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An assessment of the baseline variability in the level of DNA damage in women as measured by the single… Ell, Karalynn Elizabeth 1996

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AN ASSESSMENT OF THE BASELINE VARIABILITY IN THE LEVEL OF DNA DAMAGE IN WOMEN AS MEASURED BY THE SINGLE CELL GEL ELECTROPHORESIS ASSAY by KARALYNN ELIZABETH ELL B.Sc, The University of Alberta, 1992 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Occupational Hygiene Programme) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA April 1996 © Karalynn Elizabeth Ell, 1996 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of GroxUa4e, S-WllfrS The University of British Columbia Vancouver, Canada Date 0 l / 3 Q / % DE-6 (2/88) Abstract It is thought that a correlation between levels of DNA damage expressed in an individual and the potential for the future development of cancer exists, and that biomonitoring for such damage could eventually be used to detect individuals at risk before the onset of disease. The purpose of studies such as that described in this thesis is to develop and refine methods of biomonitoring for genetic damage. The objective of this thesis was to assess the background levels of DNA damage in women using the Single Cell. Gel Electrophoresis (SCGE) assay and interpret the meaning of the observed variability with respect to future study design and the sample size necessary to ensure statistical significance. Thirteen female subjects were recruited from the Vancouver Hospital-UBC site and the Occupational Hygiene programme at the University of British Columbia. Each subject was to provide six blood samples over a period of ten weeks, at varying or irregular intervals. A total of 73 blood samples were obtained. Peripheral blood lymphocytes were isolated from the samples, embedded in agarose, lysed to release the nuclear contents, and exposed to an electric current in order to allow the DNA to migrate from the nucleus. This procedure, known as the SCGE or "Comet" assay, enables the detection of single strand breaks and alkali-labile sites in the cellular DNA, as smaller fragments created by breakage will travel farther from the nucleus of the cell than larger fragments or unbroken DNA. Both technical and biological variability were observed in the sample data. A calculation of the coefficient of variation for several groups of data provided a crude estimate of variation for this study. The components of overall or total variability could not be determined, but both the inter- and intra- individual variability appeared to exceed the replicate-to-replicate technical variability. The internal standard used in this study did not provide the information desired with respect to day-to-day variability. It was concluded that the observed variation within individual subjects in the study necessitates the use of a longitudinal or multiple-samples-over-time study design, rather than cross sectional. The sample size necessary for statistical significance in a cross-sectional study comparing two groups with an alpha of 0.05 and a power of 0.80 is approximately 70 individuals per group if the detection of an increase of fifteen percent in image length is desired, and approximately 20 individuals per group if an increase of thirty percent in image length is to be detected. iv Table of Contents Abstract ii Table of Contents iv List of figures vi i i List of tables ix Abbreviations ix I. Introduction and background 1 1.1 Introduction 1 1.2 Rationale for biomonitoring 4 1.2.1 The link between biomarkers of genetic 4 damage and cancer 1.2.2 Different types of biomarkers 5 1.3. Issues in biomonitoring 6 1.3.1 Lymphocytes - the physiology and kinetics 6 of sampling and monitoring 1.3.2 Variability of response 8 1.3.3 Sources of variability and methods of minimization 9 1.3.3.1 Biological 9 1.3.3.2 Technical 10 1.4 Measuring. Genotoxicity 11 1.4.1 Lymphocyte Assays 11 1.4.1.1 Sister Chromatid Exchange (SCE) 11 1.4.1.2 Chromosome Aberrations (CA) 12 1.4.1.3 Micronucleus (MN) 12 1.4.1.4 Single strand breakage assays (ssb) 12 1.4.1.4.1 Single Cell Gel Electrophoresis 13 (SCGE or "Comet") assay 1.4.2 Comparison of lymphocyte assays: advantages 14 of the Comet assay 1.5 History of the determination of background levels from 15 genotoxicity assays II. Objective and specific aims 18 III. Materials and Methods 19 III. 1 Contacting and recruitment of subjects 19 111.2 Development and administration of the questionnaire 20 111.3 Blood sample collection 21 111.4 Lymphocyte isolation 21 III.4.1 Counting of cells in a solution 22 111.5 Control cells - culturing and usage of a MOLT-4 cell line 23 111.6 Procedure for the SCGE Assay 24 111.6.1 Preparation of slides 24 111.6.2 Cell lysis 25 111.6.3 DNA unwinding and electrophoresis 25 111.6.4 Neutralization 26 111.6.5 Alcohol fixing 26 111.6.6 Staining of slides 26 111.6.7 Scoring of slides 26 111.7 Enumeration and analysis of data 28 IV. Results 30 IV. 1 Recruitment of subjects 30 IV.2 Blood Sampling 31 IV.3 Photographic examples of the images observed 31 IV.4 Questionnaires 34 IV.5 Data Analysis 35 IV.5.1 Presentation of results 35 IV.5.2 Comparison of means when fifty versus one 36 hundred cells are scored v i IV.5.3 Investigating the relationship between assay 36 results and the date of sample. IV.5.4 Use of different metrics for analysis 47 IV.5.5 Assessment of the effectiveness of MOLT-4 cells as 54 -an internal standard IV.5.6 Assessment of the technical, inter- and intra- 5 8 individual variability IV.5.7 Comparison of the blood sample and 61 questionnaire data. IV. 5.8 Normality testing of the data 66 IV. 6 Sample size calculation 66 V. Discussion 67 V. l Recruitment 67 V.2 Development of the experimental protocol for the comet assay 68 V.3 Future changes to the experimental protocol 70 V.4 Correlation between date of sample and mean image length 70 V.5 Analysis of the technical variability 71 V.6 Assessment of the inter- and intra-individual variability 73 V.7 Comparison of the different test metrics 74 V.8 What do the results say about study design? 75 V.9 Future considerations for the use of the comet assay 75 in field studies V. 9.1 Development of the Comet assay protocol 75 V.9.2 Use of the MOLT-4 cell line as an internal standard 77 V.9.3 Development and assessment of the questionnaire 78 V.9.4 Recruitment of subjects 79 V.9.5 Statistical analysis of the data obtained 79 V.9.6 Summary 79 VI Conclusions 81 v i i VII References 83 VIII Appendices A - Information sheet and letter of introduction provided to the 88 nursing units for the purpose of recruitment B - Letter of consent to participate in this study 91 C - Questionnaire 94 D - Results of Blood sampling 107 E - Questionnaire results 109 F - Raw data 113 G - The t-test results for each 50 cell sample 129 vi i i List of Figures Figure 1 Conceptual basis for the development of biomarkers for use in 5 m o l e c u l a r e p i d e m i o l o g y Figure 2: a.) Diagrammat ic representation of the scor ing protocol , and 29 b.) Diagrammat ic representation of the grat icule F igure 3: Photographic examples o f the images observed 32-33 F igure 4 a.-m.) Statist ical analysis and histogram for image length 37-43 measurements, for a l l subjects F igure 5: a.) The relationship between the mean image length in p m 44 -46 and the date of sample, b. ) the relationship after the samples from the first sampl ing day is removed, c. ) the relationship after the fo l l owing two sampl ing days are also removed. F igure 6-11: Graphica l representation of the six test metrics for each 48-53 sample, grouped by subject. F igure 12: Compar ison of a.) mean blood sample image length with 55-56 the mean image length of the corresponding M O L T - 4 slide and b.) comparison with large M O L T - 4 cells excluded. F igure 13: Sample plots of questionnaire responses versus blood sample data 63-65 a. ) age of subject versus mean image length, b. ) v i tamin C intake versus mean image length, and c. ) smoking status versus mean image length ix List of Tables Table 1: Examples of the determination of background variability 15-16 in genotoxicity assays Table 2: Coefficient of variation and ANOVA calculations used to 57 assess the stability of the MOLT-4 cell line Table 3: Coefficient of variation for a.) replicate blood sample slides, 59-60 b.) between subjects, within days, and c.) between subjects. Abbreviations ANOVA- analysis-of-variance C A - chromosome aberrations C V - coefficient of variation EDTA - ethylenediaminetetraacetic acid FBS - fetal bovine serum HepB - hepatitis B HBSS - Hank's buffered saline solution HIV - human immunodeficiency virus HLA - human leukocyte antigen HPRT - hypoxanthine guanine phosphorybosyltransferase MN - micronucleus PBL - peripheral blood lymphocytes SCE - sister chromatid exchange SCGE- single cell gel electrophoresis U B C - University of British Columbia 1 /. Introduction and Background 1.1 Introduct ion It is well known that environmental and occupational exposures to chemicals play a potential role in the development of cancer. In order to understand and minimize this potential for disease development, it is desirable to monitor and obtain data from human populations. Historically, relationships between ambient exposure and disease frequency in a population of interest are derived by comparing the disease frequency in the general population with estimated exposures. This situation is unfavorable for two reasons: exposure estimation is difficult and considerable differences can exist between an ambient exposure and the biologically relevant internal dose of a substance that is received, and it is necessary for the disease of interest to develop in a significant number of people before a cause-effect relationship can be established. In light of this, it is often desirable to obtain data by looking directly at exposed individuals or groups for early biological effects. In this way, a harmful exposure could be minimized before the onset of disease in order to minimize future negative health effects. 2 A method of biological monitoring in humans is the use of biological markers. Biomarkers are cellular, biochemical or molecular changes that are measurable in biological media, such as body fluids and tissues, that can provide information on internal exposure, susceptibility, and/or biological effects such as DNA damage (Hulka, 1990; Wilkosky, 1990). Biomonitoring has several advantages over the inference of exposure via ambient exposure measurements. Fluctuating exposures and exposure from more than one source of entry (i.e. inhalation and dermal absorption) can be integrated into one exposure measurement, non-specific hazards can be detected, acute and long term exposure associations can be made, and an increase in the knowledge of disease mechanisms can be gained (Wilkosky, 1990; Wilkosky & Griffith, 1990). The use of biomarkers can also improve risk assessment by increasing the validity and decreasing bias in epidemiological studies, and can increase the understanding of genetic variability and susceptibility in an individual (Hulka, 1990; Perera & Whyatt, 1994). A common application of biological markers in epidemiology is to the study of mutagenic and carcinogenic agents. Because of the latency period involved in cancer development it can be difficult to establish an exposure-disease relationship. By providing an earlier occurring and more sensitive outcome than tumour formation, a biomarker, assuming a valid relationship with future cancer development, may overcome these problems of latency (Perera & Whyatt, 1994). In addition, utilization of biomarkers of genetic damage can potentially lead to an avoidance of cancer development by stimulating a reduction of potentially harmful exposures before the initiation of carcinogenesis occurs. 3 There are several types of information needed regarding a particular biological marker in order to determine its utility in epidemiological research, all of which are concerned with validity. There are three broad categories of validity: measurement, internal study, and external (Schulte & Mazzuckelli, 1991; Vine, 1992). Measurement validity refers to the ability of a marker to correctly reflect dose, characterize a dose-response relationship between exposure and disease, and exhibit a predictive relationship between marker response and disease development. Internal study validity has been defined as "the degree to which index and comparison groups are selected and compared so that, apart from sampling errors, the observed differences between the dependent variables are attributed only to the hypothesized effects" (Last, 1983). In other words, internal study validity refers to the variability of the analytical technique and the effect of genetic, environmental, and other factors that can influence the total variability, affecting the reliability of the marker. The external validity refers to the applicability of the biomarker to large numbers of people in a non-experimental setting (Hulka, 1990). For many biomarkers, methodological development and exposure assessment are well documented, but the background variability in a "normal" population is not well quantified or understood. The purpose of this thesis is to assess the internal study validity or the baseline variability of a specific biomarker assay of DNA damage, the Single cell gel electrophoresis or "Comet" assay. 4 1.2 Ra t iona l e for b i o m o n i t o r i n g 1.2.1 The l ink between biomarkers of genetic damage and cancer: F o r a biomarker to be a useful tool, there has to be a relationship to the b i o l o g i c a l phenomenon of interest. In the case of carcinogenesis , the b iomarker must measure an occurrence which can be causally associated with tumour d e v e l o p m e n t . Essent ia l ly a l l cancers are the result o f mutation in a somatic ce l l which results in uncont ro l led growth and prol i fera t ion (Therman, 1986; T h o m p s o n , Mc lnnes , & W i l l a r d , 1991). There are at least two types o f genes involved - oncogenes and tumour suppressers. It is known that chromosomal defects such as translocations can result in tumour development. T w o wel l known examples are the translocation between chromosomes 8 and 14 in Burki t t ' s lymphoma and between chromosomes 9 and 22 in chronic myelogenous leukemia (Therman, 1986; Thompson , M c l n n e s , & W i l l a r d , 1991). Increased risk of malignancy is also associated with inherited condi t ions resul t ing in chromosome ins tabi l i ty , such as ataxia Telangectas ia , Fanconi anemia, B l o o m syndrome, and xeroderma pigmentosum (Cleaver , 1968; L o h m a n , M o r o l l i , Darroudi , Natarajan, Gossen, Venema, et al . , 1992; Thompson, et a l . , 1991). A s w e l l , there is a tendency for known carcinogenic chemicals to cause chromosome breakage (Sorsa, W i l b o u r n , & V a i n i o , 1992). It seems reasonable to assume, then, that somatic chromosomal and D N A damage is directly related to cancer formation. In fact, it is generally believed that any agent capable o f causing structural changes to D N A has the potential for carc inogenic i ty ( V i n e , 1992). Th i s log ica l inference, however, is not yet supported by much experimental evidence in humans, as it is usual ly imposs ib le to correlate 5 biomarker responses to tumour incidence occurring many years after the measurement was made. 1.2.2 Different types of biomarkers: There are several different types of biomarkers, classified by the stage in disease development that is measured. These classifications are internal dose, biologically effective dose, biological response, and susceptibility (figure 1). Exposure Environ-mental genotoxic chemical A Internal dose Biologically effective dose Metabolites DNA and protein ndducts Early biological effect I I 1 Altered structure-function 1 HPRT, HLA, SCEs, MN, SC.CIi Mutations, deletions, translocations Clinical disease 1 Malignant tumour absorption, distribution, metabolism repair, replication cell proliferation, clonal expansion further genetic change Figure 1: Conceptual basis for the development of biomarkers for use in molecular epidemiology (Wogan, 1992) Internal dose biomarkers involve the measurement of the amount of a carcinogen or its metabolite present in cells, tissues, or body fluids (Perera & Whyatt, 1994). An example of this would be the measurement of mutagenic compounds in urine. 6 Markers of biologically effective dose indicate amount of chemical that has interacted with critical subcellular targets, usually D N A , or with an established surrogate target. This group includes D N A and protein adducts and unscheduled D N A synthesis. Unlike internal dose measures, this class accounts for differences in metabolism of the chemical in question and repair mechanisms (Hulka, 1990). Biological response markers measure irreversible biological or chemical changes in cells or tissues that arise from deleterious interactions at the target site, and are thought to be a step in the pathological process toward disease. This group includes sister chromatid exchanges, chromosome aberrations, formation of micronuclei, D N A strand breaks (including the S C G E assay), and gene mutations. None of these markers are exposure specific and can be influenced by lifestyle and environmental factors(Hulka, 1990; Perera & Whyatt, 1994). Susceptibility markers measure individual differences that can influence response to carcinogens, including variability in D N A repair, micronutrient levels, inherited mutations, difference in meiabolic activity (Perera & Whyatt, 1994). The amount of information available regarding the exposure of interest, the availability of the necessary equipment and the cost of performing the analysis, the sensitivity and specificity of the biomarker desired, and the knowledge of the relationship between the marker and the development of disease all influence the decision of which type of biomarker to use for a given experiment. 1.3. Issues in biomonitoring: 1.3.1 Lymphocytes - the physiology and kinetics of sampling and monitoring: While biomarker assays may be performed in a variety of cell and tissue types, the most commonly used is peripheral blood lymphocytes (PBL) . The lymphocytes themselves are not the target tissue for carcinogens, and the associated lesions 7 detected in mature lymphocytes do not lead to cancer. However, exposures that cause a response in lymphocytes will probably also affect other tissues where malignancies can occur (Sorsa, et al., 1992; Wilkosky, 1990; Wilkosky & Griffith, 1990). The reason that levels of a given biomarker in lymphocytes is thought to have a relationship with the effects on target tissues is because lymphocytes circulate throughout the body. Blood-borne lymphocytes make up less than five percent of the total body pool, with most found in the spleen, lymph nodes, and other lymphatic tissue (Boggs & Winkelstein, 1983). These cells move freely in and out of circulation, and there is continuous movement from one tissue to another, so that a given cell is likely to have traveled through each tissue type in the body. The average time that a given lymphocyte of the recirculating pool spends in the peripheral blood has been estimated at 30 minutes, and the estimated overall recirculation time for the body is 12 hours (1986). This means that lymphocytes with DNA damage that has been induced somewhere else in the body will eventually be present in the peripheral blood. The kinetics of PBL also affects biomonitoring, as the persistence of the response to an exposure is dependent on the half life of the cells involved. There are three types of PBL: T-cells, B-cells, and null cells. T-cells make up sixty to eighty percent of circulating lymphocytes, B-cells make up five to ten percent of the lymphocyte pool, and the number of null cells in circulation is more variable (Boggs & Winkelstein, 1983). By kinetic criteria, both B- and T-lymphocytes can be long or short-lived, but T-cells have an average half-life of 3 years, and B-cells live an average of 1 to 10 days (MacKay, 1994; Sprent & Tough, 1994). This suggests that both acute and long-term or past exposures can be detected in PBL, by differentiation of the response of B- and T-lymphocytes, respectively. Some biomarker assays, such as those evaluating DNA single strand breakage, may be affected by the fact that lymphocytes can undergo programmed cell death 8 (Ashwell, 1994). This term describes the mechanics of regulated cell death, and apoptosis describes the morphology. The most often reported experimental finding with apoptosis is the fragmentation of nuclear DNA. A photograph of an apoptotic cell is shown in figure 4(d). 1.3.2 Variability of response: All evidence to date suggests that there is a high degree of variability in biomarker levels among persons with similar exposures, due to factors other than the exposure itself (Anderson, Francis, Godbert, Jenkinson, & Butterworth, 1993; Perera & Whyatt, 1994). Such variability is referred to as normal or baseline, occurring throughout the general . population and in all experimental settings. An understanding of this variability in a genetic endpoint will increase confidence in results obtained form monitoring these endpoints in a group that is environmentally or occupationally exposed. Also, knowledge of the levels of background variation allows elucidation of the appropriate study design, increasing statistical power and the sensitivity and specificity of measurements. There are two types of variability that can be observed with the use of biomarkers: biological variability and technical variability. Biological variability can itself be described both in terms of between different individuals and within a given individual. The sources of this variation are discussed in the following section. Biomarkers in any classification group can be affected by biological variability. For instance, exposure assessment based on ambient air measurements may result in misclassification because identical ambient doses may not result in identical internal doses. Differences in metabolic clearance, cellular repair, and susceptibility can also lead to misclassification by increasing the variability between 9 individuals and therefore affecting the biologically effective dose (Elinder & Vesterberg, 1985). A knowledge of the different types of variability can prevent serious exposure or disease misclassification in epidemiological studies. 1.3.3 Sources of variability and methods of minimization 1.3.3.1 Biological: The nature of the endpoint measured by a given biomarker has some influence on what factors affect biological variability. For most markers, differences in susceptibility, genetic factors, lifestyle, and environment account for the majority of this type of variability, particularly between different individuals. For example, cigarette smoking status, age, sex, and white blood cell count have been shown to account for 20-30% of the total observed interindividual variation in baseline SCE rates, and about 30% of the variation may be accounted for by genetic factors (Lazutka, Dedonyte, & Krapavickaite, 1994). Exposures to Vitamin C (Green, Lowe, Waugh, Aldridge, Cole, & Arlett, 1994), exercise (Hartmann, Plappert, Raddatz, Grunert-Fuchs, & Speit, 1994), and estrogen in the form of human reproductive hormones (Joseph-Lerner, Fejgin, Ben-Nun, Legum, & Amiel, 1993) have also been shown to increase the variability in biomarker response. Studies of environmental exposures have found ambient pollution to be associated with increases in biomarker levels (Perera & Whyatt, 1994). Intraindividual variability is influenced by a number of factors, including natural variation over time, health, lifestyle and environment changes, synergistic exposures, and other personal characteristics (Wilkosky, 1990). Levels of DNA damage may be affected in a given individual by circadian rhythms, circa-annual effects, and monthly rhythms in females (D'Souza, Thomas, & Das, 1988; Haus, Lakatua, Swoyer, & Sackett-Lundeen, 1983). 10 Any type of biological variability, with knowledge of the underlying cause and ascertainment of its presence in a potential study participant, can be minimized. This can be achieved by matching for that factor in the exposed and control groups, excluding that individual from the study, or using the subject as his/her own control. Intraindividual variability can be decreased in longitudinal study designs by informing the participants of factors that should not vary throughout the course of the study, or sampling at the same time each day, week or month. However, manipulation of the study population in this manner can decrease external validity, as results obtained may not be applicable to the general population. It may be desirable to choose a biomarker that is not as susceptible to biological variability if possible. 1.3.3.2 Technical: Sources of technical variability are both easier to define and easier to control than biological variation. Variation in each step of the experimental process can introduce technical variability, including sample collection and storage, lymphocyte isolation and culturing, slide preparation, scoring of data, analysis of data, and day to day variation if all experiments are not performed on the same day. This variation can be minimized to some extent by preparing large batches of reagents and aliquoting amounts for daily use, gaining confidence in the experimental protocol through practice, and building checks into the scoring protocol to indicate and prevent or correct bias. Technical variability can also be quantified through the use of internal standards and controls. Internal standards can include measurement of confounding substances for biomarkers of internal dose, assessment of biomarker response to a substance exhibiting a well documented and constant effect (i.e. radiation) concurrent with measurement of response to the substance of interest, and analysis 11 of a control cell population that is from one source or a non-deviating source along with test cells. This cell population could be composed of a cell line that is cultured throughout a study period or a collection of purified cells that is preserved in stasis, perhaps by freezing. 1.4 Measuring Genotoxicity 1.4.1 Lymphocyte Assays Although there are many different types of biomarkers available for use, the most common method of detecting DNA damage is with lymphocyte assays such as sister chromatid exchange, chromosome aberrations, micronuclei formation, and single cell gel electrophoresis. These assays will be explained in the following sections. 1.4.1.1 Sister Chromatid Exchange (SCE): During mitosis, through mechanisms involving DNA breakage and rejoining that are not well understood, sister chromatids can exchange seemingly identical segments of DNA, apparently without affecting cell viability or function (Wilkosky & Rynard, 1992). Elevated SCEs apparently indicate that cells have been exposed to a mutagen. Chemicals most likely to cause SCEs in vitro include alkylating agents, DNA binding agents, DNA base analogs, repair interferents, and single strand break inducers. Persistence of SCEs depends on rate of DNA repair and normal half life of the affected cell. Sister chromatid exchange appears extensively in the scientific literature as a method of measuring the effect of potentially carcinogenic exposures. 1.4.1.2 Chromosome Aberrations (CA): Chromosome aberrations are defined as gross morphological changes in chromosome structure, detected by metaphase analysis (Littlefield & Goh, 1973). These changes can include chromosome breaks, dicentrics, translocations, rings and inversions, and separation of the sister chromatids. Such alterations can be detected in patients with congenital malformations, in malignant tumour tissue, and in response to environmental and occupational exposure to certain agents with primarily clastogenic effects. 1.4.1.3 Micronucleus (MN): Micronuclei consist of small amounts of DNA that arise in the cytoplasm when chromatid/chromosomal fragments or whole chromosomes are not incorporated into daughter nuclei during mitosis (Vine, 1992). Formation of micronuclei is the result of two different mechanisms: clastogenicily or chromosome breakage and aneugenicity or improper segregation of chromosomes during mitosis (Sorsa, et al., 1992; Vine, 1992). Many agents have been shown experimentally to increase the level of micronuclei in a population of cells. 1.4.1.4 Single strand breakage assays (ssb): Single strand breakage assays measure the formation of breaks in one strand of DNA. Examples of DNA ssb assays include(Ahnstrom, 1988): a) velocity sedimentation, which takes advantage of the fact that DNA will separate by size of fragments on a sucrose gradient, b) DNA unwinding, a process using denaturation of DNA to isolate the more quickly separating sections containing breaks from unbroken segments, c) filter elution, which utilizes the ability of filters to discriminate DNA fragment sizes, d) DNA precipitation, in which DNA that is damaged 13 by strand breaking agents will not precipitate out of solution with unbroken strands, and e) microelectrophoresis of single cells. 1.4.1.4.1 Single Cell Gel Electrophoresis (SCG'E or "Comet") assay: A comprehensive review of the Comet assay is given by Fairbairn, Olive and O'Neill (Fairbairn, Olive, & O'Neill, 1995). A detailed description of the assay is given below. The comet assay is based on a simple property of DNA; nucleic acids have a negative charge. A small number of cells are embedded in an agarose gel, lysed to expose the contents of the nucleus, immersed in electrophoresis buffer, and subjected to an electric current which pulls the negatively charged DNA toward the positive electrode. Smaller fragments travel farther through the gel matrix, which allows the amount of DNA damage to be quantified if the damage causes a break in the strands of the DNA or causes unwinding from the scaffolding. The cells are stained with a fluorescent DNA-binding dye so that they can be visualized under a fluorescent microscope and analyzed. The image that is seen, if there is DNA damage, resembles a comet, with the smaller DNA fragments creating the tail. The conditions of electrophoresis greatly influence the types of damage that can be seen with this assay. The original development of the SCGE method used a neutral electrophoresis buffer (Ostling & Johanson, 1984), which, because it did not denature the DNA before electrophoresis, only allowed the detection of double-stranded DNA breaks. Singh et al (Singh, McCoy, Tice, & Schneider, 1988) modified this procedure by using a strongly alkaline electrophoresis buffer (pH 13) in order to allow the DNA to denature, thus allowing the detection of single-strand breaks and alkali-labile sites. This increases the sensitivity of the assay in detecting DNA damage because many agents produce much greater amounts of single-stranded than 14 double-stranded damage, and the alkaline conditions also degrade RNA, a common source of artifacts (Singh, et al., 1988). Several methods of quantifying the amount of damage that is present have been used. These methods include: image length or tail length (Hartmann, et al., 1994; Singh, Stephens, & Schneider, 1994), percentage of DNA in the tail (Anderson, et al., 1993), and the tail moment, or. the product of the percentage of DNA in the tail and the tail length (Olive, Banath, & Durand, 1990). 1.4.2 Comparison of lymphocyte assays - advantages of the Comet assay: The biomarker that is the focus of this thesis is the single cell gel electrophoresis (SCGE) or "Comet" assay. It is advantageous to utilize the comet assay because it is relatively quick and simple, requires a small number of cells for analysis, and can be used on both proliferating and non-proliferating cells. Other cytogenetic methods of assessing levels of DNA damage, such as sister-chromatid exchange and the micronucleus assay, are limited to proliferating cells (Hartmann, et al., 1994), and often to circulating lymphocytes. Other methods of detecting single strand breaks are tedious and require a more complicated analysis and interpretation than the SCGE method. SCGE also allows for intercellular comparisons of damage and repair, which these other ssb assays do not. The comet assay has been shown to be extremely sensitive in the detection of DNA damage induced by some sources (Anderson, et al., 1993; Betti, Davini, Giannessi, Loprieno, & Barale, 1994) and may detect levels of damage caused by low concentrations of toxic substances that other assays cannot (Hartmann, et al., 1994). It has been observed that the SCGE assay lacks specificity for radiation-induced DNA damage (Tice & Strauss, 1995 May), but it may be feasible to identify this type of DNA damage in certain subtypes of cells. The comet assay can also be used to observe the repair of DNA damage, and, as individual 15 cells can be analyzed, identify sub-populations of cells based on resistance or sensitivity to permanent damage (Olive, et al., 1990). 1.5 History of determination of background levels in genotoxicitv assays: As mentioned earlier, the development and utilization of genotoxicity assays did not always involve an assessment of background levels of the biomarker of interest. Table 1 summarizes a selection of papers focusing on baseline variation, representing an extremely small proportion of the total literature concerning assays of genotoxicity. Reference Assay used Sample size Results and Conclusions (DiGiorgio, MN Meo, Laget, Guiraud, Botta, & Dumenil, 1994) (Thierens, MN Vral, & Ridder, I 1991) 122 males and 78 females large inter-individual variability; variation factors considered were age, sex, and smoking, and only smoking could be attributed to a rise in MN frequency (25%). 5 male and 5 female Interindividual differences in micronucleus frequencies at higher doses of radiation increase the uncertainty of dose assessment (Lazutka, et SCE al., 1994) (Huber, MN Braselmann, & B auchinger, 1989) (Bender, CA, Preston, SCE Leonard, Pyatt, & Gooch, 1989) (Anderson, CA, et al., 1993) SCE 123 male and female 30 people 493 males and females, age range of 1.1 to 83.7 8 samples over 2 years from 24 males and 24 females. variation factors of age, sex, and smoking were considered and accounted for 31.3% of variability measured (26.7% from smoking) Significant interindividual variability was detected for baseline levels; the max. difference between lowest and highest yields was a factor of 15. Frequency of induced micronuclei decreased with age. Inter-sample variance was no greater between samples from the same subject than that between samples from different subjects. No effect seen with age and race for both CA and SCE; mean SCE frequency was 5% higher in fern ales „i.h£ri_majes^ ^..^^.^^^ m . „ m . Significant effects of year and season of sampling, with no pattern; no significant effects with age or sex, although SCE frequency were always higher in females than males. Table 1: Examples of publications focusing on the determination of background variability in genotoxicity assays 16 Reference Assay used Sample size Results and Conclusions (Littlefield CA & Goh, 1973) 10 men and 23 women Intraindividual variability was greater than interindividual variability, greater variability was seen in cultures from women than in cultures from men, and differences were seen in cultures obtained at different times of the (Dewdney, SCE Lovell, Jenkinson, & Anderson, 1986) 73 males and 33 females; 23 males and 9 females sampled again 6 months later Highly significant interindividual variation found, with less but still highly significant variation between replicate samples. Sex and smoking habits affected SCE frequency, with females having significantly higher than males. Most of the variation was between cells (Tucker, SCE Christensen, Strout, McGee, & Carrano, 1987) 4 females, twice a week for 8 weeks, and 2 males and 2 females for 5 days. Some variation was associated with the menstrual cycle, with peaks corresponding to ovulation and the beginning and end of menstruation. Day-to-day changes in the mean SCE were no different from those expected with random sampling. In the weekly study, there were significant differences that were not attributable to chance. (Betti, et al., SCGE, 1994) SCE (Margolin SCE & Shelby, 49 males and 51 females reexamination of other studies SCE did not reveal any significant effect of smoking, sex, or age, while SCGE DNA migration was significantly affected by smoking (more in males than in females), and no difference was seen with mage. m m _ ™ . „ Women average 0.5 SCE/cell higher than men among normal healthy adults (Fenech, MN Neville, & Rinaldi, 1994) (Betti," et al., SCGE 1994) 152 females and 113 males 49 males and 51 females Clear and consistent difference was seen in the frequency of MN in males and females (higher in females), and a greater dispersion in MN frequency in females over 40 was seen, which mQy. ^L,.^U£J9:..19,1L.PXJ.!}.^.J^„£^0JB°§2!S^ ™~™ DNA migration was significantly increased by smoking, particularly in men, and no difference was seen from age. No difference was found with SCE, suggesting a higher responsiveness of the comet assay. (Betti, Davini, Giannessi, Loprieno, & Barale, 1995) SCGE 85 males and 115 females; 60 smokers SCGE is more sensitive than SCE in revealing smoking habit effects but comet induction did not seem related to the amount of tar inhaled. Sampling time played a greater role in SCGE versus SCE. Table 1: Examples of publications focusing on the determination of background variability in genotoxicity assays (cont.) Examina t ion of the results and conclusions from these studies indicates that although much variat ion is seen both wi th in an ind iv idua l and between those in a study group, it is difficult to establish causes or reasons for this var iabi l i ty . This information also attests to the fact that variat ion exists in the response detected by genotoxici ty assays and is important to assess in some manner before using that ass on a large scale. 18 //. Objective and Specific Aims The objective of this thesis is assess the background levels of DNA damage in women using the Single Cell Gel Electrophoresis assay and interpret the meaning of the observed variability with respect to future study design and the sample size necessary to ensure statistical significance. Specific aims: 1. ) Establish a protocol for the Single cell gel electrophoresis or "Comet" assay. 2. ) Recruit a population of 15-20 female nurses unexposed to known DNA damaging agents. 3. ) Develop and administer a questionnaire containing questions concerning possible genetic and environmental exposures. 4. ) Obtain six sequential blood samples for each participant over a period of ten weeks. 5. ) Perform the comet assay on the samples and analyze. 6. ) Assess the results and perform statistical analysis. 7. ) Inform workers, publish results, and write and defend thesis. 19 ///. Materials and Methods III.I Con tac t ing and recruitment of subjects Subjects were recruited from the Vancouver H o s p i t a l - U B C site and the Occupat ional Hygiene programme in December, 1994. To meet the inc lus ion cri teria, participants had to be females who had never received chemotherapy or radiotherapy, were not occupat ional ly exposed to antineoplast ic drugs, X - r a y s , ethylene oxide , or anesthetic gases in the last five years, were not currently pregnant or breastfeeding, and were not infected with H I V or H e p B . Ini t ia l contact at the hospital was made with the Occupat ional Health Nurse, who suggested several options for recruitment, the most feasible o f which was contact ing nurses direct ly in their units. It was suggested that the psychiatry units be contacted first, as they were the least l i ke ly to experience chemical exposures. W i t h the approval of the nursing unit managers , the author spoke to nurses during several different shifts in order to provide information and answer questions. A n information sheet (see Appendix A ) and a sign-up sheet were left in the unit information book, in order to contact those who were not present and provide the required twenty-four hour period for indiv iduals to consider par t ic ipat ion. T h i s procedure was also fol lowed in two other units (wards 1A and B , and wards 2A and B ) . It was brought to the attention of the author at this time that the information sheet may be confusing, wi th respect to the reference to occupat ional exposures in the 20 exclusion criteria. A new sheet was provided to these two units which more clearly impressed the fact that these were occupational, rather than theraputic or diagnostic, exposures. Names from the list were contacted to verify interest in participation, and at a convenient time before samples were taken the individuals were given two copies of the consent form (see Appendix B), one of which was signed and taken by the author, and the other retained by the participant. At this time, dates were scheduled for six blood samples to be given, over a period of ten weeks. Sampling was scheduled on days when the participant was able to give blood between 12 noon and 3 pm, with an attempt made to sample at random intervals during the ten week period. The subjects from the occupational hygiene program were approached by the author with the second information sheet mentioned above, and consent was obtained in the same manner. These participants were scheduled for blood sampling to coincide with nurses, in no specific groupings, so that more than one blood sample could be worked on each day. All samples were scheduled between noon and three pm. III.2 Development and administration of the questionnaire The primary purpose of the questionnaire (Appendix C) was to provide information on possible exposures to anything that is known to damage DNA. Environmental factors such as occupation, diet, and tobacco use, possible genetic factors such as a history of cancer, and other factors such as age and menstrual cycle may have an effect on the levels of DNA damage observed by various different methods (Hayes, 1992). The information gathered on the questionnaires was necessary to compare the presence and levels of such factors between individuals in the study and between different samples given by a single individual. 21 The questionnaire was based on one developed by Hugh Davies of the Quintana laboratory (personal communication, 1994), and was modified to elicit information more specific to females and less geared toward employment history. A longer form was used for the first blood sample, gathering detailed information; the subsequent samples were accompanied by a shorter form which asked only those questions where the information could have potentially changed between samples. A removable coversheet contained the name of the subject and contact information, and an identification number which was used to link this information to the rest of the questionnaires. The questionnaire was not tested before administration but was critiqued on the clarity of questions and the possibility of biases by several individuals not involved in the study. Each participant recruited from the hospital was given one copy of the primary questionnaire as well as five copies of the shorter version at the time the consent form was signed. When blood samples were given, the appropriately labeled questionnaire was sent back to the author via campus mail within one to two days. The participants from the Occupational Hygiene programme were given the appropriate questionnaire whenever a blood sample was taken, and usually filled it out before leaving, or within one day. III.3 Blood sample collection Six blood samples from each participant were collected on various days over the period of January 25 to April 6, 1995. Five of the participants had venous blood samples drawn into heparinized tubes at the Medical Laboratory at the Vancouver Hospital - UBC site. The remaining 8 participants gave blood via a fingerstick by the author in the laboratory. Using a semi-automatic lancet (Becton-Dickson, 22 Rutherford, NJ), the tip of a finger was pricked, and two samples of approximately 100 u.L were drawn. All samples were drawn between 12:00 and 3:00 in the afternoon. III.4 Lymphocyte isolation For the venous samples, 100 uL whole blood was mixed with 500 uL cold Hank's buffered saline solution (HBSS, Sigma, St. Louis, MO) in a 1.5 mL microfuge tube. For the fingerstick samples, 90-100 uL of whole blood was mixed with 500 uL cold HBSS with 2% ethylenediaminetetraacetic acid (EDTA, Sigma, St. Louis, MO) to prevent coagulation. 100 uL of cold Ficoll-paque (Pharmacia, Baie D'Urfe, PQ) was then carefully layered at the bottom of each tube. The samples were then spun in a microfuge for 3 minutes at 3500 rpm. This results in a layering of the components of the blood, with the red blood cells at the bottom, followed vertically by a Ficoll layer, a thin cloudy layer containing lymphocytes, and then the HBSS in the upper layer. 100 uL of the layer containing the lymphocytes was extracted with a micropipet, removing as little of the Ficoll layer as possible. The lymphocyte solution was then washed by adding 800 uL of cold HBSS, mixed thoroughly by inverting the tube, and centrifuged at 3000 rpm for 5 min. The supernatant was then removed, and the pellet that formed was resuspended in 10 uL cold HBSS. III.4.1 Counting of cells in a solution Normally, the isolated lymphocytes are counted in order to determine what volume of cell suspension should be used. Cells are counted using a hemocytometer (Fisher Scientific, Vancouver) according to the protocol of Sigma (1992). A cell suspension is prepared in a balanced salt solution, 10 uL of which is thoroughly mixed with 10 uL of 0.4% trypan blue solution (Sigma, St. Louis, MO). With the coverslip in place, approximately 10 uL of this mixture is transferred to the 23 hemocytometer, the chambers of which fill by capillary action. Cells are counted in one or more of the etched squares until 100 cells are observed. The average cell count per square multiplied by the dilution factor multiplied by 104 yields the number of cells per mL, and this value multiplied by the original volume of fluid from which the cell sample was removed gives the total number of cells. To eliminate this step from the experimental protocol, the number of cells that could be isolated from different volumes of whole blood, using the above procedure, was identified. It was concluded that 100 pL of whole blood would yield approximately 35 000 lymphocytes, which, from previous experience, is known to be an adequate number to allow for enough cells on a slide without excessive overlapping of the images. III.5 Control cells - culturing and usage of a MOLT-4 cell line For this series of experiments, a MOLT-4 cell line was used as an internal control. An internal control series is necessary to ensure that the levels of DNA damage observed are the result of biological variability and not technical variability. This is accomplished by monitoring the response of a stable or known component of the experiment, such as a cell line, and comparing this response each time the experiment is performed. These cells were originally from American Cell Type Cultures and were obtained from Dr. NP Singh at the University of Washington in Seattle. This cell line is a stable T-cell leukemia, derived from peripheral blood, and is characteristically hypertetraploid, having a modal chromosome number of 95 (occurring in 24% of cells) and 0.8% of the cells having higher ploidy (Minowada, Ohnuma, & Moore, 1972). The original passage number for the cells is not known but at the end of the experiments the cells had undergone sixty-five additional passages. The cell line was cultured in RPMI 1640 /10% FBS (both solutions from Terry Fox Laboratory, Vancouver); every four to five days, the culture was split under 24 sterile condit ions by creating a 8:1 or 16:1 di lu t ion o f the previous culture with fresh solution. The di lu t ion was determined by a visual inspection of the culture to be split, and i f the density of cells was low, an 8:1 di lut ion was made. So that the cel ls used in every experiment were approximately the same age, a culture was split three days prior to blood sampling. Th is would ensure that the culture was dense enough for harvesting an adequate number o f ce l ls , but s t i l l in the exponential growth phase and containing as few dead or dy ing cel ls as possible. W h e n used in experiments, 1.5 m L of the cel l solution was removed, placed in a microfuge tube, and spun at 1500 rpm for 10 minutes. The resulting pellet was resuspended in 1 m L of H B S S and respun at the same speed for 5 minutes. The final pellet was resuspended in 50 u L H B S S and 10 u L was removed and counted as described previously . Whatever volume of solution that was determined to contain approximately 25 000 cells was removed and fresh cold H B S S was added to 10 u .L . III.6 Procedure for the Single ce l l gel electrophoresis (Comet -) Assay The procedure for the Comet assay used was a modif icat ion of the protocols from two sources (Singh, et a l . , 1994; T ice & Andrews, 1993). A l l of the solutions used in this assay were made in advance in large quantities and al iquoted into the required volumes so that day-to-day var ia t ion from this source is m i n i m i z e d . III.6.1 Preparation o f slides: After the lymphocytes were isolated and resuspended in 10 u.L of H B S S , the sample was mixed with 100 u L of 0.5% agarose (Seakem G o l d , F M C Bioproducts) in phosphate buffered saline ( P B S , Sigma, St. Lou i s , M O ) at 4 2 ° C . This mixture was qu ick ly pipetted onto a fully frosted microscope slide, covered with a 24 x 60 mm covers l ip (both from Fisher Scient i f ic , Vancouver , B C ) , and placed on a flat ice pack 25 for 30 seconds. The slide was allowed to warm slightly, a second layer of 100 u,L agarose was added, the coverslip was replaced, and the slide was again placed on the ice pack for 30 seconds. 111.6.2 Cell lysis: The coverslip was removed from the slides, which were then immersed in cold, freshly prepared lysing solution for one to two hours at 0°C. Lysing solution consisted of the pre-prepared base (2.5 M NaCI (Fisher Scientific, Vancouver, BC), 1% Na-lauroyl sarcosinate (Sigma, St. Louis, MO), 100 mM EDTA, and 10 mM Tris base (Boehringer-Mannheim)), with 1% Triton X-100 (peroxide-free, Boehringer-Mannheim) added before use. When the Triton X-100 was added, the solution was placed on a rocker in a 4°C refrigerator for 30 minutes. 111.6.3 DNA unwinding and electrophoresis: Slides were placed in the electrophoresis assembly near the positive end of the box and electrophoresis buffer (see below) was gently poured over the slides. After 20 minutes to allow for DNA unwinding (Tice & Andrews, 1993) the power supply was set to 24 V and the buffer level was adjusted until the amperage was approx. 300 mAmps. The slides were electrophoresed for 20 min. The electrophoresis buffer consisted of 300 mM NaOH (JT Baker, Inc), 0.1% 8-hydroxyquinoline (Sigma, St. Louis, MO), 2% dimethyl sulfoxide (DMSO, also from Sigma), and 10 mM EDTA. To make 1L of the buffer, first add 700-800 mL dH20 to a beaker and add, in order, lg 8-hydroxyquinoline, 20 mL of 500 mM stock EDTA (pH 7.5), 30 mL of 10 N NaOH, and stir. Add 20 mL of DMSO to the buffer, and adjust volume to 1 L. 26 111.6.4 Neutralization After electrophoresis, the slides were removed and immersed in 50 mL 0.4 M Tris (pH 7.5) for 1 hour. 111.6.5 Alcohol fixing After Tris treatment, the slides were dipped in 100% ethanol (JT Baker, Inc) for 15 minutes in order to fix the DNA by drying the agarose. The slide was then removed, drained, and air blown slightly to dry off the ethanol from the slide. 111.6.6 Staining of slides A coverslip was placed on each slide, and then one slide at a time was uncovered and 50 mL of YOYO-1 dye (Molecular Probes, ;1 mL/mL of distilled water) was added. The slides were then analyzed using a FITC filter (Nikon Canada). Analysis involved measuring the image length and the head diameter with the eyepiece micrometer. III..6.7 Scoring of slides All slides were scored on a Nikon LABOPHOT2-POL microscope with a fluorescence unit attached and a B2-A filter cube (Ex 450-490, DM 510, BA 520). The majority of slides were scored within one week of preparation; all slides were scored within two weeks of preparation. Each slide was stained just before placement under the microscope. After slide preparation, each slide was blinded by Hugh Davies, an independent coworker, who covered the identification mark and affixed a code number unknown to the author, who performed all scoring for this study. When all scoring was completed, the code was broken and the slides were recoded with the subject and sample identification numbers. 27 Scoring protocol is depicted in figure 2a; scoring was initiated at the position indicated, and proceeded from the bottom of the slide to the top. Generally, 50 cells could be scored from this one area, but if there was not enough cells, the slide would be moved horizontally to the left over two fields of view, and then scoring would resume from the top to bottom until 50 cells were scored. When a cell was located, the image was measured in the following manner. As depicted in figure 2b, the cell was arranged in the field of view so that the edge of the head was at the left or the beginning of the eyepiece micrometer (point A). The end of the image 'tail' is characterized by the point farthest to the right where three concentrated units of dye or "pixels" can be found on the same vertical plane (point B) (Singh, et al., 1994). The value on the micrometer at this point is the image length. The radius of the head was also measured as the value on the micrometer at the center of the image 'head' (point C), multiplied by two. If an image was longer than the micrometer, the image was shifted so that the point at 100 moved to 0, and the remainder of the image was measured as described above. The eyepiece graticule was calibrated by measuring the length of a micrometer with divisions known to be 0.01 mm in length. The graticule itself was 20 micrometer divisions or 0.20 mm in length, with each of the 100 graticule divisions measuring 0.002 mm or 2 pm . Photographs were taken of random images which were representative of the various types of cells that could be observed. The camera (Nikon N2000) was mounted on the microscope and several different shutter speeds were used. 28 III.7 Enumeration and analysis of data Data obtained from both questionnaires and scoring was entered into a Microsoft Excel™ spreadsheet. Statistical analysis was performed on a Macintosh Quadra™ 605 using an SPSS™ (SPSS Inc, Chicago IL) software package. a.) Slide Coverslip =T 1 ibegin scoring b.) Figure 2: a. ) Diagrammatic representation of the scoring protocol, showing point where scoring begins and the direction of slide movement to find new images, and b. ) Diagrammatic representation of the graticule, with image that measures a head diameter of 18 microns and an image length of 29 microns. Point A is the beginning of the image, point B is the midpoint of the head, and point C is the last point where three "pixels" of DNA line up vertically, corresponding to the end of the image. 30 IV. Results IV.1 Recruitment of subjects With the method of recruitment described in the previous section, thirteen female participants were obtained for this study. Of this number, five were recruited from the hospital and eight from contacts within the Occupational Hygiene programme. It is hard to determine the refusal rate for the method of recruiting volunteers used in this study. It can be noted that, with respect to the nurses, while the author did experience much interest in the results of this study, there was little interest in actual participation. Thirty percent of the individuals contacted directly in the wards who met the exclusion criteria agreed to participate, and one individual who initially agreed later refused. Two of the five nurses who agreed to participate did so after reading about the study in the information books. It can be assumed that this is a very low percentage of the actual number of individuals who work in these units. Ninety percent of those approached in the occupational hygiene programme agreed to participate. 31 I V . 2 B l o o d Sampl ing Obta in ing six blood samples from each ind iv idua l over the ten week period allotted was very successful. E leven individuals gave the full number o f samples and, for various reasons, one ind iv idua l contributed four samples and another gave only three. In total, 73 blood samples were collected. A number o f technical diff icult ies resulted in a loss of samples before any information could be obtained from them. Upon complet ion of a l l possible scor ing, usable data was acquired from 56 of the blood samples given, or 77 percent. This data is presented in Appendix D . IV.3 Photographic example of the images observed Figure 3 is a photographic example of the images observed in these experiments. The head diameter and image length are given in u m . Figure 3(a and b): Example of images observed - a.) undamaged cell, 11 head diameter, b.) slightly damaged cell, 11 nm head diameter and 30 nm image length. 33 Figure 3 (c and d): Example of images observed - c.) fairly damaged cell 12 nm head diameter and 95 um image length, and d.) apoptotic cell, 4 um head diameter and 180 um image length. 