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ECNIS Web-based course in Molecular Epidemiology in Cancer Biomarker Validation

ECNIS Web-based course in Molecular Epidemiology in Cancer Biomarker Validation. Slide B1. 1.

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ECNIS Web-based course in Molecular Epidemiology in Cancer Biomarker Validation

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  1. ECNIS Web-based course in Molecular Epidemiology in Cancer Biomarker Validation Slide B1.1

  2. Commenti:intraindividual variation si riduce con biomarkers per esempio con la misurazione di POPs! (PCB ecc) perchè sono long-term e stabili (lipofilici)- adducts in cord blood? (Perera) http://www.aacrmeetingabstracts.org/cgi/content/abstract/2005/1/512-b

  3. Characteristics of ideal biomarkers • Sensitive and specific • Relation with exposure • Standardized and validated • Relatively easy to perform • Non-invasive • High throughput • Inexpensive Slide B1.3

  4. Levels of validation:Intra-individual variationInter-individual variationand confoundingIntra-laboratory variationInter-laboratory variationValidity (vs a standard) and predictive valueTime relationshipsDose-responseAbility to predict outcome Slide B1.4

  5. QUALITY CONTROL OF A BIOMARKER: MEASUREMENT ERROR Measurement error is classified as: preanalytical (biological and sampling error) Or analytical (laboratory) error Laboratory error focuses on method, instrument, reagent or matrix effects.

  6. Preanalytical Error • individual genetic, environmental, behavioural and health status-related variability (including smoking status, weight and weight loss, physical exercise) • Example of genetic source of variation: FOLATE AND MTHFR • Health status-related: retinol or ascorbate and trauma, several biomarkers and inflammation • sampling error: within subject variation due to hourly, daily, weekly, monthly … changes

  7. To reduce Analytical Errors – Quality Control measuresExample of Quality Control Program: national cholesterol Education Program (US) Goals:1. Attain analytical accuracy and precision (<3% cv)2. Identify individual determinants of cholesterol variation (lifestyle factors)3. Identify clinical determinants of variation (metabolic states, illness)4. Other sampling sources (fasting status, posture, serum vs. plasma)The NCEP guidelines have proven adequate to ensure 90% correct classification

  8. Overall measure of error is the COEFFICIENT OF VARIATION = SD/MEAN x 100%(SD in repeated measurements)CV is ideally calculated for samples at the bottom, middle and top of the reference concenttration range determined in healthy subjectsLog transformation is even better (Rappaport book)

  9. OTHER EXAMPLES OF QUALITY CONTROL:- Gunter et al (1996), international round-robin for folate involving 20 labs: CV of 27% for serum folate, and 36% for whole blood folate, with substantial intermethod variation- Pfeiffer et al (1999), interlaboratory comparison of homocysteine in plasma samples (14 labs): CV=9% among labs, and 6% within labs

  10. TWO MAJOR APPROACHES TO REDUCING • MEASUREMENT ERROR ARE: • TO BLIND THE ANALYST TO THE CASE-CONTROL STATUS OF SPECIMENS • 2. TO ELIMINATE SYSTEMATIC DIFFERENCES IN THE WAY CASE AND CONTROL SPECIMENS ARE HANDLED

  11. Validation and relevance: some examples Inter-centre variation (and potential confounding) for an intermediate marker (plasma DNA amount in EPIC) Slide B1.11

  12. Genetic alterations in plasma DNA • Useful when tumours not available • Good concordance between tumour and plasma mutations • When does tumour DNA appear in the blood? • Can plasma DNA be used as a biomarker for genotoxic exposure? Slide B1.12

  13. Utrecht DNA concentration sorted by EPIC number (origin) Slide B1.13

  14. Univariate and multivariate analysis: plasma DNA amount (logarithm transformation, dependent variable), by center, age, gender and time between blood drawing and diagnosis (for cases only). Univariate analysis: Variable F-value DF p-value Controls only (N=778) Center 11.23 22 <0.0001 Age 5.21 1 (a) 0.023 Gender 0.52 1 0.47 Cases and controls (N=1185): F-value DF p-value Center 16.6 23 <0.0001 Age 1.56 1 (a) 0.21 All deaths and tumours 2.3 6 0.03 Slide B1.14

  15. Biomarkers vs external/other measurements

  16. Cotinine measurements. Means, SD and distribution by detectable (greater than 0.05 ng/ml) and undetectable levels, by ETS status. Only subjects with ETS information (N=374). Values of cotinine greater than 10 ng/ml (N=11) excluded (Vineis et l, BMJ 2004).ETS status Mean cotinine (N, SD) Yes 0.55 (189, 0.96) No 0.17 (174, 0.49) p-value<0.0001 (Wilcoxon Rank-sum test)ETS status Cotinine Detectable UndetectableYes 89 100 No 37 137OR=3.30 (95% CI 2.07, 5.23) p<0.0001 Cotinine and ETS (environmental tobacco smoke) from questionnaires Slide B1.16

  17. Bulky DNA adducts and dose-response relationship (Peluso et al, AJE 2001 Slide B1.17

  18. Comments- The fact that adducts and other markers are related to exposure does not imply that they are a better measure- Biomarkers can increase biological plausibility of associations- They can be useful for example if it is possible to show that intra-individual variability is lower with the marker than with external exposure measurements - They can address issues such as saturation of enzymes at high levels of exposure (dose-response, risk assessment)- DNA adducts are an integrated marker (over several sources of exposure) that expresses also individual susceptibility (eg for DNA repair), and can be predictive of cancer onset Slide B1.18

  19. Genotypes vs exposures

  20. Genotyping Comparison of four genotyping methods at the Cambridge laboratory. The standard is represented by a panel evaluation of all results (courtesy of A Dunning). Slide B1.20

  21. Problems with studies on gene-environment interactions:- low study power- frequent false positives due to multiple testing- functional data often missing- early studies not confirmed by subsequent larger or better conducted studies- publication bias Slide B1.21

  22. Effects of random classification error on relative risk estimates R=correlation coefficients between the measurement of exposure/genotype by different assessors and a reference standard, and the resulting observed relative risks (modified from Hankinson et al, 1994, ref. 3). True relative risks (RRt)Assessor R 1.5 2.0 2.5 Observed relative risks 1 0.10 1.1 1.1 1.12 0.60 1.3 1.5 1.73 0.80 1.4 1.8 2.14 0.90 1.4 1.9 2.3Observed RR=exp (ln RRt*R) Slide B1.22

  23. According to estimates, the common genotyping method Taqman has 96% sensitivity and 98% specificity, thus allowing little error in classification. On the contrary, sensitivity in environmental exposure assessment is quite often lower than 70% and specificity even lower. Slide B1.23

  24. Genotype is stable, measured accurately (sens, spec=90-100%), frequency of alleles is highEnvironmental exposures are changing (life-course events), often measured inaccurately, frequency may be too low Slide B1.24

  25. In addition, genetic polymorphisms are investigated with high-throughput technologies that allow researchers to investigate hundreds of thousands of SNP at a time: with the usual p-values this originates a large number of false positives (see Bayesian strategy proposed by Colhoun et al, Lancet 2003 361: 865-872)In environmental research false negatives are an important problem Slide B1.25

  26. Limitations of current biomarker studies Some markers are not very reliable (e.g. interlaboratory variation for adducts) Biological meaning not always clear (e.g. mutations in plasma DNA) Long gap between marker development and its validation Unknown or unsatisfactory time relationships between exposure, marker measurement, disease Usually only one spot biosample available (little known on intra-individual variation) Little known on potential confounders Slide B1.26

  27. Further issues in validation of biomarkers and examples (from R Godschalk) • Knowledge of pharmacokinetics and relevance of measurements in time • Insights in reasons for inter- and intra-individual variation • Measured variation = Inter + Intra + variation in assay • 3. Surrogate vs. Target tissue • WBC vs. lung tissue in smokers • 4. Comparison with other Biomarkers (“gold standard”) • for example 1-OH-Pyrene in urine Slide B1.27

  28. and inflammation Cig. smoke (Particles) Neutrophils ROS Activation H2O2 MPO +Cl HOCl Exposure to cig smoke  lung DNA damage Cig. smoke (PAH) Detoxication GSTs NATs Activation CYP450s MPO PAH-DNA adducts DNA repair NER BER DNA damage/mutagenesis Cancer Slide B1.28

  29. Characteristics of ideal biomarkers Sensitive and specific - Relation with exposure + Standardized and validated - Relatively easy to perform +/- Non-invasive + High throughput - Inexpensive + Immunocytochemical staining of PAH-DNA adducts in Mouth Brushes Slide B1.29

  30. The use of Induced Sputum (IS) in smoking-related DNA adducts analyses • Objectives • To study the applicability of Induced Sputum (IS) as source of lung derived cells • To establish correlation between DNA adduct levels in IS derived cells and smoking intensity • To compare DNA adduct levels in IS with PBL • Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands Slide B1.30

  31. Intra-individual DNA adduct analysis Intra-individual variation in IS is higher than in PBL When quantitating the adduct levels in Induced Sputum of certain individuals considerable variation could be observed. We could not find a reason for that since smoking habits and dietary conditions were kept similar over time as much as possible. There is also some variation in PBL but to a lower extent. Slide B1.31

  32. Characteristics of ideal biomarkers Sensitive and specific +/- Relation with exposure + Standardized and validated +/- Relatively easy to perform - Non-invasive +/- High throughput - Inexpensive +/- Postlabeling of DNA adducts in Induced Sputum Slide B1.32

  33. Stability of DNA adducts in PBL of smokers 21 weeks stopped smoking. Decay curve exponentially. BG in nonsmokers. Godschalk et al. CEBP 1998 Slide B1.33

  34. Saturation in DNA adduct levels at high exposure levels A) DNA adduct levels B) efficiency of DNA adduct formation in (○) smoking and (•) non-smoking aluminium workers exposed to PAH 1 foundry 2 electrolysis 3 bake oven 4 anode factory 5 pot-relining department Van Schooten et al. Mut Res 1997 Slide B1.34

  35. Correlation between surrogate and target tissue HOWEVER, * Not all studies find such a relationship. * DNA adduct levels in PBL/ WBC of low exposed subjects are often below or near the limit of detection Wiencke et al (1999) J Natl Cancer Inst;91(7):614-9. Slide B1.35

  36. Differences between tissues The example of cigarette smoke exposure 16 14 12 10 Adducts per 108 nucleotides 8 6 4 2 DL = detection limit 0 Lung BAL IS Mono- Lympho- Granulo- cytes cytes cytes WBC Slide B1.36

  37. Overall Conclusions Type of tissue and choice of biomarker depends on research goal Most biomarkers AND surrogate tissues still need further validation. Laboratory validation ‘Field’ validation Non-invasive and high throughput methodologies are required for Molecular Epidemiology studies Slide B1.37

  38. The end

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