Computational Analyses Methods And Tools Supporting Cancer Biomarker Identification And Targeted Therapy Development

Over time, much has been done in attempt to understand the various causes and complex molecular mechanisms of cancer, yet it still represents one of the leading causes of mortality worldwide. Fortunately, cancer therapeutics have evolved, from broad chemotherapies with multiple harsh side effects to molecular missiles which target specific cancer causing genes, leaving a patient's normal cells largely untouched. Similarly, cancer detection strategies and prognosis methods have also advanced, allowing doctors and patients to better manage and control the disease. The main challenge currently is to identify those genes that are specific markers for a particular cancer and can inform prognosis and those that may be "targeted therapies". This can be accomplished most rapidly through the use of large-scale cancer genomic datasets and sophisticated integrative analyses, methods, and tools to detect and prioritize candidate genes and biomarkers. As such, the goal of this work is to develop analyses, methods, and frameworks that benefit the translational research community by identifying and prioritizing genes for biomarker and drug development. Specifically, using integrative approaches on The Cancer Genome Atlas (TCGA) and various datasets from Gene Expression Omnibus (GEO), we perform analyses to identify a marker of survival and Epithelial-mesenchymal transition (EMT) in ovarian serous adenocarcinoma and a 5-gene signature of survival and molecular subtype in pancreatic ductal adenocarcinoma. Additionally, we highlight associated oncogenic pathways and suggest potential therapeutic strategies in these analyses. In order to improve detection of these survival markers we also evaluate a suite of techniques used commonly in the literature for survival analysis and determine best practices when using RNA-Sequencing data. Finally, we develop an application that allows researcher to access cancer 'big data' and apply their experience and domain expertise alongside the application logic of the tool to identify survival markers, therapeutic avenues, and genes that may represent an 'Achilles heel' for a set of tumors. This undertaking involves many different facets of bioinformatics, including statistical methods of analysis, high-performance computing, graph theory, web programming, and UI/UX interaction, as well as domain expertise in cancer target discovery. While there is much activity in the translational cancer informatics domain, the current study adds to the wealth of knowledge and tools in the community and presents another foothold to gain novel insights into this devastating disease.

Nowhere is the explosion in comprehensive genomic testing more evident than in oncology. Multiple consensus guidelines now recommend molecular testing as the standard of care for most metastatic tumors. To aid in the advancement of this rapidly changing field, we intend this Special Issue of JPM to focus on technical developments in the genomic profiling of cancer, detail promising somatic alterations that either are, or have a high likelihood of being, relevant in the near future, and to address issues related to the pricing and value of these tests.The last few years have seen the cost of molecular testing decrease by orders of magnitude. In 2018, we saw the first “site-agnostic” drug approvals in cancer (for microsatellite unstable cancer (PD-1 inhibitors) and NTRK-fusions (TRK inhibitors)). Research on targetable mutations, determination of genetic “signatures” that can use multiple individual genes/pathways, development of targeted therapy, and insight into the value of new technology remains at the cutting edge of research in this field. We are soliciting papers that present new technologies to assess predictive biomarkers in cancer, original research (pre-clinical or clinical) that demonstrates promise for particular targeted therapies in cancer, and articles that explore the clinical and financial impacts of this paradigmatic shift in cancer diagnostics and treatment.

This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.

Discover how biomarkers can boost the success rate of drug development efforts As pharmaceutical companies struggle to improve the success rate and cost-effectiveness of the drug development process, biomarkers have emerged as a valuable tool. This book synthesizes and reviews the latest efforts to identify, develop, and integrate biomarkers as a key strategy in translational medicine and the drug development process. Filled with case studies, the book demonstrates how biomarkers can improve drug development timelines, lower costs, facilitate better compound selection, reduce late-stage attrition, and open the door to personalized medicine. Biomarkers in Drug Development is divided into eight parts: Part One offers an overview of biomarkers and their role in drug development. Part Two highlights important technologies to help researchers identify new biomarkers. Part Three examines the characterization and validation process for both drugs and diagnostics, and provides practical advice on appropriate statistical methods to ensure that biomarkers fulfill their intended purpose. Parts Four through Six examine the application of biomarkers in discovery, preclinical safety assessment, clinical trials, and translational medicine. Part Seven focuses on lessons learned and the practical aspects of implementing biomarkers in drug development programs. Part Eight explores future trends and issues, including data integration, personalized medicine, and ethical concerns. Each of the thirty-eight chapters was contributed by one or more leading experts, including scientists from biotechnology and pharmaceutical firms, academia, and the U.S. Food and Drug Administration. Their contributions offer pharmaceutical and clinical researchers the most up-to-date understanding of the strategies used for and applications of biomarkers in drug development.

Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

This book comprehensively summarizes the biology, etiology, and pathology of ovarian cancer and explores the role of deep molecular and cellular profiling in the advancement of precision medicine. The initial chapter discusses our current understanding of the origin, development, progression and tumorigenesis of ovarian cancer. In turn, the book highlights the development of resistance, disease occurrence, and poor prognosis that are the hallmarks of ovarian cancer. The book then reviews the role of deep molecular and cellular profiling to overcome challenges that are associated with the treatment of ovarian cancer. It explores the use of genome-wide association analysis to identify genetic variants for the evaluation of ovarian carcinoma risk and prognostic prediction. Lastly, it highlights various diagnostic and prognostic ovarian cancer biomarkers for the development of molecular-targeted therapy.

Many people naturally assume that the claims made for foods and nutritional supplements have the same degree of scientific grounding as those for medication, but that is not always the case. The IOM recommends that the FDA adopt a consistent scientific framework for biomarker evaluation in order to achieve a rigorous and transparent process.

Genomic sequencing technologies have augmented the classification of cancer beyond tissue of origin and towards a molecular taxonomy of cancer. This has created opportunities to guide treatment decisions for individual patients with cancer based on their cancer’s unique molecular characteristics, also known as precision cancer medicine. The purpose of this text will be to describe the contribution and need for multiple disciplines working together to deliver precision cancer medicine. This entails a multi-disciplinary approach across fields including molecular pathology, computational biology, clinical oncology, cancer biology, drug development, genetics, immunology, and bioethics. Thus, we have outlined a current text on each of these fields as they work together to overcome various challenges and create opportunities to deliver precision cancer medicine. As trainees and junior faculty enter their respective fields, this text will provide a framework for understanding the role and responsibility for each specialist to contribute to this team science approach.

This integrated book covers the entire spectrum of cancer biomarkers in development and clinical use. Predictive and prognostic markers are explored in the context of colon cancer, breast cancer, lung cancer, prostate cancer, and GIST. International experts provide insight into toxicity markers and surrogate markers. Attention is also given to biomarker assay development, validation, and strategies. A powerful tool for determining decisions on therapy, selecting drug regimens, monitoring the efficacy of treatment, and performing individualized surveillance, biomarkers represent the forefront of cancer research and treatment. As these technologies become increasingly available for clinical use, this book will be an essential resource for oncologists and translational researchers.

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning