Glioblastoma (GBM, WHO grade IV astrocytoma) is among the most common adult brain tumors and one that is invariably fatal. GBM is classified as either primary (de novo) or secondary in origin. Secondary GBMs are derived from previously lower grade (WHO grades II or III) gliomas. While secondary GBMs present with similar clinical characteristics as their primary counterparts, the molecular pathways involved in their pathogenesis distinguish the two and have functional consequences for their behavior. Although a large number of histologic markers are routinely utilized to distinguish primary from secondary GBM, advances in genomic and bioinformatics techniques have drastically improved classification of high-grade gliomas and our understanding of the molecular pathways that influence tumor behavior and response to treatment. The significant influence of molecular identity on tumor behavior has been recognized by the most recent WHO classification of CNS tumors, wherein specific molecular markers have been integrated as part of tumor subtype identification process, as a supplement to traditional histological analysis. Indeed, the change heralds a new era for neuro-oncology, one that is moving toward targeted therapeutics as part of the standard of care. Thus, a comprehensive grasp of this diverse landscape is necessary. In this chapter, we provide an overview of our latest understanding of the molecular diversity of GBM, specifically as it pertains to primary and secondary GBMs, and how it influences prognostication and therapeutic decision-making.
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.