34 IV.4 Questionnaires Questionnaires were completed for all blood samples taken, and although consent to recontact was given in all cases it proved to be unnecessary. For each question asked, possible answers were coded in a numerical format for ease of analysis. Upon receipt of the final questionnaire for each individual, all data was entered and coded. After all of the data was compared, some questions were recoded to ease analysis. For example, the amount of vitamin C used was changed from an actual amount taken in milligrams to an assignment into one of three groups corresponding to high, medium, and low usage, determined by the data itself. A summary of the questionnaire data used for analysis is given in Appendix E. Many differences existed both between the individuals in the study and between different samples given by the same person. The age of participants ranged from 23-57 years. Seventeen percent of samples were taken when the individual sampled was inflicted with a cold or other virus, and 75% had experienced a cold or other virus within a month of the sampling date. Two of the participants, or 15%, had been diagnosed with cancer in the past, affecting 19% of the samples. Twenty-three percent of samples were drawn from an individual vaccinated in the last six months, and 54% were vaccinated in the last two years. Sixty-three percent of the samples were drawn within one year of a diagnostic X-ray exposure, and the number of X-rays experienced in the last ten years ranged from 0 to 30. The majority of these X-rays were dental. Thirty-one percent of the thirteen study participants ever used oral contraceptives, with current usage by three. One individual was using a Norplant contraceptive device for the duration of the study. Sixty-nine percent of the participants experienced regular menstrual cycles (28 days ± 1) and the days since 35 the start of the last menstrual cycle ranged from 2 to 175, with one participant post-menopausal. Three of the participants, or 23%, were smokers, consuming less than one package of cigarettes per day. For only 25% of the samples the individual exercised vigorously 6 to 48 hours prior to the sample being drawn. For 42% of the samples a stressful event occurred prior to sampling. Over half of the individuals, or 54%, reported no vitamin C use, and 75% reported no vitamin E use. Of the half which did use vitamin C, 32% reported low usage, and 62% used relatively high amounts of vitamin C on a regular basis. Information on diet also showed great variation between study individuals. None were strict vegetarians, although servings of vegetables per day ranged from 2 to 10, and the amount of red meat intake ranged from none to relatively high amounts. There were also significant difference in the amount of cured and grilled meat in the diet, as well as the amount of caffienated beverages, particularly coffee, consumed per day. Observed differences in exposures or circumstances that can change over time, such as the presence or absence of viral infections and menstrual period, are indicative of the potentially significant events that can occur between sampling dates. IV.5 Data Analysis IV.5.1 Presentation of results The raw data is presented in Appendix F. All scorable slides for both blood samples and MOLT-4 samples are included. A histogram created from all data for each subject, and the results of a statistical analysis of this data, is presented in figure 4 (a-m). 36 I V . 5 . 2 Compar ison of means when fifty versus one hundred cells are scored A s mentioned in the materials and methods, two slides or replicates were prepared for each b lood sample taken. However , only thir ty-two o f the f i f ty-s ix samples, or 57 percent, produced two slides on which the cel ls were numerous and distinct enough to score. In order to determine how to treat this data, a two-tailed t-test was performed for each set of replicates. The results o f these t-tests provided informat ion on the variat ion introduced dur ing slide preparation, and the effect o f pool ing the data from the two slides scored when possible. The t-test results are listed in Appendix G . The t-tests suggest that the data from the two slides scored can be pooled for further analysis without in t roducing s igni f icant bias to the interpretat ion to that analysis . I V . 5 . 3 Investigating the relationship between assay results and date o f sample It was noted early on in the scoring of samples that the overal l image length measurement was decreasing, par t icular ly since the first week of sampl ing . In order to investigate this observation, the mean image length for each b lood sample and the M O L T - 4 slides was plotted versus the date of sample (figure 5a). If no systematic effects were present, no relationship should exist, but as can be seen by the plot, the mean decreases greatly between the first and last of the sampl ing days. The author felt that the large in i t ia l measurements were due to inexperience in sample handl ing , and i f included in statistical and questionnaire analysis cou ld potentially bias results. If the data from the first day of sampling is removed from the analysis (figure 5b), the relationship decreases. If the f o l l o w i n g two sampl ing days are also removed, the relationship decreases again (figure 5c). 37 Basic Statistics end histogram for al l data from al l subjects: Count Midpoint 1682 13.00 740 23.00 416 33.00 241 43.00 172 53.00 110 63.00 110 73.00 153 83.00 122 93.00 88 103.00 47 113.00 21 123.00 16 133.00 14 143.00 1 153.00 2 163.00 6 173.00 3 183.00 0 193.00 0 200.00 Ona symbol equals approximately 40.00 occurrences _i 400 800 1200 Histogram frequency 1600 2000 Basic Statistics "eon 33.829 Std err .476 Median 21.000 Mode 11.000 Std dew 29.900 variance 894.004 Kurtosis 8.004 S E Kurt .073 Skewness 2.056 S E Skew .039 Range 397.000 Minimum 8.000 Maximum 405.000 Sum 133624.500 Statistics and histogram for subject 566 Count Midpoint 211 15.00 31 25.00 17 35.00 9 45.00 6 55.00 4 65.00 7 75.00 5 85.00 3 95.00 4 105.00 1 115.00 1 125.00 1 135.00 0 145.00 0 155.00 0 165.00 0 175.00 0 185.00 0 195.00 One symbol equals approximately 8.00 occurrences r 80 160 240 Histogram frequency 320 400 Mean Mode Kurtosis S E Skeu Maximum 22.933 11.000 6.263 . 141 138.000 Std err Std dew S E Kurt Range Sum 1 . 3 0 6 2 2 . 6 2 0 . 2 3 1 1 2 8 . 0 0 0 6 8 9 5 . 0 0 0 Med i an Oar Iance Skewness Mini mum 13.000 511.662 2.518 10.000 Figure 4: a.) Basic statistics and histogram for all image length measurements from all subjects, and b.) statistics and histogram for all image length measurements for subject 566. 38 Statistics and histogram for subject 574 Count Midpoint 129 15.00 59 25.00 16 35.00 9 45.00 6 55.00 10 65.00 2 75.00 7 ' 85.00 2 95.00 3 105.00 4 115.00 0 125.00 3 135.00 0 145.00 0 155.00 0 165.00 0 175.00 0 185.00 0 195.00 One symbol equals approximately 4.00 occurrences Mean Mode Kur tosIs S E Skeu Maximum 28.588 11.000 4.506 . 154 131.000 40 80 ' 120 Histogram frequency 160 200 Std err 1.604 Median 19.000 Std dev 25.355 Variance 642.862 S E Kurt .307 Skeuiness 2.222 Range - 121.000 ' Minimum 10.000 Sura 7147.000 Statistics and histogram for subject 655 Count Midpoint 89 15.00 21 25.00 14 33.00 13 45.00 12 55.00 It 65.00 4 75.00 9 85.00 11 95.00 5 103.00 3 115.00 5 '125.00 2 135.00 1 145.00 0 155.00 0 165.00 0 175.00 0 185.00 0 195.00 One symbol equals approximately 2.00 occurrences 20 40 60 Histogram frequency 80 100 Mean Mode KurtosIs S E Skeu Maximum 39.885 11.000 .098 .172 145.000 Std err Std dev S E Kurt Range Sum 2.409 34.064 .342 135.000 7977.000 Median UarIance Skewness Minimum 22.000 1160.343 1.112 10.000 Figure 4: c.) Basic statistics and histogram for all image length measurements for subject 574, and d.) statistics and histogram for all image length measurements for subject 655 39 Statistics and histogram for subject 681 Count Midpoint One symbol equals approximately 2.00 occurrences 83 27 8 5 4 10 4 7 1 1 0 0 0 0 0 0 0 0 0 Mean Mode Kurtosis S E Skeo Maximum 15.00 . 25.00 35.00 45.00 55.00 65.00 75.00 85.00 95.00 105.00 115.00 125.00 135.00 145.00 155.00 165.00 175.00 185.00 1S5.00 27.733 11.000 1.161 . 198 101.000 20 40 60 Histogram frequency 80 100 Std err Std dew S E Kurt Range Sum 1.814 22.222 . .394 91.000 4160.000 Med i an UarIance Skewness Mini mum 18.000 493.821 1.526 10.000 Statistics and histogram for subject 703 Count Midpoint 128 15.00 95 25.00 52 35.00 21 45.00 19 55.00 15 65.00 13 75.00 22 85.00 13 95.00 10 105.00 5 115.00 2 125.00 2 135.00 2 145.00 0 155.00 0 165.00 0 175.00 0 185.00 0 195.00 One symbol equals approximately 4.00 occurrences Mean Mode Kurtosis S E Skew Max I mum 38.993 12.000 31.489 . 122 405.000 40 80 120 Histogram frequency 160 200 Std err Std dew S E Kurt Range Sua 1.721 • 34.423 .243 395.000 15597.000 Med I an UarIance Skewness MI n i mum 27.000 1184.915 3.788 10.000 Figure 4: e.) Basic statistics and histogram for all image length measurements for subject 681, and f.) statistics and histogram for all image length measurements for subject 703. 40 Statistics and histogram for subject 711 Count Midpoint 230 15.00 94 25.00 42 33.00 31 45.00 12 55.00 7 • 65.00 11 75.00 15 85.00 22 95.00 18 105.00 11 115.00 1 125.00 1 135.00 , 3 145.00 0 155.00 0 165.00 2 175.00 0 185.00 0 195.00 Mean Mode Kurtosis S E Skeo Max i mum 34.659 11.000 1.999 . 109 175.000 One symbol equals approximately 8.00 occurrences 80 160' 240 Histogram frequency 320 400 Std err 1.418 Median 20.000 Std deu 31.714 Uariance 1005.776 S E Kurt .218 Skeuiness 1.655 Range 165.000 Minimum 10.000 Sum 17329.500 Statistics and histogram for subject 715 Count Midpoint 234 15.00 84 25.00 49 33.00 23 45.00 18 55.00 5 65.00 5 75.00 11 85.00 7 95.00 5 105.00 4 115.00 0 125.00 3 135.00 2 145.00 0 155.00 0 165.00 0 175.00 0 185.00 0 195.00 Mean Mode Kurtosis S E Skeo Maximum 28.216 12.000 5.225 .115 143.000 One symbol equals approximately 8.00 occurrences 80 160 240 Histogram frequency 320 400 Std err 1.145 Median Std deu 24.284 Uariance S E Kurt .230 Skeuiness Range 133.000 Minimum Sum 12697.000 18.000 589.711 2.261 10.000 Figure 4: g.) Basic statistics and histogram for all image length measurements for subject 711, and h.) statistics and histogram for all image length measurements for subject 715. 41 Statistics and histogram for subject 728 Count Midpoint 108 16.00 42 26.00 14 36.00 13 46.00 5 56.00 0 65.00 2 76.00 4 86.00 5 95.00 3 105.00 3 116.00 0 126.00 0 136.00 0 146.00 0 155.00 0 166.00 1 176.00 0 186.00 0 196.00 One symbol equals approximately 4.00 occurrences Mean Mode Kurtosis S E Skew Max i mum 28.845 14.000 7.955 . 172 180.000 40 80 120 Histogram frequency 160 200 Std err Std deu S E Kurt Range Sum 1.S31 25.894 .342 169.000 5769.000 Median far i ance Skewness Mini mum 19.000 670.493 2.629 11.000 Statistics and histogram for subject 812 Count Midpoint 168 15.00 48 25.00 22 35.00 7 45.00 12 55.00 8 65.00 9 75.00 16 85.00 6 95.00 3 105.00 1 115.00 0 125.00 0 135.00 0 145.00 0 155.00 0 165.00 0 175.00 0 185.00 0 195.00 One symbol equals approximately 4.00 occurrences 40 80 120 Histogram frequency 160 200 Mean 28.433 Std err 1.437 Median 16.000 Mode 11.000 Std deu 24.885 Uariance 619.263 Kurtosis 1.281 S E Kurt .281 Skewness 1.574 S E Skeo .141 Range 103.000 Minimum 10.000 Maximum 113.000 Sum e530.000 Figure 4: i.) Bas ic statistics and histogram for al l image length measurements for subject 728, and j . ) statistics and histogram for al l image length measurements for subject 812. 42 Statistics and histogram for subject 851 Count Midpoint 103 15.00 51 25.00 37 33.00 20 45.00 7 55.00 5 65.00 10 75.00 12 85.00 2 95.00 0 105.00 1 115.00 1 125.00 0 135.00 1 145.00 0 155.00 0 165.00 0 173.00 0 185.00 0 195.00 One symbol equals approximately 4.00 occurrences 40 80 120 Histogram frequency 160 200 Mean 31 100 Std err 1 462 Median 22.500 Mode 15 000 Std dev 23 114 far i ance 534.235 Kur tos i s 3 034 S E Kurt 307 Skeuiness 1.73? S E Skew 154 Range 130 000 Mini mum 10.000 Max i mum 140 000 Sum 7775 000 Statistics and histogram for subject 906 One symbol equals approximately 1.50 occurrences Count Midpoint 69 13.00 10 23.00 7 33.00 5 43.00 2 53.00 3 63.00 1 73.00 1 83.00 0 93.00 1 103.00 0 113.00 1 123.00 0 133.00 0 143.00 0 153.00 0 163.00 0 173.00 0 183.00 0 193.00 0 200.00 Mean Mode KurtosIs S E Skeo Maximum 15 30 45 HIs togram frequency 60 75 22.070 11.000 8.561 .241 125.000 Std err Std deu S E Kurt Range Sum 2.052 - 20.523 .478 117.000 2207.000 Median Uariance Skeuiness Mini mum 13.000 421.197 2.726 8.000 Figure 4: k.) Basic statistics and histogram for all image length measurements for subject 851, and 1.) statistics and histogram for all image length measurements for subject 906 43 Statistics and histogram for subject 933 Count Midpoint 72 15.00 25 25.00 4 35.00 9 45.00 2 55.00 3 65.00 5 75.00 10 85.00 7 95.00 4 105.00 3 115.00 3 125.00 2 135.00 0 145.00 0 155.00 0 165.00 1 175.00 0 185.00 0 195.00 One symbol equals approximately 1.50 occurrences Mean Mode Kurtosis S E Skeur Max 1 mum 38.467 11.000 .691 . 198 170.000 15 30 45-Histogram frequency 60 75 Std err Std dew S E Kurt Range Sum 2.912 35.667 .394 160.000 5770.000 Median War Iance Skeiuness Mini mum 21.000 1272.130 1.300 10.000 Statistics and histogram for subject 942 Count Midpoint 174 15.00 65 25.00 32 35.00 19 45.00 6 55.00 9 65.00 7 75.00 11 85.00 6 95.00 11 105.00 3 115.00 0 125.00 0 135.00 2 145.00 1 155.00 1 165.00 2 175.00 1 185.00 0 195.00 One symbol equals approximately 4.00 occurrences Mean Mode Kurtosis S E Skeo Maximum 32.380 12.000 5.402 . 130 180.000 40 80 120 Histogram frequency 160 200 Std err Std dew S E Kurt Range Sua 1.654 30.939 .260 170.000 11333.000 Median Uar i ance Skemness Minimum 20.000 957.233 2.261 10.000 Figure 4: m.) Basic statistics and histogram for al l image length measurements for subject 938 and n.) statistics and histogram for all image length measurements for subject 942 samp Ii ng day 115 cases p l o t t e d . Regress ion s t a t i s t i c s of MEAN on DflTEHO: C o r r e l a t i o n - .46144 R Squared .21293 S . E . o f E s t 28.56345 S i g . .0000 I n t e r c e p t s . E . > 104.97009C 5.20930> S lopeCS.E .> -3.05753< .55300> Figure 5(a.): Scatter plot depicting the relationship between the mean image length for both lymphocytes and MOLT-4 cells in u,m and the sampling day for the slide. Comparison when f i r s t sampling day excluded ri 1 1 1 1 1 1 1 1 1 1 \ 180-sampling day 107 cases p l o t t e d . Regression s t a t i s t i c s of MERN on DRTENO: C o r r e l a t i o n - .33677 R Squared .11341 S . E . of E s t 27.50336 S i g . .0004 I n t e r c e p U S . E . > 94.45247< 5.67919) S l o p e C S . E . ) - 2 . 1 3 2 1 K .58176) Figure 5 (b.): T h e relat ionship between the mean image length for both lymphocytes and M O L T - 4 cel ls in u.m and the sampling day for the sl ide after the samples from the first sampl ing day are removed. samp I ing day 94 cases p l o t t e d . Regression s t a t i s t i c s of MERN on DRTENO: C o r r e l a t i o n -.12698 R Squared .01612 S . E . o f E s t 25.65819 S i g . .2226 I n t e r c e p t s . E . > 77.97603 < 6.45724) S l o p e < S . E . ) - . 7 6 4 7 K .62279) Figure 5 ( c ) : The relationship between the mean image length for both lymphocytes and M O L T-4 cells in p m and the sampling day for the slide that exists after the first three sampling days are removed. 47 IV.5.4 Use o f different metrics for analysis Several different metrics were used in this analysis to determine i f any provided clearer or more interpretable information. The metrics used were mean image length, median image length, mode image length, percentage o f images over 72 u.m i n length, percentage of images over 134 u m in length, and percentage of images over 194 u m in length. The most commonly used metric is the mean, but this value may not give an accurate summarizat ion of the data because it is influenced by outliers. The median and mode are not affected by outliers and may be a better choice i f outliers are of concern. Because different influences or factors may cause different types of damage to cel ls , the percentage o f images that equal or exceed certain values was also u t i l i zed . In this case, the values correspond to the mean of al l image length measurements from lymphocyte samples (72 u.m), the mean plus one standard deviat ion (134 um) , and the mean plus two standard deviations (194 u.m). A graphical representation of these metrics for each sample, grouped by subject, is given in figures 6 through 11. 4 8 y. do" c <T> O S T o' 05 o 3 O 3 3 o W 3 3 o w o c >< d c r t—i. o Mean image length in um ro 4^ CO o K3 o o o o o o O O 566 574 :6 5 5 681 703 71 1 c | 715 ° 728 81 2 '8 5 1 906 938 942 • • -• • • -a (D 3 3 (Q <D CD o o o a. 03 0) 3 •a CD (A c c^r *<D o (£2 O c •a (A > O • • CO CO CO CO CO 03 03 03 03 03 3 3 3 3 3 • o - o T J X3 T3 CD ro CD CD CD CJ> en CO ro in 03 CD 31 m c <T> Q —i T3 er o' SB T3 O 3 O 3 sr 3 o.. S 3 0! o 3 00 o o c o. 3 .cr a o 566 574 655 681 703 71 1 in c 8 7 1 5 ° 728 812 851 906 938 942 Median image length, in um ro co oo o ro o o o o o o -o-• -•-a • — • • oo—» . — • — • oa—• • —D-fD Q. 0)' 3 o> <D (Q O ID o •r C7 o o a. in 3 CD (A C CT o «-* C Q o c ~a cn > • O • • • CO CO CO CO CO CO 03 03 03 03 03 3 3 3 3 3 3 "O X) T3 T3 CD CD CD CD CD CD CO cn W CM CO LO CD QJ 03 03 03 03 03 C L Q . C L C L Q_ O -E E E E E E ro ro ro cc ro ro CO CO 00 CO CO CO • • • o < (fi Q . 13 O o E 0) Q . E CO (fi T3 O O . Q . C O (0 <u U) c <D cn co E o T3 O •LO "O D L « > I r- H h H h 2176 8S6 906 (-9 8 2 L 8 82/ 9 L Z S I- VI £01 189 999 t>Z9 999 O O O O O O O O O O o O C O C D T l - C M O C O C D T f C M C\J T- 1- I- T- T-uin uj 'i|i6u3| OBBLUI epo|/\| 3 o C3 4) 60 C 60 E o -a o E c o c a , o IS D. S3 i-o oo 3 60 51 era' C m O T 3 O o •a 3 O 3 3 * cn T3 f t »-t o f t 3 w era o 3 CO CJQ f t o < o N5 f t 3 OQ f t SB O 3 " Percent of cells over 72 um ->• PO co 4^ cn a> -v i o o o o o o o o 566 574 655 681 703 71 1 CO c f 7 1 5 728 812 851 906 938 942 -a • - a - O -• o-»-<•—O •—« >-r> • . • T3 CD O CD »—*• 0) IQ CD O -* O CD wT o < CD -t 1^ ro ubj c 3 CD O 5' (Q - i o O c (Q <-+ CO 3* c o-CD o IT a; o o Q. CO 03 3 "O CD c a. C f t ' o > O • • • CO CO CO CO CO 03 03 03 03 03 03 3 3 3 3 3 . 3 X J X J X J -a X3 X J CD CD CD CD CD CD co cn CO ro 52 21 c r w <— T3 C9 CD 3 O 3 CD o CD S3 rs 3 0 9 CD 1/1 o < CD CO 4^ CD 3 era CD W O s r c CL Percent of cells over 134 um o c n o c n o c n o c n o c n H IT 4 — I — I — h 566 574 655 681 703 71 1 CO c f 7 1 5 728 i -• 812 851 906 938 942 . - a >—a-> • O • • • CO CO CO CO CO CO CO W CO CO CO CO 3 3 3 3 3 3 X 5 X) T 3 T 3 T 3 •a CD CD CD CD CD CD a> en 4^ CO ro —*• 13 CD —r O CD 3 j-t Q) C Q CD O CD W o < CD 5' _ L co w c cr c Q - 3 o CD 3 O W 3" CD O =7 O O Q . CO fi) 3 •D CD 53 31 ere' c Percent of cells over 194 um c r cs OS T3 fl> 3 O 3 3* O T 3 CD *n O o 3 ere o 3 ere o O < 4^  3 cs 3 ere m CO o tr O-_ L _ ! . - I . | \ ) o r o * > - c r > 0 3 o r o 4 ^ c o c o o H 1 1 1 1 1—H 1 1 1 1 5 6 6 5 7 4 6 5 5 -a • • 8 5 1 . •—>: 9 0 6 9 3 8 9 4 2 r> • O ' • • • CO CO CO CO CO CO 03 03 03 03 03 03 3 3 3 3 3 3 ~o T> T3 •o •o CD CD CD CD CD CD CO cn 4> CO ro -o CD o CD 3 #— 0) IQ CD O CD CO O < CD CO CO 4^ c a- c Q - 3 o (Q CD 3 O co - x CD 0) o o o o_ CO 0) 3 •o CD 54 IV.5.5 Assessment of the effectiveness of M O L T - 4 cells as an internal standard: In order for the M O L T - 4 cel l l ine to be considered useful as an internal standard it has to possess several characteristics. The cel l l ine must respond to the assay in a manner s imilar to that o f the cel l type or tissue monitored, and must remain stable over an extended period of l ime so that the technical var iab i l i ty of the assay is accurately assessed and not augmented by var iabi l i ty in the ce l l l ine itself. A quantitative assessment o f the relat ionship between the measured response of the ce l l l ine by this assay and the measured response of the tissue sample cells was not performed. A qualitative assessment was accomplished by comparison o f the mean b lood sample image length with the mean image length of the corresponding M O L T - 4 slide; this is presented in figure 12(a). This plot shows a slight but s ta t i s t ica l ly ins ign i f i can t re la t ionship between these two factors. W h e n slides made with M O L T - 4 cells were observed under the microscope, two subsets of cells could be seen; one set was very s imilar in size and appearance to lymphocytes , and the other group was noticeably larger in the head region. A s noted in the introduction, the M O L T - 4 cel l l ine is a stable hypertetraploid T - c e l l leukemia, wi th a modal chromosome number of 95 occurring in 24 percent of cel ls . It was decided that the larger cells contained in all l ike l ihood four times the amount o f D N A found in a ce l l from the blood samples, and therefore, possessing the potential for a greater exhib i t ion of D N A damage, should be excluded from analysis. A comparison plot of the mean blood sample image length with the mean image length of the corresponding M O L T - 4 sl ide, with large cel ls removed from analysis, is presented in figure 12(b). Th i s plot also indicates a slight relationship between the response of the ce l l l ine as detected by the Comet assay and that of the lymphocytes from the b lood samples. 55 a . ) Comparing blood sample t image length with corresponding MOLT-M a n i m a 9 I e n 9 t h i n u m 160 140+ 120f 100-H 100 120 140 Mean MOLT image length in um 3? cases p l o t t e d . Regression s t a t i s t i c s o f MERN on MOLT C o r r e l a t i o n .24465 R Squared .05985 S . E . of E s t 20.21062 S i a 1445 I n t e r c e p t s . E . ) 43.28115< 13.07405) S lope<S.E. > . 19509< ."13069) Figure 12(a.) Scatter plot with regression line for mean image length in fim and the corresponding MOLT-4 group slide. 56 b. ) Comparison when Iarge MOLT eel Is excIuded 160+ 140+ 120+ 100+ Mean MOLT image length in um 37 cases p l o t i e d . Regression s t a t i s t i c s of MERN on LRGMERN: C o r r e l a t i o n v 12948 R Squared .01676 S . E . of Es t 20.66857 S i g . .4450 I n t e r c e p t s . E . ) 54.48260< 10.49337) S l o p e C S . E . ) .17240< .22317) Figure 12-(b.) Scatter plot with regression l ine for mean image length in u.m and the i corresponding M O L T-4 group slide with large cells excluded. 57 A n assessment of the stability of the cel l l ine over time was a l lowed by the calcula t ion o f coefficients o f variat ion for each sampling day, as we l l as an overal l C V for al l M O L T - 4 slides produced, and with the execution of an A N O V A to determine the percentage of var ia t ion attributed to samples from a given day, or wi th in sample, and samples from different days, or between samples. The results o f this statistical analysis are presented in table 2. a.) M o l t - 4 ID Date of Sample Coeff ic ient of variat ion (%) A , B 2 5 - J a n 3.01 K , L 8 -Feb ' " ' M , N , 0 1 0 - F e b 23772 " R, S 2 3 - F e b _ „ . . J 1 I I _ T, U 2 4 - F e b 11.22 W , X . 7 - M a r 21.07 _ _ ~ A A , Y , Z 8 - M a r G G . H H 2 2 j : M a r _ m 3535 II, JJ 23 - M a r 13" 4 3 " Z Z Z I M M , N N 30 -Mar 6.54 average coefficient o f var ia t ion for •eplicale s l ides: T5?72 Overa l l coefficient of variat ion for all M O L T - 4 slides produced: 27.63 b.) Source o f v a r i a t i o n sum of degrees of m e a n F, s i gn i f i cance % of total s q u a r e s f r e e d o m s q u a r e of F v a r i a b i l i t y B e t w e e n (cel ls from d i f f e r e n t W i t h i n (cel ls f rom same day) T o t a l 18460.801 14 1318.529 4.979, 0.004 85 3177.814 12 264.818 15 21638.614 ""83T254 100 Table 2: a.) Coefficient of variation and b.) Ana lys i s -of -Var iance calculations used to assess the stability of the M O L T - 4 cel l line. 58 The overall coefficient of variation calculated for the MOLT-4 slides indicates that there is approximately 28% variation between the means for the MOLT-4 slides over the course of the study. The average replicate CV of 15.72% suggests that some of this variation may be biological in nature; however, the component of variation that is attributable to biological differences and the proportion resulting from technical variation over the time course of the study cannot be elucidated. The ANOVA results indicate that the variability between cells from different days is significantly higher than the variability between cells from the same day, as would be expected. IV.5.6 Assessment of the technical, inter- and intra-individual variability In order to make a crude comparison of the information obtained from each slide, the coefficient of variation was calculated for each of the six test metrics. One CV value was calculated for each set of replicate slides when a sample produced two scorable slides. A value was also obtained for each of the thirteen subjects, using all samples available (three to six values, depending on the subject). A comparison between subjects was made by calculating a coefficient of variation value for the mean image length of the first, second, and third samples from each subject, as well as a value using the average mean image length of all days for each subject. The results of these calculations are given in Table 3. 59 Coefficient of variation calculation for each replicate pair, for each test metric mean median mode % of % of % of R e p l i c a t t image image image images im ages images ID length length length over 72 m m over 134 mm over 194 mm 566/1 27.66 38.04 30.50 40.02 66.99 60.61 566/2 60.92 44.19 12.86 141.42 141.42 141.42 566/3 71.22 ""51.74" 0.00 128.56 141.42 574/3 4.88 7.29 25.38 9.43 9.43 _ 20.20 574/4 2.64 4 . 2 9 ™ ' 24.38 35.36 "'"o7oo"'- 141.42 655/4 1.14 15.71 0.00 16.97 28.28 141.42 681/1 4.07 2.77 0.00 7.86 22.33 681/2 6.67 15.7*1 ""'"d'oo" 2438 : 5439 n / a 703/2 3.87 4.29 """45.00 9.87 12.86 47.14 703/3 5.17""" 5T01 45.25 5.89 """"4.56 1577 r 703/4 5.11 41.39 " 0.00 23.57 47.14 n / a 711/1 24.75 12.36 j i i r 44.19 """"" ~ 4 . 0 8 " 0.00 711/2 15.56 20.48 _ 47.14™" 32.64 42.43 711/3 9.08 5.89 3.45 54.39 8732 28.28 711/4 ~~72 5.66 0.00 10.88 20.20 n / a 715/2 35J03 _39.77~" 28.28" 58.64 80.81 " 715/3 3.14 T 7 7 ™ " 676o "o76o'""" " "'""T'5'71 47,14""" 715/4 4.26 4.7 f" "6. T5~"~ 17.68 28.28 n / a 715/6 48~>0 "~41.59~~ 67oo" " " " 117785 ""l 06.07 84.85 728/1 8.23 2.30 6.58 14.35 6.43 38.57 728/2 6.94 17.88 11.47 7.71 22.33 0.00 728/3 2T70 TT™47 ''10.10 0.00 '"15.71™ 812/3 7.91 11.38 10.10 20.20 20.20 n / a 812/5 37.28 67oo 67764' 86781" " 851/1 _ l 3 . j £ 9 _ _ IJ2-AL..Z 15.71 123.74 141.42 n / a 851/2 13T6'9 " .__ "™4478"4"" """'TT.47 '"'"54739 T4L42 906/3 """'5T89"™ l'5'.7'i' l".5"9" 25"."3"8"" 94.28 938/1 3.51 1.36 1.47 8.95 _ 4 1 . 2 5 _ 9A3 938/3 23.90°"°"""" ~ 3 4 . 0 3 - 3.07 38.57 ..... ^ _ 47.14 942/2 27.94 39.70 4.56 66.55 66.00 35.36 942/3 1.93 10.10_ 11.79 "28.28 20.20 942/4 0.32 4.56 6.15 6.15 0.00 28.28 Average coefficient of variation for all replicate pairs, or each test metric 16.30 17.07 16.65 35.48 45.51 63.85 Table 3(a.): Coefficient of variation calculation to compare replicate slides, for human lymphocytes, for each test metric. 60 Coefficient of variation calculation for each subject, for each test metric S u b j e c t ID mean image length median image length mode image length % of images over 72 m m % of images over 134 mm % of images over 194 mm 566 25.84 6.28 4.65 58.25 124.10 81.65 574 29.88 29.43 35.29 67.84 70.31 58.08 655 38.74 64.57 116.07 46.46 65.54 72.11 681 23.86 1.94 11.79 92.93 103.71 141.42 703 32.90 """' W.54~' " 57.90 48748" 59753" 165.27 711 33.73 42.99 39.20 51.66 41.66 99.86 715 28.76 39.00 37.44 75.98 49.21 70.71 728 36.82 30.20 12.06 87.30 103.62 93.26 812 851 906 19.70 7.24 "4.37 26.98 9.69 30.30 " 13.86 *8J. 6 8 ™" 6.15 33.67 35.36" " 63.51 0.00 12.86* 141.42 141.42 60.61 938 942 15.24 207*32 32.47 ~ ™30.34"" 4.95 Tf.83 16.67 38769 26.19 35.36 35.25 30.43 Average coefficient of variation foi all subjects, for each test metric: 24.42 28.83 33.30 51.96 58.12 91.65 Table 3(b.): Coefficient of variation calculated to compare within each subject, between days, for each of the six test metrics. Coefficient of variation calculated for correspondin g samples from each subject Coefficient of Variation (%) Using first sample from each subject 29.1 Using second sample from each subject 33.6 Using third sample, from each subject 24.6°' Using the average of all days from each subject 20.9 Table 3(c): Coefficient of variation for comparing between subjects The average CV for the mean image length of the replicate slides was calculated as 16.3%, with a range of values from 0.32% to 71.2%. This suggests that although the average difference between two replicate slides from the same sample can be very low, the mean image length measured can also be quite different between two slides that should have a relatively similar population of cells. These 61 values give some idea o f technical var iab i l i ty from the replicate-to-replicate source, but g ive no information on other types o f technical va r iab i l i ty , such as day-to-day. The wi th in subject, between days C V calculations resulted in an average o f 24.42% for the mean image length, with a range of 4.37% to 38.74%. This suggests that some indiv iduals are more variable over time with respect to the response measured by the comet assay than others in the study. It also suggests that, because the average value is greater than that calculated for the replicate slides, some o f the var ia t ion seen wi th in indiv iduals may be attributable to b io log ica l effects. Th i s suggestion is cautious, however, because of the other types o f technical var iab i l i ty that could not be assessed by the information gained from this study, and which could make up an unknown portion of the var iabi l i ty seen wi th in a given subject over t i m e . The C V calculat ions for the between subject values, using a certain sample from each subject, indicate that the difference between the mean image length o f study subjects at any given time point may remain relat ively constant, ranging only from 24.6% to 33.6%. The C V calculated using the average of all days for each subject was lower, at 20.9%, as would be expected as extremes within an individual are muted by the calcula t ion of an average, and the variance between subjects would decrease as we l l . A comparison of the C V values calculated for each of the different test metrics shows an increase in variat ion from the mean image length to the percentage o f images over 192 u m in length. This should be expected, as the metrics other than the mean are more influenced by out ly ing or extreme data points. IV .5 .7 Comparisons of the blood sample and questionnaire data A comparison of the sample data and the questionnaire data was necessary in order to elucidate whether the observed b io logica l variat ion was the result o f known 62 genetic or environmental factors. Sample plots for several questionnaire responses and mean image length are shown in figure 13. Ana lys i s o f the various study metrics wi th respect to the questionnaire data was achieved by plott ing all of the data and matching different factors, such as a co ld - l ike virus, to the corresponding sample points (data not shown). If a higher proportion of the data points correlated with the factor were found to l ie above the average value for the metric, it could be suggested that this factor may affect in ter - individual var iabi l i ty . For al l of the study metrics, there is no evidence of anything which may have had a posit ive effect on levels o f D N A damage. L o o k i n g at each ind iv idua l with respect to their questionnaire responses also provided l i t t le insight into the factors responsible for the wi th in i nd iv idua l v a r i a t i o n . 63 160 140+ 120 100-60+ 40 20+ 0+ 15 Mean image length versus age i versus age 1 M h 25 35 45 55 65 Rge in years 47 cases p l o t t e d . Regression s t a t i s t i c s o f MERH on RGE-C o r r e l a t i o n -.22901 R Squared .05245 S . E . of E s t 20.86506 S i q 1215 InterceptCS.E. ) 81.33680< 11.51609) S l o p e C S . E . > - .50949<. , . '32283) Figu re 13(a): Examples o f comparison plots of questionnaire responses versus b lood sample data: - age of subject versus mean image length 64 160 140 120f 100 Mean image length versus v i tamin C intake Uitarn in C intake (0 -1 , lowest to h ighest 47 cases p l o t t e d . Regress ion s t a t i s t i c s o f MEAN on UITC_: C o r r e l a t i o n - .02423 R Squared .00059 S . E . of E s t 21.42844 S i g . .8716 I n t e r c e p U S . E . ) 64.28933< 4.30486) S lope<S .E . ) -.56546C 3.47810) Figure 13(b): Examples of comparison plots of questionnaire responses versus blood sample data: - vi tamin C intake versus mean image length 65 Mean length versus smoking s t a t u s 160+ Smoking s t a t u s (Onon-smoker , 1=smoker) 47 cases p l o t t e d . Regression s t a t i s t i c s of MERN on SMOKER—: C o r r e l a t i o n - .09026 R Squared .00815 S . E . o f Es t 21.34724 S i g . .5463 I n t e r c e p t s . E . ) 64.91657< 3.60834) S lope<S .E . ) -4.34157< 7.14111) Figu re 13(c): Examples of comparison plots of questionnaire responses versus blood sample data: - smoking status versus mean image length 66 IV.5 .8 Normal i ty testing of data It is important when choosing methods of analysis to test the normali ty of the data to be analyzed as this w i l l affect the types of statistical analysis that can be p e r f o r m e d . L o o k i n g at the histogram (figure 4a) for the image length of all cel ls from all samples taken, the data cannot be considered normal but can be characterized as s l i g h t l y l o g n o r m a l . IV .6 Sample size ca lcula t ion One o f the desired results of an assessment of background levels o f variation is to enable the determination of an appropriate sample size for future studies, in order to ensure the statistical s ignif icance of the results. A ca lcula t ion of the sample size necessary for statistical s ignif icance was executed for a cross-sectional study comparing two groups with an alpha o f 0.05 and a power of 0.80. In order to detect an increase of fifteen percent in image length a sample size of approximately 70 individuals per group would be required; to detect an increase o f thirty percent in image length approximately 20 ind iv idua l s per group wou ld be needed. 67 V. Discussion V . l R e c r u i t m e n t The recruitment procedure was less successful than anticipated. A l though 13 indiv iduals d id agree to participate, the ini t ia l objective was to recruit between 15 and 20 nurses for the study. There are several factors that could potentially have increased the success of recruitment at the Vancouver H o s p i t a l - U B C site. A s in many other studies, remuneration can be given to participants for samples g iven. A l t h o u g h this may create a bias in the intentions of the individuals who volunteer, it is l ike ly that it w o u l d have increased by some amount the part icipation from the hospi tal . It is also l ike ly that i f an exposure of concern to this group, such as antineoplastic drugs or X - r a y s , was being studied more indiv iduals wou ld have been w i l l i n g to participate. F r o m a theoretical or research perspective, the findings from a study assessing background var iab i l i ty are extremely valuable, but this value is not always clear from the perspective of a potential subject. It was not possible in this situation to approach a large number of nurses personally, wh ich could also have stimulated participation. A s it was, most o f the target audience was reached by the information sheet alone. It was noted earlier that the in i t i a l information sheet may have contained some vague informat ion, and some of the nurses may have interpreted exc lud ing occupational exposures such as X- rays 68 and anesthetic gases as referring to personal exposures in a patient setting, and considered themselves to be inva l id as participants. A second amended sheet was forwarded to the units but it is unl ikely that, having seen the information once, this second sheet was carefully regarded. If it had been possible for the author to phys i ca l ly approach and speak with these nurses, such problems cou ld have been a v o i d e d . V . 2 Development of the experimental protocol for the comet assay The development o f the experimental protocol for the Comet assay look much longer than anticipated. Because this test is relat ively new, refinements in effectiveness and sensit ivi ty are s t i l l occurr ing at a constant rate, and in order for the data from this study to be most useful, it is desirable to use the most up to date methods. A t some point, however, a final protocol must be accepted. Several problems arose when modif icat ions were introduced to the protocol for the Comet assay used previously in this laboratory, the most t ime-consuming i n v o l v i n g image analysis. Init ial analysis of the images produced was performed wi th a computer, measuring not only image length but the intensity, and therefore the amount, o f D N A in both the head and tail regions. Change to a more sensitive dye ( Y O Y O - 1 ) wreaked havoc with the computer imaging system, and although much time and effort was spent on remedying this situation, it was not successful. It was decided that because of the increased sensit ivi ty and the newly discovered potential for preserving the slides until image analysis could be performed, the newer dye would s t i l l be used. Preparation of slides from the blood samples went smoothly, but 23 percent o f the samples were compromised before any information cou ld be extracted from them. There were two reasons why this occurred. From the protocols for the Comet assay upon which ours were based, it was stated that the slides could be left in the lysis 69 buffer at 4°C for at least four weeks without affecting the results (Tice & Andrews, 1993). For all but 10 of the 73 samples, the slides were left in the lysis buffer for a maximum of two hours. The slides from those ten samples, from the second and third day of sampling, were left in the lysis buffer for approximately seven days due to an illness on the part of the researcher. After the slides were scored, it was noted that cells on all slides from that time period were increased dramatically in tail length, and, as this was not seen for samples from any other date, the data from the affected samples was discarded. This effect of prolonged exposure in our laboratory was not noted early on because even in the early stages of protocol development slides were never left longer than overnight in the lysis buffer. It should also be noted that, after personal communication with Dr. NP Singh (1994), several changes were made to the composition of the lysis buffer that may have decreased the amount of time slides could remain immersed without any effect. These changes involved the removal of dimethyl sulfoxide (DMSO) from the lysis solution and the addition of Triton X-100. Another seven samples were lost toward the end of the study because of another assumption of stability that was proved false in our laboratory. It had been demonstrated to the author that, from previous experimental verification, the technique of alcohol drying would allow the slides to be preserved for at least six months (Dr. NP Singh, personal communication). Normally, the slides were scored within one week of preparation; for these seven samples, however, it was more than two weeks before the slides were looked al. Al this time, the slides no longer had any visible cells or DNA on them. Some of the slides from the same sampling days were scored two days after they were prepared and were normal, indicating thai something happened to the cells on the slides in ihe following weeks. It could be possible that, since alcohol drying fixes the DNA to the microscope slide, the DNA loosened and was washed off when dye was added to the slide. W h i l e this may seem obvious in hindsight, it can be concluded from these incidents that i f a protocol developed for use is appreciably different from that used in other laboratories upon which it was based, the effect of different steps on the experimental outcome detailed by these other laboratories should be ver i f ied with data derived from the new protocol . V . 3 Future changes to the experimental protocol It has been suggested by Sardas el al (Sardas, Walker , A l k o y l , & Karakaya, 1995 Dec.) that longer times for the unwinding and electrophoresis steps of Ihe comet assay protocol may be necessary i f the detection of lower levels of damage is desired. This is l ike ly for agents having a subtle effect on D N A damage, and may have been a beneficial addition to the protocol for a study of background or baseline levels o f D N A damage. It would also have been beneficial to score a greater number of cells on each slide, which would a l low for a more val id estimate of the true mean. V . 4 Corre la t ion between dale of sample and mean image length When all available data from this study is compared to the sample number, as in figure 5, there is a large correlation between the two factors. Th is observation is interesting, because the assignation of a sample number is in no way related to the assay itself. The correlation could be explained in two ways: the experimenter was hon ing the technique over l ime , and the introduct ion o f technical va r i ab i l i t y into the total measured decreased accordingly , or the sample number, being related by nature to the date o f sampling, indicated some sort o f seasonal effect, as samples were taken from January 25 to A p r i l 6. Bo th explanations may have some val id i ty , but the first is verif iable. Some individuals started to give samples later on in the study, and there is less of a correla t ion between mean and sample number for these participants than those who 71 gave samples in the first few weeks. In fact, the first three days of sampling resulted in average image lengths so much greater than the days that followed that it was decided to remove these samples from analysis. When these data points are removed, there is less of a correlation between mean and sample number overall. V.5 Analysis of the technical variability Two groups of information could potentially allow for an analysis of the technical variability in this study: comparison of replicate slides and analysis of MOLT-4 variability. Each time that a blood sample was taken, two replicate slides were prepared and analyzed. A comparison of these replicates gives insight into the degree of technical variability that results from replicate-to-replicate variation. Theoretically, if all steps of the experimental protocol are performed identically, there should be no significant difference between the replicates. The average coefficient of variation calculations for the blood sample replicates was 16.30%, and the MOLT-4 same-day slides had an average CV of 15.72%, deviating significantly form the theoretical zero percent to suggest that some amount of technical variability at the replicate-to-replicate level exists in this study. Because both slides in any two replicate sets were treated to the same conditions throughout preparation, it is difficult to pinpoint the source of this observed variation. The most likely candidate for an introduction of variation at this level is during scoring, when errors in judgment on the part of the scorer and an inadvertent non-random sample of the total cells on a slide can occur. It would have been desirable to rescore some slides in order to determine whether significant scorer effects on the test metrics occurred; unfortunately, as mentioned earlier in the discussion, the slides were not stable enough to enable rescoring. 72 It is interesting to note that the range of C V s for the blood sample replicates is much greater than that for the M O L T - 4 same-day slides. There is no obvious reason for this difference to exist. A n a l y s i s of the M O L T - 4 ce l l l ine var iabi l i ty can also provide information regarding technical va r i ab i l i t y , with respect to the day-to-day sources of var ia t ion . This source of variat ion could not be analyzed to a comfortable degree o f confidence using the data obtained from the M O L T - 4 cel l l ine, however, because the sources of var iab i l i ty wi th in the ce l l l ine i tself could not be determined. F igure 12 indicates that only a slight relationship exists between the subject mean image length and the M O L T - 4 mean image length. There are three explanations as to why a greater relationship does not occur: the response o f the M O L T - 4 cells in the Comet assay is dramatically different than that o f lymphocytes, the technical var iabi l i ty in this study was extremely low, or the cel l l ine did not remain stable over the time course of the study but instead had a relatively large amount o f b i o l o g i c a l va r iab i l i ty itself. The response of the M O L T - 4 cells was very s imilar to that of the sample lymphocytes under condit ions where a large amount of D N A damage was induced (data not shown). It cannot be determined without quantitative evidence whether more subtle and less damaging exposures w i l l produce s imi la r responses in both cel l t y p e s . The stability of the cel l l ine over l ime has the greatest effect on our lack o f confidence i n this standard as an accurate measurement o f technical va r i ab i l i ty . The coefficient of variat ion of 27.63% calculated from ihe mean image length for a l l M O L T - 4 slides suggests that the cell line is not stable over time, but varied in the amount o f D N A damage measured. If one looks at the cel l line as another participant in the study, provid ing samples over an extended time period, the value o f 27.63% is 73 quite close to the average C V for within subjects, between days o f 24.40%, suggesting that the ce l l l ine varies over time as the participants in the study d id , exhib i t ing both b i o l o g i c a l and technical va r iab i l i ty . Wi thout more detailed information on the components of var iabi l i ty for the ce l l l ine, this information cannot be used to assess the day-to-day technical var iab i l i ty in this study. V . 6 Assessment of the inter- and in t ra - ind iv idua l va r i ab i l i ty V i s u a l observation of the data indicates that some amount o f both between and wi th in ind iv idua l var iab i l i ty was detected in this study, although the exact proportions of each which make up the total var iabi l i ty could not be calculated. V a r i a b i l i t y between individuals can have two sources: b io log ica l or genetic differences and envi ronmenta l differences. Env i ronmen ta l sources o f va r i ab i l i t y could include exposures to cigarette smoke (Sardas, Walker , A l k o y l , & Karakaya , 1995 D e c ) , pesticides (Ribas, F r e n z i l l i , Barale , & Marcos , 1995 A u g . ) , styrene (Bast lova, V o d i c k a , Peterkova, H e m m i n k i , & Lambert, 1995 Oct.; V o d i c k a , Bast lova, V o d i c k o v a , Peterkova, & Lambert , 1995 Jul .) , and radiation (Plappert, Raddatz, Roth , & Fl iedner , 1995 M a y ; T ice & Strauss, 1995 M a y ; Vi jayalaxmi , T ice , & Strauss, 1992). Genetic differences could include vary ing proportions of peripheral ce l l types in the b lood (Ho lz , Jorres, Kastner, & Magnussen, 1995 Nov. ) as wel l as syndromes which cause an increase in the levels of D N A strand breakage (Cleaver, 1968; Lohman , et a l . , 1992; Thompson, et al . , 1991). None of the informat ion gained from the quest ionnaires, charac te r iz ing either of these two sources, could be associated with an increase in D N A damage. S i m i l a r i t y in some unknown genetic or unqueried environmenta l factor cou ld e x p l a i n these re la t ionsh ips . There is a also a relatively large amount of wi th in ind iv idua l var iabi l i ty to account for in this study. Var iab i l i ty within one ind iv idua l is diff icul t to attribute to 74 a certain environmental factor when only a smal l number o f samples are taken, as is the case here. C y c l i c a l , continuous effects which are always present, such as stages of the menstrual cycle (D'Souza, et a l . , 1988; Joseph-Lerner, et a l . , 1993) and changes in diet and v i tamin intake (Green, et a l . , 1994; Hartmann, Neiss , Grunert -Fuchs, Poch, & Speit, 1995 Apr . ) , could possibly be associated with changes in the level of D N A damage in one person. Seasonal differences can also have an effect on levels of D N A damage (Tucker, et a l . , 1987). A s wi th the between ind iv idua l va r i ab i l i ty , no factor from the questionnaire data could be attributed with the variation in D N A damage in one person over time. V . 7 Compar i son o f the different lest metrics The reason that several different metrics were used in this analysis was to determine the relat ive usefulness. Figures 6-11 provide a graphical presentation of the data from each subject for each test metric, and depict clear differences in the range o f values both wi th in and between indiv iduals for each of the different metrics. The coefficient of variat ion calculat ions also show that the different test metrics detect different levels o f variance from the same samples. However , none of the different metrics a l lowed an attribution o f factors from the questionnaire to changes in the levels of D N A damage. The choice o f metrics for an environmental exposure would be dependent on the b io log ica l effects of that exposure on the tissue type being studied. Fo r example, i f exposure to chemical A causes a large amount of damage to a small number o f cells , only ca lcula t ing the mean image length may not detect this effect, whi le the percentage o f images over a large value (ie. the mean +/- two standard deviations) could . L o o k i n g at several different metrics for the data from any given study could potent ia l ly increase the amount o f useful or interesting informat ion obtained. 75 V . 8 What do the results say about study design? The data collected for al l individuals in this study indicates that, over time, differences can be detected in the levels of D N A damage in the same person. This observed in t ra - ind iv idua l va r iab i l i ty gives evidence that it may be more appropriate to obtain mul t ip le samples from participants over a period o f t ime, rather than pe r fo rming a cross-sect ional study, par t icu lar ly when assaying women . The sample size calculations indicate that, for a cross-sectional study using this protocol o f the Comet assay, a relatively large number o f participants (70 per group) would be needed in order to detect a 15% difference in mean image length. Betti et al (Bett i , et al . , 1995) detected a smaller effect than 15% for smoking in a group of 200 indiv idua ls , suggesting that this protocol for detecting D N A damage may not be suitable for agents causing only smal l or subtle increases in response levels . V . 9 Future Considerations for the use of the Comet assay in field studies of o c c u p a t i o n a l exposures V.9 .1 Development of the Comet assay protocol The results o f this experimental process underline the importance o f a repetitive val idat ion of the test protocol to be used before any field samples are taken. After appl icat ion o f the in i t ia l experimental protocol to the ce l l type o f interest in order to become famil iar with the technique, a test series using an agent 76 known to produce an observable response in the assay should be performed. For the comet assay, the cells could be exposed to different concentrations of hydrogen peroxide (Holz, et al., 1995 Nov.) and the response at these each level recorded. This test should be replicated until it is clear that the responses observed are consistent and reproducible. A maximum acceptable amount of error should be decided upon before initiating the validation series. When any subsequent change to the experimental protocol is introduced, this same experiment should be performed in order to assess the effect of that change on the response measured. All changes should be introduced independently so that any noticeable difference in the response observed can be attributed to a certain factor. Only after such validation is carried out should a change be incorporated into the experimental protocol. The attempted introduction of refinements or changes to the experimental protocol continued until about one month prior to the collection of the first blood samples from the study participants. These changes included: 1. ) the use of immuno-magnetic beads to separate sub-populations of lymphocytes. 2. ) the use of proteinase K to digest cellular proteins. 3. ) the addition of 8-hydroxyquinoline and dimethyl sulfoxide to the electrophoresis buffer. 4. ) the addition of triton X-100 to the lysis buffer. 5. ) alcohol drying of the slides before staining. 6. ) the use of the YOYO-1 fluorescent dye instead of ethidium bromide to stain the cells. 7. ) measurement of the image length of the cells observed rather than a computer-aided image analysis. The first six of these changes had been incorporated successfully by other laboratories, and the effects on the response of cells measured by the assay had been 77 assessed in these laboratories and found to be negligible (Singh, et a l . , 1994; T ice & Andrews , 1993). Several o f the steps or changes were incorporated into the authors' protocol on the assumption that no effect would be seen in our laboratory as we l l . It is d i f f icul t to draw a conc lus ion regarding the appropriateness o f this assumption, however, as the steps for a repetitive val idat ion of the protocol were not undertaken at any stage o f the protocol development. It is safe to assume, however, that, had this been done, many of the samples lost could have instead added to the information base of the study. A l s o , potentially useful changes listed above that cannot be found in the f inal experimental protocol used, because of perceived negative effects on ce l lu la r response, may have been added i f they had been validated properly. It wou ld be expedient to keep rigorous track of the changes that are incorporated into a protocol , and ensure that the posi t ive effects exceed the negative. One should also consider the amount of l ime available for complet ion o f the study, and the proportion of that time which can be allotted lo val idat ion of the e x p e r i m e n t a l p r o t o c o l . V .9 .2 Use of the M O L T - 4 cel l l ine as an internal conlrol The use of the M O L T - 4 cel l l ine as an internal control d id not effectively provide information on between sample, day lo day technical va r iab i l i ty , as was intended. The calculations of coefficient of variation indicate a large amount of both inter- and intra-day var iab i l i ty , but the proport ion of this value which may be attributable to technical rather than b io log i ca l va r i ab i l i ty cannot be e lucidated because of the variabil i ty of the M O L T - 4 cell line itself over time. A better source of informat ion on day-to-day technical var iabi l i ty may have been to use frozen aliquots of a single blood sample, thawed and treated as the sample cells were every day that testing was performed. Each aliquot of cells originated from the same sample and should therefore have approximately the same average amount o f damage. It may be 78 appropriate to use a method other than the MOLT-4 cell line to determine the amount of day-to-day technical variability. V.9.3 Development and assessment of the questionnaire The questionnaire used in this study could have been improved in several ways. Similar to the testing of the protocol for the comet assay, the questionnaire should have been validated in a structured manner before it was used on the actual study participants. This would ensure that the questions asked were clear to both the person writing them and the individuals answering them. It would also allow for the fine-tuning of questions so that the data would be more easily analyzed when the sampling was completed. It is important to have a clear idea of why you are asking the questions and how you are going to use and record or code the data before the questionnaire is used. It may also have been beneficial to ask additional questions that were more specific to the unique aspects of this study. For example, more specific questions on the menstrual cycle could have been incorporated in the questionnaire so that more detailed information could have been used to assess the effects of variations in cycle on levels of DNA damage. A questionnaire for providing such detailed menstrual cycle information was produced and given to the author by Dr. Jerilyn Prior, of the University of British Columbia (personal communications). However, because the participants had to be trained over several months time to be able to properly answer the questions, it was decided that these questions could not be incorporated into the study at such a late date. 79 V.9.4 Recruitment of subjects It was expected that there would be a higher response rale for this study. The number of participants could have been increased if a greater number of potential subjects could have been approached in person, rather than rely on the information letters distributed on the wards. Recruitment of nurses from other hospital sites could have been attempted as well. V.9.5 Statistical analysis of the data obtained It is important to have access to a strong background in slatistics in order to complete the complicated statistical analysis required for this data. The types of analyses which may have to be performed should be determined or at least estimated before the collection of samples commences. V.9.6 Summary 1. ) A repetitive validation of the assay protocol should be performed on both the original procedure and any changes introduced before any field samples are taken. 2. ) Rigorous track of the changes thai are incorporated into a protocol should be kept, and insurance obtained that the positive effects of inclusion in the protocol exceed the negative. 3. ) Consider the amount of time available for completion of the study, and the proportion of that time which can be allotted to validation of the experimental protocol. 4. ) Consider another method other than a cell line to determine the amount of day-to-day technical variability. 5. ) Any questionnaire used should be validated before it is given to the study participants. 6. ) Obtain more detailed information with respect to menstrual cyc le on the q u e s t i o n n a i r e . 7. ) Approach more potential participants in person to increase the number o f study subjects. 8. ) Obta in experience in statist ical manipulat ions and analysis before co l l ec t i ng s a m p l e s . 81 VI Conclusions 1. An experimental protocol should undergo repetitive validation and be handled with confidence before that protocol is applied to a study population. Any changes incorporated into the protocol should be validated independently. 2. Significant amounts of technical and biological variability were observed, but the proportion of each in the overall or total variability could not be determined. The amount of biological variation appeared to exceed the technical variation. 3. None of the variability observed was attributable with any degree of confidence to any of the questionnaire data. 4. A larger number of cells should be scored for each sample to increase the validity of the test measurements. 5. The MOLT-4 cell line appeared lo be unstable over lime, expressing some levels of biological variability. Because the components of variability could not be accurately assessed with the data available, this cell line should not be used as an internal standard for this assay until more information is collected. 82 6. The results of this study suggest that it may be necessary to employ a longi tud ina l study design, rather than cross-sect ional , when us ing the Comet assay, par t icular ly when the populat ion o f interest is comprised o f women. 7. The sample size necessary for statistical s ignif icance in a cross-sect ional study comparing two groups with an alpha of 0.05 and a power of 0.80 is approximately 70 ind iv idua ls per group i f the detection of an increase of fifteen percent in image length is desired, and approximately 20 individuals per group i f an increase of thirty percent in image length is to be delected. 83 VII References (1986). B i o l o g i c a l Dosimetry: Chromosomal Aberra t ion Ana lys i s for Dose Assessment. (Technical Reports Series N o . 260). 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Amer i can Journal of A n a t o m y . 168 . 467-517. Hayes , R . (1992). B iomarkers in occupat ional cancer ep idemio logy : considerat ions in study design. E n v i r o n m e n t a l Hea l th Perspec t ives . 9 8 , 149-154. H o l z , O. , Jorres, R . , Kastner, A . , & Magnussen, H . (1995 Nov . ) . Differences in basal and induced D N A single-strand breaks between human peripheral monocytes and lymphocytes . M u t a t i o n Research, 332(1-2) . 55-62. Huber , R. , Braselmann, H . , & Bauchinger , M . (1989). Screening for in ter indiv idual differences in radiosensi t ivi ty by means of the micronucleus assay in human lymphocytes . Rad ia t ion and Env i ronmenta l B i o p h y s i c s , 2 8 . 113-120. H u l k a , B . S. (1990). Overview of Bio log ica l Markers. In B . H u l k a , T. W i l c o s k y , & J. Gr i f f i th (Eds.), B i o l o g i c a l Markers in Ep idemio logy (pp. 3-15). New Y o r k : Oxford U n i v e r s i t y Press . Joseph-Lerner, N . , Fejgin, M . , Ben-Nun, I., Legum, C , & A m i e l , A . (1993). The cor re la t ion between the frequency o f s is ter -chromat id exchange and human reproductive hormones. M u t a t i o n Research, 3 0 0 , 247-252. Last, J. (1983). A Dic t ionary of Ep idemio logy . New Y o r k : Oxford Universi ty Press. Lazu tka , J . , Dedonyte, V . , & Krapavicka i te , D . (1994). Sister-chromatid exchanges and their dis t r ibut ion in human lymphocytes in relation to age, sex, and smoking . M u t a t i o n Research. 3 0 6 . 173-180. L i t t l e f ie ld , L . , & G o h , K . - O . (1973). Cytogentic studies in control men and women. Cytogenetics and Ce l lu l a r Genet ics . 12, 17-34. 85 Lohman , P. , M o r o l l i , B . , Darroudi , F . , Natarajan, A . , Gossen, J . , Venema, J . , Mul lenders , L . , V o g e l , E . , V r i e l i n g , H . ( & Zeela'hd, A . v. (1992). Contributions from m o l e c u l a r / b i o c h e m i c a l approaches in ep idemio logy to cancer r isk assessment and prevention. E n v i r o n m e n t a l Hea l th Perspect ives , 9 8 , 155-165. M a c K a y , C. R . (1994). Concept of Memory T-cells. In E . C. Snow (Eds.), Handbook o f B and T Lymphocytes (pp. 159-175). San Diego: Academic Press Inc. M a r g o l i n , B . , & Shelby, M . (1985). Sister Chromatid Exchanges: A reexamination of the evidence for sex and race differences in humans. E n v i r o n m e n t a l M u t a g e n e s i s , 7.(Suppl. 4), 63-72. M i n o w a d a , J . , Ohnuma, T. , & Moore , G . (1972). Rosette-forming human lymphoid ce l l l ines . I. Establ ishment and evidence for o r ig in of thymus-der ived lymphocy tes . Journal of the Nat ional Cancer Institute, 4 9 , 891-895. O l i v e , P. , Banath, J . , & Durand, R. (1990). Heterogeneity in radiation-induces D N A damage and repair in tumour and normal cel ls measured using the "comet" assay. Rad i a t i on Research, 122, 86-94. Os t l ing , 0 . , & Johanson, K . (1984). Microelec t rophoret ic study o f radiat ion-induced D N A damages in indiv idual mammalian cel ls . B i o c h e m i c a l and B i o p h y s i c a l Research C o m m u n i c a t i o n s . 123 , 291-298. Perera, F . , & Whyatt , R . (1994). Biomarkers and molecular epidemiology in mutat ion/cancer research. M u t a t i o n Resea rch . 3 1 3 , 117-129. Plappert, U . , Raddatz, K . , Roth, S., & Fliedner, T. (1995 M a y ) . D N A - d a m a g e detection in man after radiation exposure - the comet assay- its possible appl icat ion for human b iomoni to r ing . Stem C e l l s . 13(Suppl 1), 215-22. Ribas , G . , F r e n z i l l i , G . , Barale, R., & Marcos, R. (1995 Aug. ) . Herbicide-induced D N A damage in human lymphocytes evaluated by the S C G E assay. M u t a t i o n R e s e a r c h . 344(1-2) , 41-54. Sardas, S., Walker , D . , A l k o y l , D . , & Karakaya, A . (1995 D e c ) . Assessment of smoking-induced D N A damage in lymphocytes of smoking mothers o f newborn infants using the a lka l ine s ing le -ce l l gel electrophoresis technique. M u t a t i o n R e s e a r c h . 335(3) , 213-217. Schulte, P . , & M a z z u c k e l l i , L . (1991). Va l ida t ion of b io logica l markers for quantitative risk assessment. E n v i r o n m e n t a l Hea l th Perspect ives . 9 0 . 239-246. Singh, N . , M c C o y , M . , T ice , R. , & Schneider, E . (1988). A simple technique for quantitation of low levels of D N A damage in individual cel ls . E x p e r i m e n t a l C e l l R e s e a r c h . 175 . 184-191. S ingh , N . , Stephens, R. , & Schneider, E . (1994). Modi f ica t ions of alkal ine microgel electrophoresis for sensit ive detection of D N A damage. In te rna t iona l Journa l of Radia t ion B i o l o g y . 66(1) . 23-28. 86 Sorsa, M . , W i l b o u r n , J . , & Va in io , J. (1992). Human cytogenetic damage as a predictor of cancer risk. In H . Va in io , P. Magee, D . McGregor , & A . M c M i c h e a l (Eds.), Mechan i sms o f carcinogenesis in risk ident i f icat ion, (pp. 543-554). L y o n : I A R C S c i e n t i f i c P u b l i c a t i o n s . Sprent, J . , & Tough, D . (1994). Lymphocyte Life-span and Memory . S c i e n c e . 2 6 5 . 1395-1400. Therman, E . (1986). Chromosomes and Oncogenes. In Human C h r o m o s o m e s -structure, behavior , effects (pp. 262-272). New Y o r k : Sp r inge r -Ver l ag . Thierens, H . , V r a l , A . , & Ridder, L . d. (1991). B io log ica l dosimetry using the micronuc leus assay for lymphocytes : in te r ind iv idua l differences in dose response. Heal th Phys i c s . 61 (5). 623-630. Thompson, M . , Mclnnes , R . , & W i l l a r d , H . (1991). Genetics in M e d i c i n e (5 ed.). Ph i l ede lph i a : W B Saunders Company. T ice , R . , & Andrews, P. (1993). Protocol for the application of the alkaline single cell gel ( S C G ) assay to the detection of D N A damage in eukaryote cells. N o . Triangle Research Park, Nor th Ca ro l i na . T ice , R . , & Strauss, G . (1995 May) . The single cell gel eleclrophoresis/comet assay: a potential tool for delect ing radiation-induced D N A damage in humans. S t e m CeJJs, 1_3(Suppl 1), 207-14. Tucker , J . , Christensen, M . , Strout, C , M c G e e , K . , & Carrano, A . (1987). Varia t ion in the human lymphocyte sister chromat id exchange frequency as a funct ion o f t ime: results of dai ly and twice weekly sampling. E n v i r o n m e n t a l and M o l e c u l a r Mutagenes i s . 10, 69-78. V i j a y a l a x m i , T i ce , R. , & Strauss, G . (1992). Assessment of radiation-induced D N A damage in human blood lymphocytes using the s ing le -ce l l gel electrophoresis technique. M u t a t i o n Research, 2 7 1 , 243-252. V i n e , M . (1992). Mic ronuc le i . In B . Hulka , T. Wi lkosky , & J. Grif f i th (Eds.), B i o l o g i c a l Markers in Ep idemio logy (pp. 125-146). New Y o r k : Oxford Univers i ty Press. V o d i c k a , P . , Bast lova, T., Vod ickova , L . , Peterkova, K . , & Lambert, B . (1995 Jul.) . B iomarke r s o f slyrene exposure in laminat ion workers: levels of o6-guanine D N A adducts, D N A strand breaks, and mutant frequencies in the hypoxanthine guanine phosphor ibosyl t ransferase gene in T - l y m p h o c y l e s . C a r c i n o g e n e s i s . 16(7) , 1473-81. W i l k o s k y , T . (1990). Cr i ter ia for Selecting and Evaluat ing Markers . In B . Hu lka , T . W i l k o s k y , & J. Griffi ths (Eds.), B i o l o g i c a l Markers in Ep idemio logy (pp. 28-55). New Y o r k : Oxford Univers i ty Press. W i l k o s k y , T . , & Gri f f i th , J. (1990). Appl icat ions of B io log i ca l Markers . In B . H u l k a , T. W i l k o s k y , & J. Grif f i th (Eds.), B i o l o g i c a l Markers in E p i d e m i o l o g y (pp. 16-27). New Y o r k : Oxford Univers i ty Press. 87 W i l k o s k y , T. , & Rynard , S. (1992). Sister Chromatid Exchange. In B . Hu lka , T . W i l k o s k y , & J. Grif f i th (Eds.), B i o l o g i c a l Markers in Ep idemio logy (pp. 105-124). New Y o r k : Oxfo rd Univers i ty Press. W o g a n , G . (1992). M o l e c u l a r epidemiology in cancer risk assessment and prevention: recent progress and avenues for future research. E n v i r o n m e n t a l H e a l t h P e r s p e c t i v e s . 98 , 167-178. 88 A p p e n d i x A Information sheet and letter o f introduction provided for the nurs ing units for the purpose o f recrui tment 89 December 16, 1994 N u r s e s V H H S C , U B C - s i t e Hospital R e : Request for par t ic ipants for a research study i n v o l v i n g the assessment o f background levels of D N A strand breakage in the white b lood cells of female nurses. The purpose of this letter is to ask for volunteers to give blood samples for a research project that w i l l contribute to the study of occupat ional exposures to nurses. I am a graduate student in the Occupat iona l Hygiene Programme al the U B C ; the pr imary focus o f this f ie ld is the recogni t ion , eva lua t ion , and cont ro l o f hazardous exposures in the workp lace . A descr ip t ion o f the thesis project that 1 am undertaking in order to complete the requirements for my degree is p rov ided on the attached page. A sheet w i l l be provided in your communicat ions book so that you can leave your name and a contact number i f you wish to participate in this study or would l ike more informat ion . Y o u should also feel free to contact K a r a l y n n E l l at 822-9573 dur ing the day i f you have any questions or w o u l d l i ke more details about the research that I am doing. Thank you for taking the time to read this information and cons ide r ing my request. S i n c e r e l y , K a r a l y n n E l l 90 F o r my thesis, I w i l l be conduct ing a study o f basel ine values for genetic markers among female nurses who have not been exposed to known D N A damaging agents. The purpose of this study is lo obtain information on the levels of D N A strand breakage caused by normal act ivi ty and the va r i ab i l i t y between different ind iv idua l s and wi thin an ind iv idua l over time. Breaks in D N A occur in healthy individuals and a normal background level is not ind ica t ive o f potential problems. The informat ion obtained in this study w i l l be used to plan future studies of occupational exposures in nurses (for example, a study of nurses exposed to antineoplastic drugs). Y o u r part icipation in this study w i l l be beneficial in many ways. T h i s study w i l l draw attention to the nursing profession and its potential hazards. It w i l l also increase knowledge regarding women and normal levels of D N A strand breakage, as research on women is currently lack ing in the literature. A d d i t i o n a l l y , it is not often k n o w n for even the more established b iomon i to r i ng tests what levels o f va r i ab i l i t y exist wi th in a given person over the course of l ime. One o f the unique aspects of this study is that it asks the participants to contribute six blood samples over a period of four months , so that this v a r i a b i l i t y w i th in one person can be assessed. T h i s information in i tself is a significant contr ibut ion to research in this f ie ld . Because we are l o o k i n g for background leve ls o f D N A breakage, some c i rcumstances , such as previous exposures to k n o w n damaging agents or elevated hormone levels , can create problems with the analysis of results. On this basis, we have decided that it is necessary to try and s imp l i fy our analysis by l i m i t i n g our study to participants who do not belong in the fo l lowing categories. If you have ever rece ived chemotherapy or radiotherapy, are current ly pregnant or breastfeeding, or have H I V or HepB infection we unfortunately must exclude you from our study. In a d d i t i o n , i f you r o c c u p a t i o n w i t h i n the last f ive years i n v o l v e d work wi th ant ineoplast ic drugs (by m i x i n g , preparation or adminis t ra t ion ac t iv i t ies ) , X - r a y s (of other people, not yourself) , ethylene oxide, or anesthetic gases (again, used on other people), you would also have to be excluded. Par t ic ipa t ion w i l l require s ix blood samples given at random dates over a three month period and complet ion of a questionnaire. The blood samples would be given at the labora tory in the U B C - s i t e hosp i ta l on the par t ic ipants o w n t ime, and the questionnaires w i l l be prov ided as a package, completed at the convenience o f the part icipant, and returned to me v ia campus ma i l . The entire process should take 30 minutes o f your time per blood sample given. Appendix B Letter of consent to participate in this study 92 Consent Form Determination of baseline values of DNA strand breakage in a population of female nurses B A C K G R O U N D and PURPOSE Dr PJE Quintana, an assistant professor with the Occupational Hygiene Programme, and a graduate student, Ms. Karalynn Ell, will be conducting a study at the University Hospital-UBC site of baseline values for genetic markers among female nurses who have not been exposed to known DNA damaging agents. The purpose of this study is to obtain information on the levels of DNA strand breaks in relation to the menstrual cycle and normal activity, and the variability between different individuals and within an individual over time. Breaks in DNA occur in healthy individuals and a normal background level is not indicative of potential problems. The information obtained in this study will be used to plan future studies of occupational exposures in nurses (for example, a study of nurses exposed to antineoplastic drugs). Because we are looking for background levels of DNA breakage, some circumstances, such as previous exposures to known damaging agents or elevated hormone levels, can create problems with the analysis of results. On this basis, we have decided that it is necessary to try and simplify our analysis by limiting our study to participants who do not belong in the following categories. If you have ever received chemotherapy or radiotherapy, are currently pregnant or breastfeeding, or have HIV or HepB infection we unfortunately must exclude you from our study. In addition, if your occupation within the last five years involved work with antineoplastic drugs (by mixing, preparation or administration activities), X-rays (of other people, not yourself), ethylene oxide, or anesthetic gases (again, used on other people), you would also have to be excluded. This study will measure DNA strand breakage in lymphocytes from a blood sample, using the single cell gel electrophoresis or "Comet" assay, a relatively new technique for measuring levels of single stranded DNA breaks, as the assessment method. The comet assay has the potential to be a very powerful measurement tool, but little work has been done with this assay on human populations, and there is limited information on the variability that exists among normal, healthy individuals. 93 P A R T I C I P A T I O N Each study participant will donate six venous blood samples at random dates over a period of roughly three months. The blood samples will be quite small (5 mL) and taken from the arm by a registered nurse. This procedure may cause some discomfort, and slight bruising. On rare occasions infection may occur. The participant will also be required to answer some questions about their diet, health, and work history. Each blood sample will take only a few minutes, and the questionnaire less than ten minutes. C O N F I D E N T I A L I T Y All information gathered about an individual will be kept confidential. Anonymity of individuals is guaranteed in any publication. Names and other identifying information will be kept in a secure place, separate from the data. Medical information, identified by code known only to the researchers, may be released to a physician for interpretation. CONTACTS Dr. PJE Quintana and Karalynn Ell may be contacted al any time at 822-9595 if you have questions or concerns regarding your participation in the study. If you have any questions or concerns about your treatment and rights as a research subject, you may feel free to coniaci Dr. RD Spratley, Director, Office of Research Services, at 822-8595. YOUR RIGHTS as a SUBJECT You may decline to participate in the study, and after signing this form you remain completely free to withdraw from the study at any time without any adverse effect. You will be informed of the overall or group results of this study; there will be no indication of individual results, conforming to the confidentiality agreement above. 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CD 05 3 05 CD < 05 o o o 3 oo 5' o CD 05 VO VO 03 03 CD VO T 3 O T3 3 "-I 3 3 g § VO 03 cr o o 3 CD 3 " 05 < CD o 3 o 05 05 00 CD o o —I —t Z Z 00 3 o' o Appendix D Results of B l o o d Sampl ing Subject Sample ID Date of Sample Group # Mean* in jj.m Median* in jj.m Mode* in um % over 72 um % over 134 um % over 194 um 566 1 8-Feb 4 88.46 76 50 53 19 7 566 2 21-Feb 9 44.62 26 20 13 5 2 566 3 7-Mar , 16 58.26 28 22 22 16 4 566 5 22-Mar . 21 32.6 24 22 6 0 0 566 6 27-Mar 25 37.44 26 22 8 2 2 574 3 10-Feb 8 74.78 48 46 30 15 7 574 4 28-Feb 15 43.9 34 3<L„ 8 4 2 574 6 31-Mar 27 48.52 """"22 "' 13 6 _ 4 655 1 „25™laj]i_ 2 133.6 140 1 64 i ° l „ 52 _ 10 "l8 55.08 26 "22 2 5 ' ""To" 2 655 5 23-Mar 23 88.08 58 30 42 22 12 655 6 30-Mar 26 124.08 108 184 66 42 16 681 1 7-Feb 3 120.24 99 104 68 36 19 681 2 8-Mar 20 61.52 37 22 29 13 0 681 3 15-Mar 99 43.76 36 26 6 2 2 703 2 10-Feb 7 86.16 65 30 43 22 3 703 3 24-Feb 14 111.14 70 66 48 31 18 703 4 8-Mar_ 20 57.26 41 24 24 9 0 5 22-Mar ' 22 58.52 ' 36 24™" 19 10 2 29 56.24 42 28 16 10 0 711 1 10-Feb 7 80 5 2 _ 1 1 ™ 32 21 711 2 24-Feb 14 100.68 """52"" 48 26 *20 711 3 28-Feb 15 72.32 44 42 39 17 5 711 4 7-Mar 17 41.6 26 22 13 7 0 711 5 22-Mar 22 45.46 22 22 14 12 4 711 6 31-Mar 28 58.52 35 22 20 14 6 715 2 10-Feb 6 79.12"" 60 48™" 41 14 5 715 3 23-Feb 11 60.34 44 ,„™.™.. 32 18 9 3 715 4 ™8~-Mar 18 45.78"' '"""2T™ 5 Q 715 5 15-Mar 99 38.84 25 22 4 4 2 715 6 22-Mar 21 49.28 28 24 12 8 5 728 1 25-Jan 1 120.28 124 164 69 44 11 728 2 8-Feb 5 105.16 87 172 55 38 4 728 3 23-Feb 12 55.42 40 26 20 9 5 728 5 30-Mar 26 81.32 55 28 54 _ _ 2 0 _ 8 728 6 29 38.6 30 22 8 0 „ „ „ . . . . 0 812 "~ 1 25-Janm 1 132.16n 137 _ 84'"" 81" ~ I T ™ 812 3 YT-Feb™ T o ™™™5.'86™ 4'i"'""' 30"" Tl™" ~ 7 0™"™ 4 8-Mar 19 47.6 28 24 18 8 0 812 5 16-Mar 98 52.96 24 22 23 14 2 812 6 31-Mar 27 73.96 42 24 30 26 6 851 1 10-Feb 8 52.64 34 100 16 10 0 851 2 23-Feb 12 73.7 60 28 37 13 2 851 4 23-Mar 24 58.32 39 24 26 10 2 906 3 8-Feb 4 125.54 119 80 89 39 6 906 4 21-Feb 56 22 22 3 0 _ J 5 _ 906 6 ' 52.64"" 24 , 1 8„ r .... ^ ™~2 938 1 " 25-Jan l 144.34 156 186" " 48 938 3 7-Feb 3 104.76 92 90 66 „ 25 9 ' 938 4 21-Feb 10 89.32 55 22™ 42 30 8 938 5 16-Mar 98 66 29 24 " ~~18 6 938 6 27-Mar 25 75.48 38 24 30 22 12 942 2 10-Feb 6 75.02 54 30 34 15 8 942 3 23-Feb 11 74.12 42 30 24 15 7 942 4 7-Mar 16 62.14 30 24_ 23 12 5 942 6 23-Mar 24 • 46.96 '" 29 24 " 12 " 6 4 * These values refer to the mean, median, and mode image length A p p e n d i x E Ques t ionna i re Resu Cold or Last virus Ever been Vaccination Vaccination Subject Sample Age Nurse? virus when more or less diagnosed in the last in the last number sample than one with six months? two years? taken? month ago? cancer? 566 2 42 2 1 0 0 0 0 566 3 2 5 * 6 ' " " ""o o 566 5 42 2 0 0 0 0 0 566 6 42 2 0 0 0 0 0 574 4 32 0 0 0 0 0 1 574 6 32 0 0 0 0 0 1 655 '""26"' "6"" ~"*""T 6""~ 655 5 26 0 0 0 0 0 0 655 6 26 0 0 0 0 0 0 681 2 57 1 0 1 0 1 1 681 3 57 ' 1 0 1 0 1 „ 1 703 2 47 ™ ™ ™ 0 0 1 """™"'o"" 703 3 47 1 0 0 1 0 0 703 4 47 1 0 0 1 0 0 703 5 47 1 0 0 1 0 0 703 6 47 1 0 0 1 0 0 711 1 27 1 1 " i " " b""'""" o 711 2 27 1 0 1 0 0 1 711 3 27 1 1 1 0 0 1 711 4 27 1 0 1 0 0 1 711 5 27 1 0 1 0 0 1 711 6 27 1 1 " 1 o" o ™ "1 715 3 43 0 0 1 1 1 1 715 4 43 0 0 1 1 1 1 715 6 43 0 0 1 1 1 1 715 5 43 0 0 1 „ . 1 1 . 1 728 2 31 1 1 0 o™ ~~ ~b~~ 728 3 31 1 0 ~~ 6"' cf "d ~ " " " " ~ 0 728 5 31 1 0 0 0 0 0 728 6 31 1 0 0 0 0 0 812 3 30 0 0 0 0 0 0 812 4 30 0 0 ~ " 0 0 0 . 0 „ 812 6 30" " 0 ""6"'"" T ~0 "b" 812 5 30 0 1 0 0 0 0 851 1 24 0 0 0 o- 0 0 851 2 24 0 0 0 0 0 0 „ 851"" 4 24 o 0 o"" 0~'"™~ o 906 4 46 . 0 0 0 0 0 1 906 6 46 0 0 0 0 0 1 . 938 3 31™ „ o " o""~" """"""o" """""""cf 938 4 31 1 0 0 0 0 1 938 6 31 0 o "6" ""b 0 1 938 5 31 1 6"""" 0 ~b 0 "~ T"" 942 2 23 0 0 0 0 1 1 942 3 23 0 0 '6""" '""'"""7 ""' ~'"i 942 4 23 0 0 0 0 1 1 I l l Subject Sample number Vitamin C usage Vitamin E usage Any X-rays in the last 12 months Number of X-rays in the last ten years Ever used oral contraceptives Regular menstrual cycles (28 days ± 11 Days since the start of the last menstrual cycle 566 2 0 0 1 5 0 1 5 566 3 0 0 1 5 0 1 19 566 5 0 0 1 5 0 1 7 566 6 0 0 1 5 0 1 12 574 4 0 0 1 6 1 1 0 574 6 0 0™"™" ™T ~~6 ™""™-1 655 4 0 0 0 4 1 1 3 655 5 0 0 0 4 1 ~j "~~ TaT™ 655 6 0 0 0 ~4 1 1 25 681 2 0 0 1 15 0 0 n/a 681 3 0 0 15 0" 0 ' n/a 703 2 0 0 1 12 0 1 32 703 3 0 0 1 12 0 1 19 703 4 0 0 1 f„.„ 0 1 3 703 5 0 0 1 12 0 1 17 703 6 0 0 1 "" 12 "b 711 1 2 0 1 5 9 0 8 711 2 2 0 1 5 9 0 22 711 3 2 0 1 5 9 ~~~"~o " 26 711 4 2 0 1 5 9 0 33 „ „ „ „ 711 5 2 o ™ ™" T™" *""™~ 9" 0 711 ' 6 2 0 1 5 9 0 57 715 3 2 1 0 0 0 1 9 715 4 2 1 0 0 0 1 715 6 2 1 0 0 0 1 12 715 ! o" o ' 0"™""' "1 -"5"" 728 2 2 1 0 5 0 1 24 728 3 2 1 0 5 0 1 39 728 5 2 1 0 5 0 1 728 6 2 1 0 5 0 1 29 812 3 0 0 1 0 ™ " " " 0 " " " " 29 812 4 1 0 0 1 0 0 9 812 6 1 0 0 1 0 0 4 812 5 1 0 0 1 0 0 851 1 0 0 1 30 1 0 19 851 2 0 0 1 30 1 1 4 851 4 0 0 1 30 1 1 5 906 4 2 0 0 4 1 1 14 906 6 2 0 0 4 1 1 938 3 1 1 1 4 ™ 0 1 12 938 4 1 1 1 o~~~~™ 2 938 6 1 1 1 ~ — — " 4 " " — 938 5 1 1 1 4 0 1 25 942 2 0 0 1 15 0 0 942 3 0 0 1 1 5 0 0 175 942 4 0 0 1 15 0 0 8 942 6 0 0 1 -15 6 "24" 112 Subiect SamDle Current Exercised Any stressful Servings of How How How Cups of cups of number smoker? vigorously 6 event vegetables much much much regular regular to 48 hours occurring per day red meat cured grilled coffee tea per prior to prior to in diet? meat in meat in per day day samplinq? samplinq? diet? diet? 566 2 1 0 0 2.5 1 1 2 5.5 0 ™ 566 3 1""""" ™0 '™""6"""~ „ „ . _ „ , . "T™ 1 2~"~ '5.5 ™ 566 5 1 0 0 "2 .5 1 1 2 5.5 0 566 6 1 1 0 2.5 1 1 2 5.5 0 574 4 0 0 1 4 2 1 574 6 0 0 0 4 2 1 1 0 0 655 4 0 1 1 3 1 1 1 2 0.5 655 5 0 1 0 3 1 1 1 2 0.5 655 6 0 1 0 3 1 1 1 2 0.5 681 2 0 0 0 1.5 1 1 681 3 0 0 1 1.5 1 1 3 4 0 703 2 1 1 0 10 0 0 2 0 0 703 3 1 1 1 10 0 0 2 0 0 703 4 1 1 0 10 0 0 2 0 0 703 5 1 0 1 10 0 0 2 703 6 1 0 1 10 0 0 2 0 0 711 1 0 1 1 7 2 2 1 2 0 711 2 0 0 1 7 2 2 1 2 0 711 3 0 1 1 7 2 2 1 2 0 711 4 0 0 0 7 2 2 1 711 5 0 1 1 7 2 2 1 „ 2 „ 0 711 6 0 0 0 7 2 ~2 ™0 715 3 0 0 1 6 1 0 2 1 0 715 4 0 0 1 6 1 0 2 1 0 715 "6~ 6 " ™ 0 0 6 1 2~~ T"'~ 0 715 728 1 2.5 728 3 0 0 0 2.5 2 1 2 2.5 0 728 5 0 0 1 2.5 2 1 2 2.5 0 „ 728 6 Tj™ 0 „ „ . „ „ " 2 T 2 2.5'"" 812 3 0 0 0 2.5 0 0 0 0.5 1 812 4 0 0 0 0 0 0 0.5 1 812 6 0 0 .1' " 2T5 0 0 0 0.5 1 812 5 0 0 0 2.5 0 0 0 0.5 „ 1 851 1 1 ' 'o 2"'™" "3'"" ™" 0' 851 2 1 0 0 3 2 - 2 ~~ 2" 0 851 4 1 0 0 3 2 2 „ _ 3 2 0 906 4 0 0 0 • 2 1 906 6 0 0 0 2 1 1 0 0 1.5 938 "6" " "o " ™ "~'""o"'m"""" """2 "'1 " """"0™"" "T" 1 6"™" 938 4 0 0 0 2 1 0 0 1 0 938 6 0 0 0 2 1 0 0 1 0 938 5 0 0 1 2 1 0 0 942 2 0 1 1 5 0 0 0 1 1 . „ „ . . „ . ™ 9 4 2 3~ o'"" o~"~~"" ™i """" 0 ™'Tf"""" 0 'i 942 4 0 1 1 "0 ~ "0 " 1 942 6 0 0 1 0 0 0 1 1 A p p e n d i x Raw data I 114 Subject ID Sample ID Date of Samplej Group ID Blood (lymphocyte) cell samples 566 566 566 566 566 1 566 566 1A 1B 2A 2B 3A 3B 5A 8-Feb 8-Feb 21-Feb 21-Feb 7-Mar 7-Mar 22-ME 4 4 9 9 16 1 6 21 12 1 5 1 0 1 0 1 0 1 0 1 0 13 20 .1 o 1 0 1 1 1 0 1 0 14 22 10 1 0 1 1 1 0 1 0 14 23 1 0 1 0 1 1 1 1 1 0 15 25 1 0 11 1 1 1 1 1 0 1 5 25 1 0 1 1 1 1 1 1 1 0 1 6 26 1 0 1 2 1 2 1 1 1 0 17 26 1 0 1 2 1 2 1 1 1 0 1 9 ' 27 1 0 1 2 1 2 1 1 1 1 20 29 1 0 1 2 1 2 1 1 1 1 20 31 1 0 1 2 1 2 1 1 1 1 20 31 1 0 12 13 1 1 1 1 21 31 1 0 1 2 1 4 1 1 1 1 21 32 1 0 1 3 1 7 1 1 1 1 22 35 1 0 1 3 1 8 1 2 1 1 22 36 1 0 1 3 20 1 2 • 1 1 23 40 1 1 1 4 21 1 2 1 1 23 40 1 1 1 6 22 1 2 1 1 24 41 1 1 1 6 22 1 2 1 1 25 42 1 1 1 7 22 1 2 1 1 25 42 1 1 17 22 1 2 1 1 25 42 1 1 1 7 23 1 2 1 1 26 43 1 1 17 24 1 2 1 1 26 44 1 1 1 9 27 1 2 1 1 26 45 1 1 2 0 27 1 3 1 2 27 47 1 1 22 29 1 3 1 2 27 48 1 1 22 32 1 3 1 2 28 50 1 1 23 32 1 3 1 2 33 50 1 1 23 33 1 3 1 2 35 52 1 1 26 42 : 13 1 2 35 54 1 1 27 43 1 3 1 2 37 . 55 12 27 45 1 3 1 2 37 56 12 27 49 1 3 1 2 37 58 1 2 30 56 1 3 1 2 38 62 1 2 32 68 1 3 1 2 41 64 1 2 35 70 1 4 1 2 42 70 1 3 35 71 1 4 1 3 43 71 1 3 48 73 1 4 1 3 43 75 13 49 75 1 4 1 3 44 77 1 3 56 82 1 4 1 4 44 78 1 3 57 82 1 4 1 5 48 81 1 4 57 83 1 5 1 7 52 84 1 4 62 85 1 5 22 62 85 1 4 64 91 1 5 22 63 90 1 5 67 92 1 8 36 76 99 1 6 74 93 22 36 76 100 1 7 75 105 23 36 83 105 25 82 107 33 50 101 110 35 100 110 35 50 123 110 35 138 125 49 78 115 566 574 574 574 574 574 655 655 6A 3A 3B 4A 4B 6A 1B 4A 27-Mar 10-Feb 10-Feb 28-Feb 28-Feb 31-Mar 25-Jan 8-Mar 25 8 8 15 15 27 2 1 8 1 0 1 0 1 0 1 0 1 0 1 0 20 1 0 1 0 1 1 1 2 1 1 1 1 1 0 20 1 0 1 1 1 3 12 1 1 1 1 1 1 30 1 0 1 1 14 1 3 1 1 1 3 1 1 30 1 0 1 1 14 1 4 1 1 1 3 1 1 33 1 1 11 1 5 1 5 1 2 13 1 1 33 1 1 11 15 1 6 12 1 3 1 1 33 1 1 11 16 1 6 1 2 1 3 1 1 34 1 1 11 1 6 1 6 1 2 1 4 1 1 36 1 1 1 1 1 7 1 6 1 2 1 4 1 1 36 1 1 11 1 7 17 1 2 1 4 1 1 39 1 1 11 1 7 1 8 1 2 1 4 1 1 44 1 1 11 1 8 1 9 1 2 1 5 1 1 44 1 1 11 1 9 20 1 3 1 5 1 1 50 1 1 1 2 20 20 1 3 1 5 1 1 53 1 1 1 2 20 20 1 3 1 5 1 2 55 1 2 12 20 20 1 3 1 6 1 2 57 1 2 1 2 21 21 1 3 1 6 1 2 58 1 2 1 2 21 21 1 3 1 6 1 2 59 1 2 12 21 22 1 4 1 6 12 59 1 2 12 23 22 14 1 6 12 60 1 2 13 23 23 1 5 17 1 2 62 12 13 23 23 1 6 1 7 1 3 63 1 2 13 23 24 1 6 1 7 13 66 1 2 13 23 25 1 6 1 7 1 3 68 1 2 13 23 26 1 6 1 7 14 70 1 2 14 24 27 17 1 7 1 4 71 1 3 14 27 28 1 7 1 7 1 4 72 1 3 15 27 29 1 7 1 8 1 4 73 1 3 15 27 30 1 7 1 8 1 4 74 1 3 1 6 28 32 1 7 1 9 15 75 1 3 1 6 31 32 1 8 1 9 1 5 75 15 17 32 34 1 8 20 1 8 82 1 6 1 7 32 35 1 9 21 1 8 82 23 17 33 43 20 21 1 9 82 29 17 33 50 20 21 1 9 82 30 19 40 51 20 21 21 83 39 1 9 46 62 20 22 21 85 40 20 47 62 21 22 23 86 53 20 48 63 21 22 26 87 54 22 58 65 25 25 30 87 57 22 59 72 25 27 30 88 63 27 67 80 28 28 40 90 67 27 69 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83 42 94 29 98 65 56 53 84 58 94 29 99 67 57 67 87 71 100 48 100 67 72 75 87 103 100 55 11 1 82 77 82 92 105 116 55 120 88 84 88 99 120 180 59 132 93 85 96 113 122 812 812 851 851 851 851 851 906 5B 6A 1A 1B 2A 2B 4A 3A 16-Mar 31-Mar 10-Feb 10-Feb 23-Feb 23-Feb 23-Mar 8-Feb 98 27 8 8 1 2 1 2 24 4 1 0 1 0 10 1 0 1 2 1 2 1 0 21 1 0 1 0 1 1 1 0 1 3 1 3 1 0 22 1 0 1 0 1 2 1 0 1 4 13 1 0 24 1 0 11 1 3 1 0 1 4 1 4 1 1 30 1 0 11 1 3 11 1 4 14 1 1 31 1 1 1 2 1 3 11 1 4 1 5 1 2 35 1 1 1 2 1 3 11 1 5 1 5 1 2 37 1 1 1 2 14 11 1 6 1 6 1 2 40 1 1 12 1 4 1 2 1 7 1 6 1 2 40 1 1 1 2 14 1 2 1 9 17 1 2 42 1 1 1 2 1 4 1 2 1 9 1 7 13 42 1 1 1 3 15 1 2 20 1 8 13 43 1 1 1 3 1 5 1 2 20 1 9 1 3 43 1 1 13 1 5 1 2 20 1 9 1 4 45 1 1 1 3 1 5 1 2 21 20 1 4 45 1 1 1 4 1 5 1 3 22 20 1 5 46 1 1 14 1 6 1 3 22 21 1 5 47 1 1 1 5 1 7 1 3 24 22 1 5 48 1 1 1 6 1 7 1 3 25 26 1 6 50 1 1 1 6 1 9 1 3 25 27 1 6 54 1 1 1 6 20 13 27 27 1 7 56 1 1 1 6 21 1 4 28 27 1 7 56 1 1 17 21 1 4 29 27 1 8 56 1 1 21 22 1 4 30 27 1 8 61 1 1 21 22 1 4 31 29 1 9 62 1 1 21 24 1 5 31 29 20 63 1 2 25 24 1 5 31 30 23 67 1 2 26 24 1 5 32 31 23 68 1 2 26 26 1 5 32 31 23 70 1 2 27 27 1 5 32 36 24 75 12 33 30 1 5 34 41 25 76 1 2 33 31 1 6 35 41 27 78 13 36 32 1 8 35 42 27 82 1 4 36 32 20 37 43 30 83 17 36 34 20 40 44 32 86 1 8 45 37 20 40 46 35 87 20 57 38 20 44 54 36 88 23 69 48 21 45 57 37 90 23 69 56 21 45 60 37 92 24 70 62 25 45 63 38 92 25 70 68 25 46 66 38 94 25 81 70 29 47 70 40 94 26 82 77 30 47 70 48 95 30 89 80 31 48 75 50 96 38 89 83 32 48 77 52 97 41 90 83 33 55 80 72 100 54 97 83 33 70 86 84 100 73 100 84 36 77 90 85 100 77 100 85 36 77 120 97 130 81 100 88 57 88 140 11 0 138 123 906 906 906 938 938 938 938 938 3B 4A 6A 1A 1B 3A 3B 4B 8-Feb 21-Feb 23-Mar 25-Jan 25-Jan 7-Feb 7-Feb 21-Feb 4 9 23 1 2 3 3 1 0 32 8 1 1 1 8 1 8 26 1 2 1 0 33 8 1 1 1 9 23 27 1 2 1 1 34 8 1 1 1 9 24 28 1 3 1 1 35 9 1 2 25 25 29 1 3 1 1 35 1 0 12 26 30 32 1 5 1 1 38 1 0 1 2 2 7 31 35 1 7 1 1 38. 10 1 2 27 33 35 1 7 1 1 39 12 27 36 36 1 7 1 2 40 1 1 1 2 28 37 37 1 8 1 2 40 1 1 1 3 30 39 38 20 1 4 40 1 1 1 3 32 40 39 22 1 5 42 1 1 1 4 34 43 42 24 1 5 43 1 1 1 4 35 43 43 24 1 5 43 1 1 14 38 53 44 25 1 7 45 1 1 1 4 40 59 45 26 1 8 46 1 1 15 45 60 46 27 1 9 47 1 1 1 6 53 61 46 27 1 9 47 1 1 1 6 54 66 47 29 20 50 1 1 1 6 56 67 47 29 21 50 1 1 1 6 58 72 47 32 21 52 1 1 1 6 62 75 48 33 22 53 1 1 1 6 66 75 50 33 22 55 1 1 1 7 66 77 52 34 24 55 1 -i 1 7 69 77 55 34 25 56 1 1 1 7 77 78 58 35 27 59 1 1 17 78 80 58 36 28 59 1 1 21 82 80 63 37 28 60 1 1 22 82 82 63 38 29 62 1 1 23 83 83 67 42 32 64 1 1 23 83 84 67 43 40 64 1 1 24 84 85 67 44 45 65 1 1 24 87 86 70 45 48 66 1 1 24 88 88 70 45 61 67 1 1 25 89 88 72 45 62 68 1 1 26 90 92 72 45 63 70 1 1 28 92 93 73 50 73 70 1 1 32 93 93 75 50 77 70 1 1 32 93 94 75 51 79 70 1 1 32 95 96 80 53 80 72 1 1 36 95 96 81 53 80 78 1 3 36 95 97 82 57 82 82 1 4 37 95 97 82 59 86 84 1 5 42 99 97 89 63 88 85 22 42 100 98 93 66 90 85 45 44 109 104 102 71 91 86 47 54 112 107 105 87 92 86 50 67 125 127 11 0 97 104 91 67 71 139 129 11 0 105 1 1 1 94 67 80 140 137 11 5 145 120 115 125 105 160 143 115 180 130 124 938 938 942 942 942 942 942 942 5A 6A 2A 4A 2B 4B 3A 3B 16-Mar 27-Mar 10-Feb 7-Mar 10-Feb 7-Mar 23-Feb 23-Feb 98 25 6 1 6 6 1 6 1 1 1 1 1 0 1 0 12 1 0 1 0 1 1 1 0 1 0 1 0 1 0 13 1 1 1 3 11 1 1 1 1 1 0 1 0 13 1 1 1 4 1 1 1 1 1 2 11 1 1 1 4 11 1 4 1 1 1 2 1 2 11 1 1 15 1 1 1 4 1 1 12 1 2 11 1 1 1 5 1 2 1 5 11 1 2 1 3 11 1 1 1 6 1 2 1 5 1 1 1 3 1 3 11 1 1 1 6 1 2 15 1 2 1 3 13 1 2 1 2 1 6 1 2 1 5 1 2 1 4 1 3 12 12 17 1 2 1 5 1 2 1 4 1 3 1 2 1 2 1 7 1 2 1 5 1 2 1 4 1 3 12 1 2 1 8 1 2 1 6 1 2 1 4 1 4 12 1 2 21 1 2 1 6 1 2 1 5 14 12 1 2 24 1 2 1 6 1 2 1 5 1 4 1 2 .1 2 24 1 3 1 6 1 3 1 5 1 5 13 1 3 26 13 • 17 13 15 15 1 3 13 26 1 3 17 1 3 1 5 1 5 13 1 3 29 1 3 17 13 1 6 1 5 1 3 15 30 1 3 1 7 1 3 1 7 1 6 14 1 5 30 1 3 1 8 1 4 1 8 1 6 1 4 15 30 1 3 20 1 4 1 9 1 6 14 1 5 33 1 3 20 1 4 20 17 1 4 1 6 34 1 4 20 1 4 20 17 14 1 6 36 1 4 20 1 5 21 17 1 4 1 8 36 1 6 20 1 5 21 20 15 20 37 1 6 21 1 5 21 20 1 5 21 37 1 6 21 1 5 21 21 1 6 21 37 17 21 1 6 22 21 1 8 22 38 17 22 17 23 24 1 8 22 40 1 8 22 1 9 24 25 21 23 42 1 8 24 21 25 27 22 24 42 21 25 23 25 27 24 26 46 22 26 23 26 28 24 28 47 22 28 26 26 28 28 30 52 22 30 26 29 28 38 37 55 24 30 28 30 32 45 47 61 26 31 32 31 34 46 47 61 29 31 35 31 40 47 59 67 37 32 36 32 44 47 86 75 3 8 36 42 37 45 51 86 77 42 36 44 61 52 74 86 82 46 40 51 73 53 77 95 86 57 40 60 79 62 81 96 90 67 41 63 79 77 82 100 97 82 49 83 83 81 92 110 98 85 61 85 83 100 93 11 1 100 90 79 96 85 103 100 121 103 93 101 105 100 115 101 121 105 165 103 107 11 0 140 170 130 11 0 177 150 170 140 450 125 MOLT-4 Cell samples 942 A AA B GG HH II J 6A 7-Feb 23-Mar 25-Jan 8-Mar 25-Jan 22-Mar 22-Mar 23-Mar 24 1 20 2 . 21 22 23 3 1 1 1 6 1 3 1 8 1 0 1 0 1 0 17 1 1 18 1 7 1 8 1 0 1 0 1 0 1 9 1 1 20 1 8 20 1 1 1 0 1 1 20 1 1 21 1 8 24 1 1 1 0 1 1 22 1 1 22 18 24 1 1 1 1 1 1 22 1 1 22 1 8 25 1 1 1 1 1 1 28 1 1 23 1 8 26 1 1 1 1 1 1 30 12 23 1 9 27 1 1 1 1 1 1 30 1 2 23 1 9 28 1 1 1 1 1 1 31 1 2 25 20 28 1 1 1 1 1 1 35 1 2 26 20 32 1 1 1 2 1 2 36 12 29 20 32 1 1 12 1 2 36 1 2 29 20 32 1 2 1 2 1 3 43 12 29 20 35 12 1 2 1 3 46 1 2 29 20 38 1 2 1 2 1 3 47 12 30 21 52 1 2 1 2 1 3 52 1 2 32 21 59 1 2 1 2 1 4 54 12 36 21 60 1 2 1 2 1 4 55 13 40 21 62 1 2 1 3 1 5 58 1 3 42 22 67 1 2 1 3 1 8 60 13 42 23 72 1 3 1 3 1 9 60 14 52 25 72 1 3 13 1 9 65 1 4 53 25 73 1 3 1 3 22 65 14 92 26 82 1 3 13 22 80 1 4 93 28 82 1 3 14 23 88 15 93 31 85 1 3 1 4 26 90 1 5 93 32 88 14 14 27 93 1 6 97 34 89 1 4 1 4 55 95 17 98 38 94 1 4 1 5 75 100 17 100 40 95 1 4 1 5 75 105 17 102 42 9 5 1 4 1 6 88 1 1 5 17 102 42 95 1 4 1 6 92 117 1 7 103 59 96 1 4 1 6 92 120 1 8 103 75 98 1 4 1 6 92 120 1 9 103 82 99 1 4 1 7 92 120 1 9 106 84 99 15 17 93 120 20 106 85 101 1 5 17 95 120 22 106 86 102 1 5 1 7 97 123 24 106 87 103 1 5 54 98 125 25 107 88 103 1 7 54 100 125 25 107 90 104 1 7 86 103 125 26 108 93 104 21 86 103 125 28 11 0 93 105 21 88 105 130 30 110 94 1 1 6 31 88 105 130 37 114 95 120 31 91 105 130 40 120 100 122 33 91 107 130 44 121 100 123 37 91 110 130 82 123 , 100 127 37 91 110 140 100 124 102 130 87 103 11 1 140 180 130 103 133 87 103 120 180 126 JJ K KK L LL M MM N 23-Mar 8-Feb 27-Mar 8-Feb 30-Mar 10-Feb 31-Mar 10-Feb 24 4 25 5 26 6 27 7 13 1 5 1 1 1 2 1 2 1 3 1 1 1 2 13 15 12 1 2 1 2 1 4 1 1 1 3 13 17 1 2 1 3 1 2 1 4 1 2 1 4 13 17 1 2 1 3 1 2 1 5 1 2 1 5 14 1 8 1 2 1 3 1 3 1 9 1 2 1 5 14 1 9 1 2 1 4 1 3 20 1 2 1 5 15 20 1 3 1 4 1 3 22 13 1 6 1 5 21 1 3 15 1 3 24 1 3 1 6 1 6 22 1 3 16 1 4 26 1 3 1 6 1 6 22 1 3 1 6 1 4 27 1 4 1 6 1 6 22 1 4 1 7 1 4 27 1 4 17 1 6 23 1 4 1 7. 1 4 28 14 17 1 7 23 1 4 1 7 1 5 30 1 8 1 7 17 25 1 5 1 7 1 5 31 1 8 1 7 17 25 1 5 1 8 1 6 33 20 1 7 17 27 1 5 1 9 1 6 33 22 1 7 1 9 27 17 20 20 34 24 1 7 1 9 28 17 21 20 36 46 1 8 20 30 1 7 23 30 37 85 1 9 21 30 1 7 25 32 38 100 1 9 21 30 1 9 27 32 49 105 20 23 30 20 28 35 58 105 22 32 3 2 20 28 35 62 11 0 23 32 32 23 30 35 73 112 25 85 32 26 33 41 75 112 26 85 36 26 42 41 79 11 3 27 95 46 26 54 50 81 30 95 55 33 72 50 88 32 99 58 36 82 50 96 33 99 80 40 85 72 97 35 100 93 40 86 72 100 40 100 93 83 87 82 100 44 100 94 98 87 82 101 47 103 96 100 8 8 82 101 48 103 97 100 88 82 103 56 103 98 100 90 82 104 57 103 100 100 90 82 105 71 105 101 100 93 84 11 0 75 106 105 102 93 84 11 0 75 106 11 0 104 94 85 110 75 108 110 104 94 85 11 0 77 108 110 105 96 87 11 0 85 110 11 1 105 96 87 110 88 110 .113 . 105 98 90 110 95 112 115 105 100 90 112 97 112 115 105 101 90 115 98 115 117 110 104 90 115 102 115 120 111 105 95 11 6 105 135 120 117 110 97 117 11 0 135 122 120 115 97 139 11 0 127 NN O CO R S T U V 31-Mar 10-Feb 6-Apr 23-Feb 23-Feb 24-Feb 24-Feb 28-Feb 28 8 29 11 1 2 1 3 14 1 5 12 1 0 1 2 1 1 20 1 2 11 11 1 2 13 1 2 11 25 1 5 1 3 14 12 13 13 1 1 26 1 5 14 1 4 1 2 13 1 3 11 28 1 6 1 4 15 13 1 5 14 1 1 30 1 6 15 1 5 13 17 1 4 11 32 1 6 17 1 6 1 3 1 7 1 6 11 32 1 6 17 17 13 17 1 6 1 1 32 17 1 9 17 1 3 1 9 17 1 4 32 1 7 20 1 7 13 1 9 1 7 1 4 34 17 21 1 7 1 4 20 1 8 1 5 38 1 7 22 1 7 14 21 1 8 1 9 45 1 7 22 1 7 14 21 1 8 24 47 1 7 22 1 8 14 23 1 8 25 48 17 22 1 8 1 5 26 20 26 48 1 8 22 1 9 15 26 20 27 48 20 24 19 1 6 30 20 31 50 20 25 20 1 6 30 20 31 51 20 26 20 18 30 20 32 52 20 28 20 1 8 30 20 32 60 21 30 20 1 8 33 20 36 63 21 35 20 1 8 33 20 36 65 21 37 20 20 37 23 36 66 21 40 20 20 40 23 36 68 22 40 21 21 41 40 37 75 24 42 21 21 60 40 40 77 24 42 22 24 75 41 40 79 27 48 24 24 81 41 41 80 27 50 30 37 90 42 42 82 27 51 30 37 92 42 45 82 28 55 30 42 95 65 46 83 28 70 31 42 97 65 52 85 30 77 31 42 97 69 66 85 30 82 32 42 100 69 75 87 32 82 33 46 103 77 82 87 43 83 35 46 103 77 82 88 81 87 36 61 105 92 89 90 92 90 37 61 105 92 95 90 100 90 52 62 107 97 100 92 100 90 56 62 107 97 100 93 105 92 70 92 107 98 100 95 105 95 75 92 11 0 9 8 100 95 107 99 75 95 110 100 101 97 11 0 103 78 95 11 0 100 101 99 110 105 79 96 110 104 103 103 11 0 11 0 81 96 114 104 105 105 11 2 110 83 100 120 105 106 11 0 115 115 90 100 125 105 107 110 120 115 92 102 130 110 11 0 115 125 117 101 102 139 .11 0 11 0 120 127 120 125 128 w X Y Z -Mar 7-Mar 8-Mar 8-Mar 1 6 1 7 1 8 1 9 1 1 11 1 1 17 1 1 1 1 1 1 20 1 1 1 1 1 2 21 1 1 11 1 2 23 1 1 11 1 3 28 1 1 1 2 1 3 28 12 1 2 14 28 12 1 2 1 4 28 1 2 1 2 1 4 31 1 2 1 2 1 5 32 12 1 2 15 33 12 12 1 6 35 1 2 1 2 1 6 36 12 1 2 17 40 1 2 1 2 1 8 40 1 2 1 2 1 8 41 13 1 2 1 8 47 13 1 2 21 48 13 1 4 25 50 1 3 1 5 26 59 1 3 15 29 67 13 1 5 32 68 14 15 41 70 1 4 1 5 45 70 14 1 5 59 72 14 1 8 76 73 14 26 77 74 1 4 65 80 77 14 68 82 80 1 4 70 82 81 1 5 7 0 85 82 15 73 85 82 15 74 85 85 1 6 75 87 85 1 8 77 88 85 1 8 79 88 87 40 80 89 87 55 80 90 90 65 80 91 92 78 82 93 92 80 83 94 93 80 84 96 95 81 84 96 96 82 85 97 96 83 85 97 96 90 85 98 98 95 87 100 99 97 90 102 100 110 90 11 8 101 125 101 138 105 129 A p p e n d i x G t-test results for each 50 cel l sample 130 vl -n vl J I vl - j .n *>• vl . 0 \3 - s i -0 vl •O jO vl z> jO 3> X> 3> X 3) j i j i j i vl j i vl £>• J I 3> 3) c j\ 3) D \3 J i < 3> . J) 5; i ' r o -»• l\3 PO ro ro -» ro -» r o ro r o ro ro r o ro ->• r o ->• r o r o -* ro ro < n 5' cr v 5.63 CO o CO oo- CO cn CD CO o CT) cn o 00 00 r o r o CD CO O r o 23.4 27.24 1.24 f value o 0.346 0.881 0.018 0.664 0.659 0.71 0.125 0.058 I 0.837 > .152 0.784 0.038 0.691 0.023 0.939 o o 0.457 I 2-tailed probability 3.51 | -0.37 I 0.27 I -3.96 -0.2 -0.81 I -1.51 | 2.09 | 0.98 I 0.43 0.45 -0.58 I 0.53 I 0.08 I -0.22 | -0.46 | 6.03 | -4.87 | -3.53 t value |< Pooled variance estimate CD 00 CD 00 CD 00 CO CO CD 00 CD 03 CD 00 CD oo CD 00 CD oo co 00 CD oo CD oo CD 00 CD 00 CD 00 CD oo CD oo CD 00 degrees of freedorrj Pooled variance estimate I 0.001 1 0.713 I 0.785 o I 0.843 I 0.419 I 0.135 I 0.039 | 0.329 899 0 I j 0.651 0.564 o CD 0.933 I 0.824 I 0.649 o o 0.001 2-tailed probability | 3.51 -0.37 0.27 -3.96 -0.2 -0.81 -1.51 2.09 0.98 I 0.43 IV cn -0.58 0.53 0.08 -0.22 -0.46 6.03 -4.87 -3.53 t value i Seperate variar 65.87 96.26 97.95 88.44 97.62 97.61 97.72 93.56 91.47 97.91 94.1 97.85 90.35 | 97.68 88.99 97.99 1 53.18 I 52.59 I 96.9 degrees of freedomj ice estimate j 0.001 0.713 0.785 o 0.843 0.419 0.135 0.039 0.329 0.668 | 0.651 0.564 o CD 0.933 0.824 0.649 o o 0.001 2-tailed probability 50/1 00 942/4 942/3 942/2 938/3 938/1 906/3 851/2 851/1 812/5 812/3 728/3 728/2 728/1 ISubject ! ro ro ro ro ro - ro ro ro - ro ro - ro - to to to -variable O l I 1.22 I 1.04 I 1.24 •-j CO | 2.09 I 2.68 I 7.41 I 3.27 I 1.24 | 1.08 I 1.16 1.24 f value I ! 0.682 | 0.484 I 0.888 | 0.457 890 0 I | 0.363 | 0.011 I 0.001 I o o 0.456 I 0.798 I 0.602 I 0.453 I 2-tailed probability I Pooled variance estimate I | -0.01 ! 0.02 | 0.15 | 2.65 I 3.09 -0.54 cn -1.43 4.15 2.77 0.77 I 0.22 -0.81 O l t value I Pooled variance estimate I CD *>• CO 03 CO 00 CO 03 CO oo CD 00 CD 00 CD 03 CD 00 CO 00 CD oo CD 00 CO 00 CD oo degrees of freedomj Pooled variance estimate I [ 0.991 I 0.984 | 0.881 600 0 | I 0.003 | 0.589 r 0.137 0.156 O | 0.007 | 0.445 0.825 0.422 I 0.253 | 2-tailed probability | Seperate variance estimate | -0.01 | 0.02 I 0.15 I 2.65 I 3.09 I -0.54 b i I -1.43 4.15 | 2.77 | 0.77 | 0.22 I -0.81 I 1.15 | t value I Seperate variance estimate | 66.15 I 97.03 I 97.96 I 96.9 | 91.89 | 96.37 87.16 oo ro 61.98 76.39 96.9 j 97.87 | .97.45 96.88 degrees of freedom Seperate variance estimate 0.99 I 0.984 0.881 600 0 0.003 0.598 0.137 0.157 o | 0.007 0.445 0.825 0.422 0.253 2-tailed probability 

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