Over the past few years, new technologies have begun to unravel the genomic secrets of cancer by illuminating differences between tumors and normal tissue. High-density genotyping and gene expression arrays can quickly and cheaply scan the genome for alterations and gene-expression changes linked to cancer. Sequencing of candidate genes has uncovered cancer-specific mutations, and other assays have identified changes to the higher-order structure of DNA and its companion proteins. Using statistical analysis to pinpoint the biochemical pathways affected by these changes allows us to untangle the complex interplay of cell regulation, cell signaling, and other functions that transform a normal cell into a cancerous one.
Yet three difficulties arise in such endeavors. Searching candidate genes rather than the whole genome for cancer-causing mutations may miss some important variations, as well as some of the structural variations, such as deletions, inversions, and translocations, that may inform us about a cancer’s onset or biology. In addition, current technologies require large amounts of DNA and RNA in order to produce comprehensive data, so only larger (often, more advanced) tumors are suitable as subjects of study. Lastly, it’s difficult to integrate information gained through these different analytic techniques.
With next-generation sequencing technologies, however, we can compare the genetic information in tumor tissue and normal tissue taken from the same person–a feat that was inconceivable until very recently (see “Interpreting the Genome”). Our group used technology developed by Illumina to sequence the complete genomes of cancerous and normal tissue in a patient with acute myeloid leukemia; we identified 10 mutated genes that appear to play a role in this cancer. Since then, an improvement on this approach has been developed that makes it possible to discover structural variants. Next-generation sequencing also allows high-resolution comparisons of the “transcriptome”–a profile of the RNA molecules present at a particular moment in time–in healthy and cancerous cells. This approach can detect RNA expressed at extremely low levels, and it can reveal RNA messages that have been processed in different ways. In addition, these new technologies enable the characterization of microRNAs–short pieces of RNA (less than 25 DNA letters) that control gene expression–and other types of RNA that do not code for proteins. Finally, the methods can derive so much information from a single type of experiment that they require only a small amount of DNA and RNA.
Because cancer genomics is relatively new, it’s led to only a few diagnostic tests so far. For example, some large clinics now screen tumor DNA from lung adenocarcinomas to determine whether the tumors will respond to tyrosine kinase inhibitors. The acceleration of cancer-related discoveries that will result from using next-generation sequencing will dramatically increase the potential for developing more such tests. Although these data provide just an initial step toward improving treatments and outcomes for cancer patients, it is a crucial one.
Elaine Mardis is codirector of the Genome Center at Washington University School of Medicine in St. Louis.
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