Fendler and Atwal: Systematic deciphering of cancer genome networks 341 few genes are investigated at a time, it is biased, and the cancer is often simu- lated with a biological model. Other steps have been made to improve this type of interrogation through the use of RNA in- terference that “knocks- down” mRNA transcripts [22]. While these scans allow for many genes to be interrogated at once, it is still necessary to define the set of genes to investigate and in what type of tissue and under what conditions. From one perspective, it seems that these ap- proaches could be supple- mented by suggesting Figure 1. A shows the typical process of investigating hypo- multiple gene candidates thetical gene interactions. A hypothesis is made, experi- for validation by starting ments are performed, and then a result is obtained. B shows with genomic cancer data a typical process in which a microarray informs about hypo- (Figure 1B), i.e., let the pat- thhaevteicableneentwcroeraktse.dA,fttheer mdaatnayiswchoollleecgteendotmogee-wthiedreinarsraoymse al- tern of expression or copy gorithmic way (discussed in text) and (a) network(s) of inter- number tell us what genes actions are inferred. Those networks can then be suggested are interacting. Indeed, cur- for biological validation. Networks of gene relations were rent technologies inform on generated from a curated protein database with p53 as the the order of tens-of-thou- center of the network [43]. sands of genes, and thus, it is necessary to amplify the of material is needed for NGS, while mi- genes that convey abnormal expression or croarrays rely on orders of magnitude more, copy number resulting from causative mu- increasing the reliance on PCR, and thus, tations. However, even NGS technology has PCR biases have a larger impact on results. variability due to random fluctuations in the Most microarray technologies now have ap- cell, and thus, large sample sizes must be propriate analogues to NGS, e.g., expression used to investigate these genes. Since col- arrays to RNA-seq [20] and aCGH to CNV- lective samples are used, probabilistic meth- seq [21] are among a few. It should be noted, ods must be employed to identify genes of however, that while NGS will likely replace interest. However, simply identifying genes array-based technologies, the amount of of interest does not convey a pathway or samples currently available is still insuffi- necessarily generate an informative cancer cient for many types of investigations. Thus, model. It is the major goal of cancer biology until sufficient NGS samples are collected, to map DNA alterations to the causative microarrays will still be needed. function of the genes altered. One way to Before microarrays or NGS technolo- understand causation is to look at networks gies, researchers focused on single gene hy- of interacting genes, i.e., genes whose ex- potheses (Figure 1A). While this is a pression affects other genes. How to dis- thorough systematic scientific approach to cover collectively active genes in an cancer biology, it is time consuming since unbiased way, however, is not obvious.