Systematic Identification Of Novel Diagnostic And Prognostic Tumor Biomarkers Based On Multi Omics Data Analysis Of Solid Tumors

Biomarkers are of critical medical importance for oncologists, allowing them to predict and detect disease and to determine the best course of action for cancer patient care. Prognostic markers are used to evaluate a patient’s outcome and cancer recurrence probability after initial interventions such as surgery or drug treatments and, hence, to select follow-up and further treatment strategies. On the other hand, predictive markers are increasingly being used to evaluate the probability of benefit from clinical intervention(s), driving personalized medicine. Evolving technologies and the increasing availability of “multiomics” data are leading to the selection of numerous potential biomarkers, based on DNA, RNA, miRNA, protein, and metabolic alterations within cancer cells or tumor microenvironment, that may be combined with clinical and pathological data to greatly improve the prediction of both cancer progression and therapeutic treatment responses. However, in recent years, few biomarkers have progressed from discovery to become validated tools to be used in clinical practice. This Special Issue comprises eight review articles and five original studies on novel potential prognostic and predictive markers for different cancer types.

Expert laboratory and clinical researchers from around the world review how to design and evaluate studies of tumor markers and examine their use in breast cancer patients. The authors cover both the major advances in sophisticated molecular methods and the state-of-the-art in conventional prognostic and predictive indicators. Among the topics discussed are the relevance of rigorous study design and guidelines for the validation studies of new biomarkers, gene expression profiling by tissue microarrays, adjuvant systemic therapy, and the use of estrogen, progesterone, and epidermal growth factor receptors as both prognostic and predictive indicators. Highlights include the evaluation of HER2 and EGFR family members, of p53, and of UPA/PAI-1; the detection of rare cells in blood and marrow; and the detection and analysis of soluble, circulating markers.

This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations

This book is a printed edition of the Special Issue "Diagnostic, Prognostic and Predictive Biomarkers in Prostate Cancer" that was published in IJMS

Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.