Cancer Bioinformatics
Authors: Weinstein John N. 
Published: November 2016

Overview
Bioinformatics is a rapidly developing scientific field in which computational methods are applied to the analysis and interpretation of biological data, usually at the cell or molecular level and usually in the form of large, multivariate datasets. In the context of cancer, the most frequent scenario is molecular profiling at the DNA, RNA, protein, and/or metabolite level for basic understanding of the biology, for identification of potential therapeutic targets, and/or for clinical prediction. The new massively‐parallel sequencing technologies are generating a data deluge—literally quadrillions of numbers in all—that strain the capacity of our hardware, software, and personnel infrastructures for bioinformatics; hence, the computational aspect of a sequencing project is now generally more expensive and time‐consuming than the laboratory work.
Bioinformatic analysis can be hypothesis‐generating or hypothesis‐testing. When it is hypothesis‐generating, the assumption is that one can afford a large number of false‐positive findings in order to identify one or a few evocative or useful ones; when it is hypothesis‐testing, the full rigor of statistical inference should be applied. A large number of statistical and machine‐learning algorithms, scripts, and software packages are now available for the analysis, and options are multiplying rapidly. Because the datasets are often so large, however, an important aspect of bioinformatics is data visualization, for example display of data arrayed along the length of the genome or, most commonly, in the form of a clustered heat map.
Cancer biologists and clinical researchers should preferably take the lead in biological interpretation of the massive datasets, but statistical analysis should be done by (or under the vigilant aegis of) a professional in informatics; when there are more variables (e.g., genes) than cases (e.g., patients), there is a tendency for the untrained to obtain overly optimistic p‐values or see false patterns in even randomized datasets. In addition, subtle errors that arise in the unglamorous tasks of data management and pre‐processing often lead to misleading or frankly wrong conclusions from the data.
As high‐throughput molecular profiling projects have become cheaper and easier, they have been incorporated progressively into the work of individual institutions, both academic and commercial. However, there are also growing numbers of large‐scale public projects in the domain. At the level of cell lines, the first was the NCI‐60, a panel of 60 human cancer lines used since 1990 by the U.S. National Cancer Institute to screen more than 100,000 chemical compounds plus natural products for anticancer activity. That panel was followed more recently by the Cancer Cell Line Encyclopedia, the Genomics of Drug Sensitivity in Cancer project, and others. At the clinical level, The Cancer Genome Atlas project has been center‐stage, profiling more than 10,000 human cancers of 33 different types, and there is an alphabet‐soup of acronyms for other such projects that are in progress or on the drawing board. Those projects, which require massive bioinformatics support, have spawned robust communities of bioinformatics expertise and served as testbeds for the development of new algorithms, software, and visualizations.