ECNIS Web-based course inMolecular Epidemiology in Cancer

Biomarker Validation

Slide B1.1

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

Characteristics of ideal biomarkers

1. Sensitive and specific2. Relation with exposure3. Standardized and validated4. Relatively easy to perform5. Non-invasive6. High throughput7. Inexpensive

Slide B1.3

Levels of validation:

Intra-individual variationInter-individual variation

and confounding

Intra-laboratory variationInter-laboratory variation

Validity (vs a standard) and predictive value

Time relationships

Dose-response

Ability to predict outcome

Slide B1.4

QUALITY CONTROL OF A BIOMARKER: MEASUREMENT ERROR

Measurement error is classified as:

preanalytical (biological and sampling error) Oranalytical (laboratory) error

Laboratory error focuses on method, instrument, reagent or matrix effects.

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

To reduce Analytical Errors – Quality Control measures

Example 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

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 subjects

Log transformation is even better (Rappaport book)

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

TWO MAJOR APPROACHES TO REDUCING MEASUREMENT ERROR ARE:

1. 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

Validation and relevance: some examples

Inter-centre variation (and potential confounding) for an intermediate marker (plasma DNA amount in EPIC)

Slide B1.11

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

GENAIR DNA concentration

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DNA concentration sorted by EPIC number (origin)

Utrecht

Slide B1.13

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 <0.0001Age 5.21 1 (a) 0.023Gender 0.52 1 0.47

Cases and controls (N=1185):F-value DF p-value

Center 16.6 23 <0.0001Age 1.56 1 (a) 0.21All deathsand tumours 2.3 6 0.03

Slide B1.14

Biomarkers vs external/other measurements

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 Undetectable

Yes 89 100No 37 137

OR=3.30 (95% CI 2.07, 5.23) p<0.0001

Cotinine and ETS (environmental tobacco smoke) from questionnaires

Slide B1.16

Bulky DNA adducts and dose-response relationship (Peluso et al, AJE 2001

Slide B1.17

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

Genotypes vs exposures

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

Method Sensitivity % Specificity %

ASO 836/864 97 753/836 90

Taqman 826/864 96 812/826 98

RsaI digest 125/173 72 103/125 82

Invader 62/92 67 45/62 73

Genotyping

Slide B1.20

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

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 2.3

Observed RR=exp (ln RRt*R)

Slide B1.22

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

Genotype is stable, measured accurately (sens, spec=90-100%), frequency of alleles is high

Environmental exposures are changing (life-course events), often measured inaccurately, frequency may be

too low

Slide B1.24

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

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

1. Knowledge of pharmacokinetics and relevance of measurements in time

2. Insights in reasons for inter- and intra-individual variationMeasured variation = Inter + Intra + variation in

assay

3. Surrogate vs. Target tissueWBC vs. lung tissue in smokers

4. Comparison with other Biomarkers (“gold standard”)for example 1-OH-Pyrene in urine

Further issues in validation of biomarkers and examples (from R Godschalk)

Slide B1.27

Exposure to cig smoke lung DNA damage

DNA damage/mutagenesis

Cancer

Cig. smoke (PAH)

ActivationCYP450s MPO

PAH-DNA adducts

DNA repairNER BER

DetoxicationGSTsNATs

Neutrophils

Activation

MPO

H2O2

HOCl

+Cl

ROS

Cig. smoke (Particles)

and inflammationand inflammation

Slide B1.28

Characteristics of ideal biomarkers

1. Sensitive and specific -2. Relation with exposure +3. Standardized and validated -4. Relatively easy to perform +/-5. Non-invasive +6. High throughput -7. Inexpensive +

Immunocytochemical staining of PAH-DNA adducts in Mouth BrushesImmunocytochemical staining of PAH-DNA adducts in Mouth Brushes

Slide B1.29

The use of Induced Sputum (IS) in smoking-related DNA adducts analyses

Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht,

The Netherlands

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

Slide B1.30

Intra-individual DNA adduct analysis

Intra-individual variation in IS is higher than in PBL

Slide B1.31

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.

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.

Characteristics of ideal biomarkers

1. Sensitive and specific +/-

2. Relation with exposure +

3. Standardized and validated +/-

4. Relatively easy to perform -

5. Non-invasive +/-

6. High throughput -

7. Inexpensive +/-

Postlabeling of DNA adducts in Induced SputumPostlabeling of DNA adducts in Induced Sputum

Slide B1.32

Stability of DNA adducts in PBL of smokers

Godschalk et al. CEBP 1998 Godschalk et al. CEBP 1998 Slide B1.33

21 weeks stopped smoking. Decay curve exponentially. BG in nonsmokers.21 weeks stopped smoking. Decay curve exponentially. BG in nonsmokers.

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 foundry2 electrolysis3 bake oven4 anode factory5 pot-relining department

A) DNA adduct levels B) efficiency of DNA adduct formation

in (○) smoking and (•) non-smoking aluminium workers exposed to PAH

1 foundry2 electrolysis3 bake oven4 anode factory5 pot-relining department

Van Schooten et al. Mut Res 1997Van Schooten et al. Mut Res 1997Slide B1.34

Correlation between surrogate and target tissue

Wiencke et al (1999) J Natl Cancer Inst;91(7):614-9.

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

Slide B1.35

Differences between tissues

The example of cigarette smoke exposure

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Lung BAL IS Mono- Lympho- Granulo- cytes cytes cytes

WBC

Slide B1.36

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

The end