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Chemometric methods for microarray data analysis and their application to leukemia subtype identification

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Analytische Chemie Chemometric methods for microarray data analysis and their application to leukemia subtype identification Inaugural-Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften
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Analytische Chemie Chemometric methods for microarray data analysis and their application to leukemia subtype identification Inaugural-Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften im Fachbereich Chemie und Pharmazie der Mathematisch-Naturwissenschaftlichen Fakultät der Westfälischen Wilhelms-Universität Münster vorgelegt von Eric Andrés Frauendorfer aus Caracas Dekan: Prof. Dr. J. Leker Erster Gutachter: Prof. Dr. K. Cammann Zweiter Gutachter: Prof. Dr. J. von Frese Tag der mündlichen Prüfungen: 12., 14., Tag der Promotion: Content I Table of Contents 1 Introduction Aims & Scope Theory Nucleic Acid: Structure and Function Gene Expression Cancer Overview Leukemia Introduction Diagnosis Treatment and the significance of molecular differences of ALL subtypes DNA Biosensors Definition and Overview DNA Microarray Technology Overview Spotted microarrays Microarrays created by photolithography Medical Applications of DNA-biosensors Affymetrix Microarrays Physical construction Calculation of gene expressions Chemometrics Introduction The role of chemometrics in microarray experiments Pre-processing Cluster analysis hierarchical clustering Principal component analysis Introduction Eigenvalues, eigenvectors Calculations in Principal Component Analysis Cross Validation Support Vector Machines (SVM) Gene selection Methods Gene shaving Significance analysis of microarrays (SAM) Predication Analysis of Microarrays (PAM) Fisher s Ratio Gene expression summary algorithms Affymetrix MicroArraySuit (MAS) 5.0 algorithm MAS, perfect match only Li Wong Model RMA Model Method development Databases Introduction Microarray Data Management System... 39 Content II 4.2 Analysis of the raw data provided by the scanner Artifact detection Medianchip images for artifact detection Artifact detection algorithm Background correction Background in Affymetrix Microarrays Background estimation using the checkerboard pattern Interpolation using the Auto-leveling Method (ALM) Thin-plate interpolation Theory Background subtraction Application of a scaling factor Probe Sequence Development Introduction Process of probe sequence development Discussion of signal processing methods Analysis of Leukemia Data Introduction Data Source and Composition Preprocessing Quality Aspects Time of measurement Homogeneity of Chips GAPDH 3 / 5 Ratio Present Calls Number of Affymetrix Outliers and Masked Cells Relation between sample class and sample quality Gene Selection Introduction Gene selection using Fisher ratio calculations Gene selection using Gene shaving Gene selection using Significance Analysis of Microarrays (SAM) Gene selection using Prediction Analysis of Microarrays (PAM) Separation of sample subgroups using selected genes Selection of genes for differentiation of the Other-subgroups Comparison of different selection methods Sample Classification Introduction Main Classifier BCR-ABL classifier TEL-AML classifier Novel-group Classifier Final sample subgroup classification Feature selection bias and true accuracies Effects of using different gene expression summary algorithms Discussion of leukemia data analysis... 99 Content III 6 Summary and Outlook Literature Index Abbreviations IV Abbreviations A ACS ALL ALM AML ASR C CEL CELL CLL CML CVUA DBMS DML DNA EST FDA FISH G GAPDH GPL ICB IM IVAT LOO MAS MDMS MIAME MM MRD NIH NSF adenine American Cancer Society acute lymphocytic leukemia auto leveling method acute myeloid leukemia analyte-specific reagent cytosin data format of Affymetrix a feature on an Affymetrix microarray chronic lymphocytic leukemia chronic myeloid leukemia Chemischen und Veterinär Landesuntersuchungsamt database management system data manipulation language desoxyribonucleic acid expressed sequence tag Food and Drug Administration fluorescence in situ hybridisation guanine glyceraldehyde-3'-phosphate dehydrogenase general public license Institut für Chemo- und Biosensorik ideal mismatch in vitro analytical test leave one out Microarray Suite microarray data management system Minimum Information About a Microarray Experiment mismatch minimal residual disease National Institutes of Health National Science Foundation Abbreviations V mrna PAM PC PCA PCR PM PMA QCM R RMA RNA SAM SD SNP SPR SQL SVM T U U133A and B messenger RNA Prediction analysis of microarrays principal component principal component analysis polymerase chain reaction perfect match premarket approval quartz crystal microbalance a statistics program robust multi chip analysis ribonucleic acid significance analysis of microarrays standard deviation single nucleotide polymorphism surface plasmon resonance structured query language support vector machine thymin uracil Affymetrix microarray types Introduction 1 1 Introduction Cancer is the second leading cause of death in the western world after heart disease. Classical cancer treatments, including radiation- and chemotherapy, can have many unwanted side effects, often weakening the patient tremendously and reducing the patient s quality of life [1, 2]. The efficiency of drugs used in chemotherapy, and therefore also the amount of the active agent needed, is greatly influenced by the molecular and biochemical properties of the cancer to be eradicated. Different cancer subtypes contain their own subset of abnormalities in the genetic code which change signaling pathways, create proteins in wrong amounts or even proteins lacking any useful structure [3]. It is therefore essential to choose the right medication for a patient with a certain type of cancer to achieve best results, and also to minimize the amount of the drug that has to be administered. The overall rate of survival has risen constantly due to improvements in diagnosis and treatment made possible by the advances of cell, molecular, computational, developmental, and structural biology as well as biochemistry, genetics, molecular biophysics, bioinformatics and chemometrics. These once separate fields of activity have molten together in the last couple of years, urged by the need for an interdisciplinary approach to understand the complex patterns behind cancer cell biology. One example for this is the molecular characterization of a tumor to determine which drug or combination of therapies is the most effective for a patient. Research is done with state of the art DNA microarrays [4] (chapter 3.4), increasing the knowledge of the genetic abnormalities responsible for certain subtypes of cancer. This information can then be used to design small, affordable diagnostic microarrays for medical applications. The first DNA based diagnostic biosensors will be approved for use in these fields in the very near future (chapter 3.4.5). These new devices have the promise of helping to further increase the effectiveness of anti-cancer therapies and to increase overall survival rates [5-7]. Once a cancer type is recognized, it has to be treated with the right drug. The use of classical chemotherapy drugs including the classical DNA alkylating agents like cis-platin or triethylmelanine, the antimetabolites like pyrimidin- or pyrin-analogues and enzymes like L- asparaginase is often followed by many side effects as these drugs can also affect normal cells. When cis-platin, the most used chemotherapy agent, enters the cell nucleus, the chloro ligands are substituted by two adjacent guanine bases on a DNA strand. This makes the DNA duplex bend and unwind at the site of cisplatin attachment. The high-mobility-group domain (HMG) proteins then become attached to the structural damage, hereby preventing cancer cell replication [8]. As was reviewed in the Current Medicinal Chemistry Anti Cancer Agents Introduction 2 journals (e.g. [9-11]), targeted therapies using novel drugs reduce side effects as scientists try to design these drugs so that they target properties unique to cancer, e.g. they disrupt certain signaling pathways [12], and thus avoid normal cells. One example is Gleevec, a drug designed to work against a certain, deadly type of leukemia (CML). It was introduced by Novartis in 2001 and revolutionized the treatment of CML. Gleevec works as a signal transduction inhibitor that interferes with cell signaling pathways in tumor development, blocking the abelson-tyrosinkinase without interfering with other tyrokinases abundant in all cells. Other so called smart drugs followed, most of them far less successful. Iressa for example, which received approval from the U.S. Food and Drug Administration in 2003, is a drug targeted at the epidermal growth factor receptor (EGFR), a protein involved in cancer cell growth. However, chemotherapy together with Iressa did not achieve better results than chemotherapy alone during phase III of clinical trials. This example, among others, has shown that a lot of research is still needed to truly understand the complex machinery of cancer creation and proliferation [13, 14]. The role of chemometrics and bioinformatics in this research is to design and select optimal measurement procedures and experiments and to maximize the information which can be extracted from data. The application of a multi step analysis of the very complex multivariate data gathered in microarray experiments has to be done in an optimal way to obtain informative results that can be used to interpret the biological background of the data. The elements which are defining the speed at which progress is made in the bioanalytics sector are now the analysis and interpretation of this complex data and far less often technical reasons [15]. The optimal application of chemometrics is important during the research, the development and the design of DNA biosensors as well as during analysis of actual samples from patients as it should never be forgotten that the data is