Bioinformatics Tools And Web Server For Cancer Biomarker Development Volume Ii

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This book discusses the application of bioinformatics in cancer disease management. It covers general aspects of cancer as a disease but also as a success story in the translation of omics data in clinical settings. It provides an overview of the specific applications of bioinformatics tools in cancer epidemiology, prevention, and screening and in the identification of novel genetic and molecular biomarkers involved in cancer development. This is accomplished through the inclusion of numerous examples of the use of bioinformatics in precision oncology.

Latest generation sequencing revolutionizes the fields of cancer research and oncology. This follow-up volume focuses more extensively on single cell sequencing of cancer and trials in drug resistance. Another exciting feature is the bioinformatics tools given, that can be used on cancer genome studies. Scientists around the world are attempting to find the root cause of cancer. A reasonable cancer treatment plan and potential cure is more optimistic now with the unfolding of the cancer genome. The collective knowledge of how to leverage next generation sequencing in cancer research is paving the way. The important information provided in this volume will move the field forward in developing novel targeted cancer therapies.

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.

This Research Topic is part of a series with, "Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research - Volume I" (https://www.frontiersin.org/research-topics/13816/bioinformatics-analysis-of-omics-data-for-biomarker-identification-in-clinical-research) The advances and the decreasing cost of omics data enable profiling of disease molecular features at different levels, including bulk tissues, animal models, and single cells. Large volumes of omics data enhance the ability to search for information for preclinical study and provide the opportunity to leverage them to understand disease mechanisms, identify molecular targets for therapy, and detect biomarkers of treatment response. Identification of stable, predictive, and interpretable biomarkers is a significant step towards personalized medicine and therapy. Omics data from genomics, transcriptomics, proteomics, epigenomics, metagenomics, and metabolomics help to determine biomarkers for prognostic and diagnostic applications. Preprocessing of omics data is of vital importance as it aims to eliminate systematic experimental bias and technical variation while preserving biological variation. Dozens of normalization methods for correcting experimental variation and bias in omics data have been developed during the last two decades, while only a few consider the skewness between different sample states, such as the extensive over-repression of genes in cancers. The choice of normalization methods determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or preprocessing strategies may promote biomarker identification and facilitate clinical decision-making.

This book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics. The cancer has been known as a heterogeneous disease categorized in several different subtypes. According to WHO’s recent report, cancer is a leading cause of death worldwide, accounting for over 10 million deaths in the year 2020. Therefore, its early diagnosis, prognosis, and classification to a subtype have become necessary as it facilitates the subsequent clinical management and therapeutics plan. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning, and nature-inspired algorithms, have been widely utilized in various aspects of oncology research, viz. diagnosis, prognosis, therapeutics, and optimized clinical management. Appreciable progress has been made toward the understanding the hallmarks of cancer development, progression, and its effective therapeutics. However, notwithstanding the extrinsic and intrinsic factors which lead to drastic increment in incidence cases, the detection, diagnosis, prognosis, and therapeutics remain an apex challenge for the medical fraternity. With the advent in CI-based approaches, including nature-inspired techniques, and availability of clinical data from various high-throughput experiments, medical consultants, researchers, and oncologists have seen a hope to devise and employ CI in various aspects of oncology. The main aim of the book is to occupy state-of-the-art applications of CI methods which have been derived from core computer sciences to back medical oncology. This edited book covers artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary algorithms, various nature-inspired algorithms, and hybrid intelligent systems which are widely appreciated for the diagnosis, prognosis, and optimization of therapeutics of various cancers. Besides, this book also covers multi-omics exploration, gene expression analysis, gene signature identification of cancers, genomic characterization of tumors, anti-cancer drug design and discovery, drug response prediction by means of CI, and applications of IoT, IoMT, and blockchain technology in cancer research.

Offering focused, practical, and up-to-date content on immunotherapies used to treat a wide variety of cancer types, Clinical Immuno-Oncology covers the basic cellular and molecular mechanisms involved in cancer initiation and progression. The text reviews the development of tumor specific antigens and the challenges of developing cancer immunotherapies. Every effort is made to relate the fundamental aspects of host immunity to the efficacy of current immunotherapies and how they work in conjunction with the immune system to combat cancer. This foundational resource explains the basics of cancer immunotherapy, discusses best practices, and provides recommendations from experienced clinicians in the field. Dr. John E. Niederhuber and a team of expert contributing authors provide clinically focused information essential for integrating these new therapies into practice—making this an ideal resource for fellows, practitioners, and other cancer team members in medical oncology, radiation oncology, cancer surgery, immunology, and cancer research. Offers balanced and synthesized content on clinical science, clinical and translational research, and evidence-based practice for cutting-edge immunotherapies. Discusses clinical applications of immunotherapies in common types of cancer including melanoma, breast cancer, pancreatic cancer, colon and rectum cancer, prostate cancer, leukemias, and hematologic malignancies. Includes dedicated chapters on immune responses, innate immunity, active immunization against cancer, and cell-based therapies by leading experts. Covers key topics such as cancer immunotherapy in the presence of COVID-19, immunotherapeutics, new findings concerning the role of the microbiome in immunotherapy, advances in engineered cell-based therapies, and active immunization against cancer.