x 1) -80 °C Metals 0.5ml 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