I literally threw myself into the reading of this book as soon as I had the chance to do so. The title is very attractive for those who, like me, work with the bioinformatics of different types of cancer. Reading this book was not complex. It is composed of 362 pages, divided into 14 chapters.

I must say right away that this book has a major downside: its title is misleading. No pages in the book talk of any bioinformatics if bioinformatics is considered as the discipline that applies computer science techniques to biological problems. With the exception of chapter 13 (“In Silico Investigation of Cancer Using Publicly Available Data”), which provides an overview of software programs and databases within 24 pages, what you can read in the book is “only” about mechanisms and pathways. Being flexible, I would have titled the book “Cancer Systems Biology,” compensating for the lack of a computational component. Warning! I am not saying that I wanted to read a textbook of bioinformatics algorithms, but I definitely associated with the title of the book the idea (and the desire!) to read methodological solutions to problems (for example, on the somatic call of insertions/deletions, on the limits of detection of a mutation with the current technology available, on the methods for the determination of alternative forms of splicing, and so on). In short, the book does not answer my expectations.

However, the mechanisms discussed, both about the arguments and the way they have been presented, are intriguing. The book starts by explaining the key events that lead and support any process of carcinogenesis: the sustained proliferation, the promotion of the immortality of cancer cells, the activation of processes of invasion and metastasis, and escaping from immune system control. All of this information is provided by constantly referring to literature sources. In particular, among the causes of carcinogenesis, an interesting explanation of the difference between driver and passenger mutations is given. Again, it would have been very essential, almost vital, to address the issue of methodologically distinguishing between the two. What are the limits of the existing bioinformatics methods that determine and distinguish between the two?

Another “computational” flash is given in chapter 2, when the authors deal with the problem of meeting the needs of “clustering,” “bi-clustering,” and semantically analyzing lists of differentially expressed genes. The methodology is interesting; that is, the topic is not too technical and not too theoretical. This chapter is a winner. Unfortunately, it is atypical of book’s chapters.

Chapter 3 offers an interesting description of staging and typing of tumors. Equally interesting is the attempt to understand cancer, starting from the genomic level, which the authors discuss in chapter 4. In particular, they describe how driver mutations may affect critical pathways, which are responsible for cell differentiation, survival, and genomic maintenance.

Maybe due to my personal interest in the topic of energy metabolism and in the hyaluronic acid involvement in tumorigenesis, I found these two chapters wordy with little insight. That, however, was not the case for chapter 7, which explains the mechanisms of escape from programmed cell death: the apoptosis. It gives a good and complete overview of the relationships between cell growth, cell cycle, cell stress, and pathways related to cell survival and apoptosis. This chapter is a hot topic and is well written.

Chapter 10, which deals with cell movement and metastasis, sparked my interest. In particular, I found appealing the descriptions of the mechanisms that push cancer cells to migrate and to reach tissues and organs also far from the point of origin of the tumor. Very interesting is the description of the tumor microenvironment, which perhaps deserved a separate chapter. The following chapter is well connected to the previous one, as it describes the “second transformation” of cancer, which occurs after metastasis. Thus, parameters such as pH levels, the microenvironment, the oxygen levels, and the immune response are described and linked.

Chapter 12 faces, without much expectation, the problem of seeking biomarkers in body fluids.

Hence, as mentioned earlier, this is a good book to understand the mechanisms, pathways, and molecular processes related to cancer. It is not bad for those who must explain their results and figure out which is the systemic impact of a given aberration. However, alas, bioinformatics is almost absent, despite the book’s title.