gathered using tumour samples obtained from individual cancer patients with their own lives and hopes. Aims & Scope 3 2 Aims & Scope The aim of this work was to create and enhance chemometric methods for the analysis of microarray data, to apply these methods, in order to obtain information on relevant genes useful for the characterization of different cancer subtypes and to use these genes for the creation of classifiers for the discrimination of unknown cancer samples. The main focus was put on the development of quality control routines for Affymetrix microarrays, which are the best developed DNA biosensor platform and will probably be the first technology applied in real world diagnostics in the very near future. Routines include quality control procedures for the processing of inhomogeneous background signals and procedures for obtaining information on the genetic traits of pediatric acute lymphocytic leukemia. Several hundred Affymetrix U133 microarrays were analyzed to create novel methods for the automatic detection of signal artifacts and to process these chips to remove inhomogeneous signal background and differences in signal scaling. These methods were applied on replicate measurements to show their efficiency in raising the quality of the obtained signals. Further, the methods were tested on microarrays with known and unknown defects to evaluate their ability to detect them. Different tools have been created for analysis, management and processing of data. A tool was created to facilitate the design of probe sequences for a custom made microarray. A pediatric leukemia dataset was analyzed with the intention of selecting genes best suited for discriminating different leukemia subtypes. This process was also used to compare different gene selection algorithms as well as different methods for the calculation of gene specific expression signals. Method development 4 3 Theory 3.1 Nucleic Acid: Structure and Function Nucleic acids as carriers of the genetic information can be subdivided into two classes: deoxyribonucleic acid (DNA) with the sole purpose of storing information; ribonucleic acid (RNA) with a role in gene-expression and protein biosynthesis. The genomic DNA is located nearly solely in the chromosomes of the nucleus of eukaryotic cells, whereas RNA can be found in the nucleus as well as in the cytoplasm [16]. DNA is an unbranched biopolymer composed of nucleotides and can reach considerable length. Each nucleotide consists of sugar (deoxyribose), a nitrogen containing base attached to the sugar, and a phosphate group. There are four different types of nucleotides found in DNA, differing only in the nitrogenous base. DNA can consist of the two purin-bases adenine (A) and guanine (G) and the two pyrimidine-bases cytosine (C) and thymine (T). RNA contains ribose as sugar component, and the structurally similar uracile (U) instead of thymine. The polymer is created by bondage of the sugar groups through the phosphate groups. The genetic information of the organism is stored in the sequence of the four bases, read in the same direction as it was synthesized, that is, from the 5 to the 3 end. DNA is synthesized by recurrent attachment of an incoming deoxynucloside triphosphate to the free 3 OH-group of the growing DNA sequence. DNA has a double helix structure in a natural surrounding, in which two complimentary DNA single strands are wound around the same axis. Fig. 3.1 DNA structure [from chromosome.com] The resulting macromolecule has a polar and a negatively charged surface created by the outside lying sugar-phosphate backbone. The interactions between the two DNA strands are Method development 5 based on hydrogen bonds between the nucleic acid bases adenine and thymine on one hand, and guanine and cytosine on the other. 3.2 Gene Expression Functional regions of the DNA are called genes, most genes coding proteins. Each amino acid component of a protein is coded by three base pairs in the DNA (codon). The information of a gene is transferred into a messenger RNA (mrna). The RNA is then transformed multiple times and transferred to the ribosomes. Here it is read and the proteins are synthesized. Proteins are the building blocks of the organic tissues and fluids; they provide most of the molecular machinery. Different genes are expressed in different cell and tissue types and at different developmental stages. Cells at certain locations thus produce the proteins they need at certain times [16]. Fig. 3.2 Transcription of information from DNA to mrna and synthesis of polypeptides in the ribosome [source: Genentech]. Analysis of variations in gene expression can lead to an understanding of disease states, targeting of drugs to specific cells, tissues or individuals, development of agricultural products, etc. [17, 18]. Gene expression can be quantified by using microarrays in order to analyze simultaneously the amount of a large multitude of different mrna in a sample. Method development 6 This can then be the basis to gain more information on certain tissue types like tumors [19, 20]. More information on nucleic acid analytics is given by Haberhausen et al. [21], Pingoud and Urbanke [22], and Christopoulos [23]. 