Constructing gene expression based prognostic models to predict recurrence and lymph node metastasis in colon cancer

The main goal of this study is to identify molecular signatures to predict lymph node metastases and recurrence in colon cancer patients. Recent advances in microarray technology facilitated building of accurate molecular classifiers, and in depth understanding of disease mechanisms.;Lymph node metastasis cannot be accurately estimated by morphological assessment. Molecular markers have the potential to improve prognostic accuracy. The first part of our study presents a novel technique to identify molecular markers for predicting stage of the disease based on microarray gene expression data. In the first step, random forests were used for variable selection and a 14-gene signature was identified. In the second step, the genes without differential expression in lymph node negative versus positive tumors were removed from the 14-gene signature, leading to the identification of a 9-gene signature. The lymph node status prediction accuracy of the 9-gene signature on an independent colon cancer dataset (n=17) was 82.3%. Area under curve (AUC) obtained from the time-dependent ROC curves using the 9-gene signature was 0.85 and 0.86 for relapse-free survival and overall survival, respectively. The 9-gene signature significantly stratified patients into low-risk and high-risk groups (log-rank tests, p\u3c0.05, n=73), with distinct relapse-free survival and overall survival. Based on the results, it could be concluded that the 9-gene signature could be used to identify lymph node metastases in patients. We further studied the 9-gene signature using correlation analysis on CGH and RNA expression datasets. It was found that the gene ITGB1 in the 9-gene signature exhibited strong relationship of DNA copy number and gene expression. Furthermore, genome-wide correlation analysis was done on CGH and RNA data, and three or more consecutive genes with significant correlation of DNA copy number and RNA expression were identified. These results might be helpful in identifying the regulators of gene expression.;The second part of the study was focused on identifying molecular signatures for patients at high-risk for recurrence who would benefit from adjuvant chemotherapy. The training set (n=36) consisted of patients who remained disease-free for 5 years and patients who experienced recurrence within 5 years. The remaining patients formed the testing set (n=37). A combinatorial scheme was developed to identify gene signatures predicting colon cancer recurrence. In the first step, preprocessing was done to discard undifferentiated genes and missing values were replaced with k=30 and k=20 using the k-nearest neighbors algorithm. Variable selection using the random forests algorithm was applied to obtain gene subsets. In the second step, InfoGain feature selection technique was used to drop lower ranked genes from the gene subsets based on their association with disease outcome. A 3-gene and a 5-gene signature were identified by this technique based on different missing value replacement methods. Both of the recurrence gene signatures stratified patients into low-risk and high-risk groups (log-rank tests, p\u3c0.05, n=73), with distinct relapse-free survival and overall survival. A recurrence prediction model was built using LWL classifier based on the 3-gene signature with an accuracy of 91.7% on the training set (n=36). Another recurrence prediction model was built using the random tree classifier based on the 5-gene signature with an accuracy of 83.3% on the training set (n=36). The prospective predictions obtained on the testing set using these models will be verified when the follow-up information becomes available in the future. The recurrence prediction accuracies of these gene signatures on independent colon cancer datasets were in the range 72.4% to 88.9%. These prognostic models might be helpful to clinicians in selecting more appropriate treatments for patients who are at high-risk of developing recurrence. When compared over multiple datasets, the 3-gene signature had improved prediction accuracy over the 5-gene signature. The identified lymph node and recurrence gene signatures were validated on rectal cancer data. Time-dependent ROC and Kaplan-Meier analysis were done producing significant results. These results support the fact that the developed prognostic models could be used to identify patients at high-risk of developing recurrence and get an estimate of the survival times in rectal cancer patients

A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithm

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

Graph-based Regularization in Machine Learning: Discovering Driver Modules in Biological Networks

Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel\u27s law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today\u27s scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, and train classification models that serve two goals: 1) class prediction for previously unseen samples; 2) knowledge discovery of the underlying causes of class differences. Application of our methods in genetic studies can help scientist take advantage of existing biological networks, generate diagnosis with higher accuracy, and discover the driver networks behind the differences. We proposed three new graph-based regularization algorithms. Graph Connectivity Constrained AdaBoost algorithm combines a connectivity module, a deletion function, and a model retraining procedure with the AdaBoost classifier. Graph-regularized Linear Programming Support Vector Machine integrates penalty term based on submodular graph cut function into linear classifier\u27s objective function. Proximal Graph LogisticBoost adds lasso and graph-based penalties into logistic risk function of an ensemble classifier. Results of tests of our models on simulated biological datasets show that the proposed methods are able to produce accurate, sparse classifiers, and can help discover true genetic differences between phenotypes

Microarray-based Multiclass Classification using Relative Expression Analysis

Microarray gene expression profiling has led to a proliferation of statistical learning methods proposed for a variety of problems related to biological and clinical discoveries. One major problem is to identify gene expression-based biological markers for class discovery and prediction of complex diseases such as cancer. For example, expression patterns of genes are discovered to be associated with phenotypes (e.g., classes of disease) through statistical learning models. Early hopes that well-developed methods such as support vector machines would completely revolutionize the field have been moderated by the difficulties of analyzing microarray data. Hence, new and effective approaches need to be developed to address some common limitations encountered by current methods. This thesis is focused on improving statistical learning on microarray data through rank-based methodologies. The relative expression analysis introduced in Chapter 1 is the central concept for methodological development where the relative expression ordering (i.e., the relative ranks of expression levels) of genes is investigated instead of analyzing the actual expression values of individual genes. Supervised learning problems are studied where classification models are built for differentiating disease states. An unsupervised learning task is also examined in which subclasses are discovered by cluster analysis at the molecular level. Both types of problems under study consist of multiple classes. In Chapter 2, a novel rank-based classifier named Top Scoring Set (TSS) is developed for microarray classification of multiple disease states. It generalizes the Top Scoring Pair (TSP) method for binary classification problems to the multiclass case. Its main advantage lies in the simplicity and power of its decision rule, which provides transparent boundaries and allows for potential biological interpretations. Since TSS requires a dimension reduction in the training process, a greedy search algorithm is proposed to perform a fast search over the feature space. In addition, ensemble classification based on TSS is also investigated. In Chapter 3, an efficient and biologically meaningful dimension reduction for the TSS classifier is introduced using the publicly available pathway databases. Pre-defined functional gene groups are analyzed for microarray classification. The pathway-based TSS classifier is validated on an extremely large cohort of leukemia cancer patients. Also, the unsupervised learning ability of relative expression analysis is studied and a rank-based clustering approach is introduced to identify molecularly distinct subtypes of breast cancer patients. Based on the clustering results, the TSP classifier is used for predicting the subtypes of individual breast cancer tumors. These rank-based methods provide an independent validation for the current identification of breast cancer subtypes. Overall, this thesis provides developments and validations of statistical learning methods based on relative expression analysis

Classification of clinical outcomes using high-throughput and clinical informatics.

It is widely recognized that many cancer therapies are effective only for a subset of patients. However clinical studies are most often powered to detect an overall treatment effect. To address this issue, classification methods are increasingly being used to predict a subset of patients which respond differently to treatment. This study begins with a brief history of classification methods with an emphasis on applications involving melanoma. Nonparametric methods suitable for predicting subsets of patients responding differently to treatment are then reviewed. Each method has different ways of incorporating continuous, categorical, clinical and high-throughput covariates. For nonparametric and parametric methods, distance measures specific to the method are used to make classification decisions. Approaches are outlined which employ these distances to measure treatment interactions and predict patients more sensitive to treatment. Simulations are also carried out to examine empirical power of some of these classification methods in an adaptive signature design. Results were compared with logistic regression models. It was found that parametric and nonparametric methods performed reasonably well. Relative performance of the methods depends on the simulation scenario. Finally a method was developed to evaluate power and sample size needed for an adaptive signature design in order to predict the subset of patients sensitive to treatment. It is hoped that this study will stimulate more development of nonparametric and parametric methods to predict subsets of patients responding differently to treatment

Machine learning of genomic profiles

Gegenstand dieser Arbeit ist das maschinelle Lernen und seine Anwendung auf genomische Profile. Maschinelles Lernen ist ein Teilbereich der Informatik, der sich mit der Analyse und dem Design von Algorithmen beschaftigt, die Regeln und Muster aus Datensätzen ableiten. Genomische Profile beschreiben Veränderungen der DNA, z.B. der Anzahl ihrer Kopien. Tumorerkrankungen werden oftmals von diesen genomischen Veränderungen hervorgerufen. Es werden verschiedene Verfahren des maschinellen Lernens auf ihre Anwendbarkeit in Bezug auf genomische Profile untersucht. Des Weiteren wird eine Verlustfunktion für Überlebenszeitdaten entworfen. Anschließend wird ein analytischer Bezugsrahmen entwickelt, um Aberrationsmuster zu finden, die mit einer speziellen Tumorerkrankung assoziiert sind. Der Bezugsrahmen umfaßt die Vorverarbeitung, Merkmalsselektion und Diskretisierung von genomischen Profilen sowie Strategien zum Umgang mit fehlenden Werten und eine mehrdimensionale Analyse. Abschließend folgen das Training und die Analyse des Klassifikators. In dieser Arbeit wird weiterhin eine Erklärungskomponente vorgestellt, die wichtige Merkmale für die Klassifikation eines Falles identifiziert und ein Maß für die Richtigkeit einer Klassifikation liefert. Solch eine Erklärungskomponente kann die Basis für die Integration eines Klassifikators , z.B. einer Support-Vektor-Maschine, in ein entscheidungsunterstützendes System sein. Die im Rahmen dieser Arbeit entwickelten Methoden wurden erfolgreich zur Beantwortung von biologischen Fragestellungen wie der frühen Metastasierung oder der Mikrometastasierung angewandt und führten zur Entdeckung bisher unbekannter Tumormarker. Zusammenfassend zeigen die Ergebnisse der vorliegenden Arbeit, dass Verfahren des maschinellen Lernens zum Erkenntnisgewinn in Bezug auf genomische Veränderungen beitragen und Möglichkeiten zu einer weiteren Verbesserung der Therapie für Tumorpatienten aufzeigen