Briefings in Bioinformatics Advance Access originally published online on August 9, 2006
Briefings in Bioinformatics 2006 7(3):313-314; doi:10.1093/bib/bbl017
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Book Review |
Cancer Bioinformatics—From Therapy Design to Treatment.
Edited by Sylvia Nagl
John Wiley & Sons Ltd, The Atrium,
Southern Gate, Chichester;
ISBN: 0-470-86304-8; 300pp.; 2006; $130. In this post-genomic era, a large amount of data such as expressed sequence tag (EST) data, structure data, microarray data, proteomic data, single nucleotide polymorphism (SNP) data and clinical data provide great opportunities to understand cancer, the second most common cause of death in the western world. These data, addressing the same problem from different angles, should be integrated to generate useful hypotheses to understand the mechanisms of cancer. At the same time, cancer should be considered as a robust while fragile system and should be attacked from the trade-off of the system. This book of Cancer Bioinformatics naturally addressed these two problems of integrating different sources of data in cancer research and mathematical modeling of cancer as a system. It is a good book not only for statisticians, mathematicians and computer scientists, but also for clinical oncologists and ethicists.
This book covers a wide range of topics. The first section of the book (Chapters 1–3) describes cancer from the system biology point of view. According to the cancer system theory, cancer is caused by the fragility of our body and the very mechanisms that robustly maintain normal physiology also serve to maintain and promote tumor progression. Thus, cancer has established itself as a robust system, in which system control, alternative modularity and decoupling are possible underlying mechanisms for robustness. However, like an airplane which is robust under various weather conditions while very fragile without power under any weather condition, so also the robust cancer system also has its fragility. In order to attack cancer, we should integrate data and identify the fragile parts of the cancer systems. The second section of the book (Chapters 4–7) gives a summary of computational models of cancer. There are four basic types of models, i.e. growth, angiogenesis, response and pathway models. Other models can be roughly thought as the hybrid of these four. However, none of the models is capable of satisfactorily capturing the rich dynamic behavior of tumor, and thus a real clinical use of mathematical models of cancer systems has not yet materialized. The third section of the book (Chapters 8 and 9) introduces the in vivo models for cancer. It mainly focuses on the mouse model for cancer research in the MTB database. This database provides basic cancer researchers with information about mouse models such as tumor frequency and incidence, genetic alterations observed in tumors, genetic background of affected mice, tumor classifications and pathology. The fourth section of the book (Chapters 10 and 11) provides some public data generated for cancer research, with the emphasis on EST data and cancer tissue resources. These EST data and cancer tissue data will be very useful to identify cancer-related genes and their functions. The fifth section of the book (Chapters 12–14) discusses the ethical and legal implications of cancer bioinformatics. Many ethical and legal issues on collecting, holding, analyzing and sharing the data are discussed.
This book is a gem for anyone who wants to work in cancer biology. It not only provides many good references and reviews for further researches in mathematical modeling and simulations of cancers, but also many useful resources and data which will be substantially valuable on attacking different biological problems besides those in cancer bioinformatics. However, the book would be even better if the statistical methods to integrate the sequence information with clinical data, literature information, gene ontology annotation and other types of data were discussed, instead of only emphasizing the electronic integration of data and the development of protocols that allow complex and subtle queries. I still strongly recommend people, who are interested in cancer research, to read the book as rudimentary for understanding the field of cancer bioinformatics and for accumulating enough background knowledge for further research.
Division of Biostatistics
School of Medicine
Indiana University
Submitted: May 10, 2006. Accepted: May 12, 2006.
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