3.3 Cancer Overview The word cancer is a generic term describing more than 100 forms of the disease that can arise in most tissues [24]. Five general subgroups can be defined [American Cancer Society, ACS]: Carcinoma - a tumor derived from epithelial cells - those cells that line the surface of the skin and the organs, also the surfaces of the digestive tract and the airways. This is the most common cancer type and represents about 80-90% of all cancer cases reported. Sarcoma - a tumor derived from muscle, bone, cartilage, fat or connective tissues. Leukemia - a cancer derived from blood cells or their precursors. The cells that form both white and red blood cells are located in the bone marrow. Lymphoma - a cancer of bone marrow derived from cells that affect the lymphatic system. Myelomas - a cancer involving the white blood cells responsible for the production of antibodies. Each form of cancer can have very different properties, although the processes through which these diverse tumors arise are quite similar [25, 26]. The human body consists of 30 trillion cells which work together, they only proliferate when get the signal to do so. It is essential for certain tissues to retain their size and properties in order to work in unison with the rest of the body. Cancer cells, on the other hand, leave this strict ruling, following their own schemes for reproduction. They can leave their site of origin and invade other tissues, often disrupting their function and thus becoming lethal [27]. Every part of the body can develop a primary Method development 7 tumor, the most prominent regions being the lung (through smoking), the breast in women and the prostate in men (see figure 3.3). The probability of developing a certain cancer type is linked to many different factors like environment, lifestyle, genetic makeup and especially age. Incidence rates of cancer diagnosis in 1998 in Europe per persons Brain, nervous system 8.82 Mouth, oral cavity Brain, nervous system 5.97 Mouth, oral cavity Lung Lung Stomach Bladder Testis 4.81 Prostate Colon / Rectum Breast Stomach 8.56 Bladder Uterus / Ovary Colon / Rectum Melanoma of the skin Melanoma of the skin Lymphoma Lymphoma Leukemia Leukemia Source: The European Commission Health Monitoring Program Fig. 3.3 Incidence rates of certain cancers diagnosed in Europe. Rates are calculated by division of the number of new cancer cases observed during the year 1998 by the corresponding number of people in the population at risk. Results expressed as an annual rate per persons at risk [28]. These facts have been known for a long time, but the research during the last couple of years has shed light into the molecular sources of these characteristics. It is now common knowledge that the malignant transformation of a cell is the product of an accumulation of mutations of certain genes within it. If a gene is mutated, its function may be disrupted; it may be present at the wrong numbers producing a wrong amount of a protein; it may be located at the wrong area of the genome or it may be missing completely. Proto-oncogenes encourage the proliferation of the cell, tumor suppressor genes inhibit it. Together, these two classes of genes keep the cell in balance, making it a functioning part of the body [28, 29]. Mutated proto-oncogenes can become carcinogenic oncogenes, driving excess multiplication. Tumor suppressor genes can be switched off by mutation, also contributing to a forming malignancy. At least half a dozen growth-regulating genes have to be affected so that a malignant development can start. Method development 8 Signals from outside are forwarded into the cell by means of a pathway built by many different genes. As members of this chain become deregulated, the net signal the cell receives can be distorted, leading, for example, to excessive multiplication. One example is the ras family of genes which are members of one certain signaling chain. Hyperactive ras proteins are found in about a quarter of all human tumors [30, 31]. Cell surface with receptor TCR / CD3 Signal Pathway raf ERK 1/2 ras MEK Activation of IFNγ Fig. 3.4 Signal cascade from a cell receptor through a pathway built by many different genes down to the activation of interferon gamma. Hyperactive ras proteins are found in a quarter of all human tumors [Novartis] Signals between cells can be forwarded by means of certain proteins acting as growth-factors, docking onto the receptors of cells surrounding the one emitting these molecules. The myc proteins are normally only created when growth-factors dock onto the cell. But many cancers, especially those of the tissues producing blood, have a constantly high level of myc which then urges the cell to proliferate. Oncogenes can also stimulate a cell into producing too much of these growth-factors, thus affecting all cells surrounding it. Examples are sarcomas and gliomas. Genes creating the receptors of a cell may also be mutated, formi
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