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A cancer roadmap

There are two major constantly expanding databases in the postgenomic era that contain vast amounts of undiscovered and much needed information underlying genome structure and function. These include genomic sequence databases and gene expression profiles acquired by DNA microarray techniques. Rational analysis of these databases allows extraction of information about the most basic mechanisms operating in normal and pathologic conditions. In a recent study from Stanford University (CA, USA), Eran Segal and colleagues analyzed data obtained in different laboratories at different times (26 studies in total) from 1975 microarrays spanning 22 types of cancer [1]. A total of 14,145 genes belonging to 17 different categories were organized in modules and the induction or repression of these modules was questioned relative to the cancer type. Several selection steps were undertaken to identify functional modules. First, gene sets were collected that contained groups of coexpressed genes, genes expressed in a certain tissue and genes belonging to the same functional category or pathway. The arrays were then identified as showing reliable expression signatures (induction or repression) of a statistically significant number of these gene clusters. Next, the core modules showing similar expression behavior in different gene clusters were identified (456 core modules). These core modules supposedly characterize the biologic processes more accurately since the genes span a large variety of functions and categories and are united by the similar expression signature. In the last step, the expression behavior of core modules was analyzed across clinical conditions such as tissue and tumor type, diagnostic and prognostic information and molecular markers. The result was presented as a matrix of modules versus clinical conditions with intensities depending on induction or repression in corresponding modules, providing the number of arrays with such annotation substantially exceeded the number expected by chance. There is no question that the resulting matrix represents a cancer roadmap containing an enormous amount of information that will be analyzed and utilized by many researchers involved in cancer studies. From the data, one can find modules shared across different cancer types, which suggest commonality of proliferation mechanisms. As an example of data analysis, the authors suggest that a single mechanism acts in both primary tumor proliferation and bone metastasis. The authors call for careful interpretation of these data, noting that the results substantially depend on the method of normalization and may be partly compromised by the diversity of the initial data sets. However, the method of normalization used in this study (expression of each gene is relative to the average expression of the gene across all arrays in the relevant data set) has been shown by the authors to be quite reliable. There are two important features of this analysis that separate it from similar studies and make the results most informative: