The Heterogeneity Of Cancer Metabolism

This open access volume will introduce recent discoveries in cancer metabolism since the publication of the first edition in 2018, providing readers with an up-to-date understanding of developments in the field. Genetic alterations in cancer, in addition to being the fundamental drivers of tumorigenesis, can give rise to a variety of metabolic adaptations that allow cancer cells to survive and proliferate in diverse tumor microenvironments. This metabolic flexibility is different from normal cellular metabolic processes and leads to heterogeneity in cancer metabolism within the same cancer type or even within the same tumor. In this book, the authors delve into the complexity and diversity of cancer metabolism and highlight how understanding the heterogeneity of cancer metabolism is fundamental to the development of effective metabolism-based therapeutic strategies for cancer treatment. Deciphering how cancer cells utilize various nutrient resources will enable clinicians and researchers to pair specific chemotherapeutic agents with patients who are most likely to respond with positive outcomes, allowing for more cost-effective and personalized cancer treatment. This book has four major parts. Part one will cover the basic metabolism of cancer cells, followed by a discussion of the heterogeneity of cancer metabolism in part two. Part three addresses the relationship between cancer cells and cancer-associated fibroblasts, and the new part four will explore the metabolic interplay between cancer and other diseases. This new section makes the book unique from other texts currently available on the market. The second edition will be useful for cancer metabolism researchers, cancer biologists, epidemiologists, physicians, health care professionals in related disciplines, policymakers, marketing and economic strategists, among others. It may also be used in courses such as intro to cancer metabolism, cancer biology, and related biochemistry courses for undergraduate and graduate students.

Genetic alterations in cancer, in addition to being the fundamental drivers of tumorigenesis, can give rise to a variety of metabolic adaptations that allow cancer cells to survive and proliferate in diverse tumor microenvironments. This metabolic flexibility is different from normal cellular metabolic processes and leads to heterogeneity in cancer metabolism within the same cancer type or even within the same tumor. In this book, we delve into the complexity and diversity of cancer metabolism, and highlight how understanding the heterogeneity of cancer metabolism is fundamental to the development of effective metabolism-based therapeutic strategies. Deciphering how cancer cells utilize various nutrient resources will enable clinicians and researchers to pair specific chemotherapeutic agents with patients who are most likely to respond with positive outcomes, allowing for more cost-effective and personalized cancer therapeutic strategies.

Cancer cell biology research in general, and anti-cancer drug development specifically, still relies on standard cell culture techniques that place the cells in an unnatural environment. As a consequence, growing tumor cells in plastic dishes places a selective pressure that substantially alters their original molecular and phenotypic properties.The emerging field of regenerative medicine has developed bioengineered tissue platforms that can better mimic the structure and cellular heterogeneity of in vivo tissue, and are suitable for tumor bioengineering research. Microengineering technologies have resulted in advanced methods for creating and culturing 3-D human tissue. By encapsulating the respective cell type or combining several cell types to form tissues, these model organs can be viable for longer periods of time and are cultured to develop functional properties similar to native tissues. This approach recapitulates the dynamic role of cell–cell, cell–ECM, and mechanical interactions inside the tumor. Further incorporation of cells representative of the tumor stroma, such as endothelial cells (EC) and tumor fibroblasts, can mimic the in vivo tumor microenvironment. Collectively, bioengineered tumors create an important resource for the in vitro study of tumor growth in 3D including tumor biomechanics and the effects of anti-cancer drugs on 3D tumor tissue. These technologies have the potential to overcome current limitations to genetic and histological tumor classification and development of personalized therapies.

The book addresses controversies related to the origins of cancer and provides solutions to cancer management and prevention. It expands upon Otto Warburg's well-known theory that all cancer is a disease of energy metabolism. However, Warburg did not link his theory to the "hallmarks of cancer" and thus his theory was discredited. This book aims to provide evidence, through case studies, that cancer is primarily a metabolic disease requring metabolic solutions for its management and prevention. Support for this position is derived from critical assessment of current cancer theories. Brain cancer case studies are presented as a proof of principle for metabolic solutions to disease management, but similarities are drawn to other types of cancer, including breast and colon, due to the same cellular mutations that they demonstrate.

This textbook presents concise chapters written by internationally respected experts on various important aspects of cancer-associated metabolism, offering a comprehensive overview of the central features of this exciting research field. The discovery that tumor cells display characteristic alterations of metabolic pathways has significantly changed our understanding of cancer: while the first description of tumor-specific changes in cellular energetics was published more than 90 years ago, the causal significance of this observation for the pathogenesis of cancer was only discovered in the post-genome era. The first 10 years of the twenty-first century were characterized by rapid advances in our grasp of the functional role of cancer-specific metabolism as well as the underlying molecular pathways. Various unanticipated interrelations between metabolic alterations and cancer-driving pathways were identified and currently await translation into diagnostic and therapeutic applications. Yet the speed, quantity, and complexity of these new discoveries make it difficult for researchers to keep up to date with the latest developments, an issue this book helps to remedy.

As our knowledge of cancer metabolism has increased, it has become apparent that cancer metabolic processes are extremely heterogeneous. The reasons behind this heterogeneity include genetic diversity, the existence of multiple and redundant metabolic pathways, altered microenvironmental conditions, and so on. As a result, methods in the clinic and beyond have been developed in order to image and study tumor metabolism in the in vivo and in vitro regimes. Both regimes provide unique advantages and challenges, and may be used to provide a picture of tumor metabolic heterogeneity that is spatially and temporally comprehensive. Taken together, these methods may hold the key to appropriate cancer diagnoses and treatments in the future. Proceeds from the sale of this book go to support an elderly disabled person.

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.

Improved understanding of the cellular and molecular makeup of tumors in the last 30 years has unraveled a previously unexpected level of heterogeneity among tumor cells as well as within the tumor microenvironment. The concept of tumor heterogeneity underlines the realization that different tumors can display significant differences in their genomic content as well as in their overall behavior. Our capacity to better understand the heterogeneous make up of tumors has very important consequences on our ability to design efficient therapeutic strategies to improve patient survival. This book highlights several aspects of tumor heterogeneity in the context of metastatic development and summarize some of the challenges posed by heterogeneity for tumor diagnostics and therapeutic management of tumors.