Since Knudson’s famous hypothesis proposing the two-hit model, our understanding of cancer as a genetic disease has progressed to the realization that cancer is not often a function of a single gene gone awry, but probably represents a complex interaction of multiple processes in the genome including altered copy number, gene expression, transcriptional regulation, chromatin modification, sequence variation, and DNA methylation. It is vital to the goal of producing better patient outcomes to understand not only what genes are involved in a certain type of cancer, but also how these other processes affect gene regulation. In short, an integrated view of the cancer genome is necessary and is now becoming possible.
The first karyotypes were produced in 1956. Shown here is a comparison of a normal karyotype of a normal female and one from a tumor. By 1960, a karyotype of a cancer genome revealed the presence of the Philadelphia chromosome. Now known to represent the BCR-ABL fusion protein, it was not until 33 years later in 1993 that a drug, gleevec, become available that targeted the fusion product. By applying high-throughput microarray technologies, the Cancer Genetics Branch is striving to make observations of the cancer genome that will provide deeper understandings of the biology of cancer, to develop prognostic and diagnostic markers to improve patient-specific treatments, and to find promising targets for directed drug therapy.
Zooming out to look at the whole genome at once, the normal genome with normal female DNA in red and normal male DNA in green shows the expected abnormalities on the X and Y chromosomes. Comparing that to a single breast cancer genome reveals the richness of the data that we are producing. Nearly every chromosome shows some copy number alteration that can be mapped to the genome to produce lists of candidate genes. But with so many alterations, it is helpful to consider multiple genomes at once, as copy number changes that occur in multiple samples are more likely to be of biological importance and not simply a product of an unstable cancer genome.
Bioc strucvariant seattle_11_09
Using R and BioConductor To Find Structural Variants In Short Read Sequencing Data Sean Davis, MD, PhD National Cancer Institute National Institutes of Health Bethesda, MD