IRCCS San Raffaele Pisana, Rome, Italy, 28 February - 2 March 2018 Why do we need epidemiology and statistcs in human studies with biomarkers ? IRCCS San Raffaele Pisana, Rome, Italy, 28 February - 2 March 2018

The past ... I received my degree in Biological Sciences in 1981 at the University of Genoa and the specialty in Epidemiology and Biostatistics 6 years later, at the University of Pavia. My career started as occupational and environmental epidemiologist. In early 1990’s I started working on the design and analysis of human studies based on biomarkers of chromosomal damage, entering the realm of Molecular Epidemiology.

The present ... My current interests are: - Validation of biomarkers of chromosome damage as predictors of cancers (and other diseases) in humans Standardization and enhancement of the Micronucleus test in PBL (the HUMN project) and exfoliated buccal cells (The HUMNxl project) Improvement of statistical methods in human biomonitoring studies Statistical analysis of microarrays and other high-throughput techniques - Creation of platforms of systems medicine (Respiratory and psychiatric diseases)

The future ... I am planning researches on Use of high throughput technologies in human studies Design and Statistical analysis Application in public health of biomarkers validated as risk predictors Validation of various indexes of individual genetic susceptibility as predictors of adverse outcomes Molecular epidemiology of neurodegenerative disorders Molecular epidemiology of respiratory and psychiatric diseases Geriatrics: Markers of (well) aging Systems Biology and Systems Medicine

Epidemiology Laboratory

External Exposure Cancer

Genetic Polymorphisms External Exposure Endogenous Exposure Genetic Polymorphisms Target Tissue SurrogateTissue Cancer

Deterministic model Stocastic model The major reason for using epidemiology in biomarkers studies is that this allows us to move from a Deterministic model Good for Mutations Good for Polymorphisms Stocastic model

ENVIRONMENT GENES DISEASE Biomarkers of Susceptibility Biomarkers of exposure Biomarkers of early effect DISEASE

SNParray and individual to Malignant Mesothelioma Susceptibility to Malignant Mesothelioma

Study design A Molecular Epidemiology Case-Control Study 93 mesotheliomas 115 lung cancers 128 controls Collected from Pneumology departments of hospitals located in Liguria, Italy

The Array 101 GENES, 247SNPs ABCG2 (2), ADH1B(3), ADH1C, AHR, ALDH2(4), APEX/APE1(2), ATM(6), ATR,BARD1(2), BRCA1(5), BRCA2(3), CASP-10CASP-3, CASP-8, CASP-9,CCND1(2), CDA(2),CDK7,CDKN1B, CDKN2A(3), CDKN2B(2), CHEK2,COMT(4), CYP1A1(6), CYP1A2(4), CYP1B1(6), CYP2A6(4), CYP2C18, CYP2C19, CYP2C9(2), CYP2D6(9), CYP2E1(6), CYP3A4, DRD2(7), DRD4(5), EPHX1(4), ERCC1(5), ERCC2, ERCC2 / XPD(2), ERCC4 / XPF, ERCC5 / G(2), FANCD2, GADD45A, GRPR, GSTA2, GSTA4(2), GSTM1, GSTM3(3), GSTP1(3), GSTT2(2), IRS2(3), LIG1(4), LIG3, LIG4(2), MDM2(4), MDR1(3), MGMT/AGT(4), MLH1, MnSOD2, MSH2, MSH3(3), MSH6(2), MTHFR(3), MYH(2), NAT1(7), NAT2(6), NBS1(2), NOD2/CARD15(4), NQO1/DIA4(2), NT5E,OGG1, p21/ KN1A(2), PARP/ADPRT(6), PCNA(3), PMS2, POLB(2), RAD23, RAD51(2), RAD52, RAD54B, RAD9, RB1(2), RECQL, SLC6A3/DAT1(2), SULT1A1(2), SULT1A2(2), TERC, TERT, TP53(3), TP53BP1(2), TP53BP2(2), TPMT(3), UGT1A7(2), XPA, XPC, XRCC1(3), XRCC2(3), XRCC3(3), XRCC4, XRCC5(2), XRCC9

SNP Mutated Odds Ratio P CYP1B1 3.30 <0.001 CYP2D6 5.24 <0.01 MnSOD2 3.07 <0.001 GSTM1 2.10 ~ 0.02 NAT2 fast 2.15 ~ 0.02 mEH low-activity 1.95 <0.05 XRCC2 2.25 <0.01

A continuum of biomarker categories reflecting the carcinogenic process resulting from xenobiotic exposures Exposure Biologically Early Internal Effective Biological dose dose Effect Altered Structure/ Function Susceptibility Disease Committee on biological markers of the National Research Council. EHP, 1987.

European Study Group on Cytogenetic Biomarkers and Health - ESCH

Multivariate Cox Regression Analysis of CA Frequency Hagmar et al., Cancer Res, 1998

Kaplan-Meier curves for total cancer incidence and mortality Survival refers to time from CA test to first cancer diagnosis or cancer death Hagmar et al., Cancer Res, 1998

The p value for a hypothesis test is the probability of obtaining, when H0 is true, a value of the test statistic as extreme as or more extreme than the one actually computed. using p values is more informative than just saying significant or not significant, but ......

The common epidemiologic approach to the study of health effects recognizes as the principal aim of a study that of quantifying exposures-effect relationships, rather then simply evaluate whether an effect is present.

A point estimate of the association between exposure and effect should be computed, along with its confidence interval, so as to provide a range of values within which the true association falls with a given probability. and this concepts bring us to the measures of association !

Measures of association reflect the strength of the statistical relationship between a study factor and a disease

The most useful and widely used measures of association in Epidemiology and Clinical Investigation are: Risk difference, Incidence Rate Difference, Risk Ratio, Incidence Rate Ratio, and Odds Ratio

Possible benefits to using biomarkers in human studies Biologic phenomena not otherwise measurable Reduce impact of differential exposure missclassification Introduce individual parameters into the study design

Possible benefits to using biomarkers in human studies Pre-disease Events/States Pre-clinical disease Link exposure to natural history Shorter studies Increase disease homogeneity (etiologic or prognostic)

Possible benefits to using biomarkers in human studies Make better inferences ref. to disease mechanisms Improve process of causal inference Improve risk assessment Improve prevention strategies Improve disease treatment