Figure 20–24 The chromosomal rearrangements in breast cancer cells. The results of an extensive DNA sequencing analysis performed on two different primary tumors are displayed as “Circos plots.” In each plot, the reference DNA sequences of the 22 autosomes and single sex chromosome (X) of a normal human female (3.2 billion nucleotide pairs) are aligned end-to-end to form a circle. Colored lines within the circle are then used to indicate the shown in blue, reveal the amplified DNA sequences (see the highly amplified regions indicated). (Adapted from P.J. Stephens et
BREAST CANCER 1 BREAST CANCER 2 al., Nature 462:1005–1010, 2009.) attributed to chromosome breakage and rejoining. Systematic surveys of the pro-tein-coding genes in common solid tumors—such as those of the breast, colon, brain, or pancreas—have revealed that an average of 33 to 66 genes have undergone somatic mutation affecting the sequence of their protein product. Mutations in noncoding regions of the genome are much more numerous, as one would expect from the much larger fraction of the genome that noncoding DNA represents. But they are considerably more difficult to interpret.
The high frequency of mutations testifies to the genetic instability of many cancer cells, but it leaves us with a difficult problem. How can we discover which of the mutations are drivers of cancer—that is, causal factors in the development of the disease—and which are merely passengers—mutations that happen to have occurred in the same cell as the driver mutations, thanks to genetic instability, but are irrelevant to the development of the disease? A simple criterion is based on frequency of occurrence. Driver mutations affecting a gene that plays a part in the disease will be seen repeatedly, in many different patients. In contrast, passenger mutations, occurring at more-or-less random locations in the genome and conferring no selective advantage on the cancer cell, are unlikely to be found in the same genes in different patients.
Figure 20–25 shows the results of an analysis of this sort for a large sample of colorectal cancers. The different sites in the genome are laid out on a two-dimensional array, with chromosome serial number along one axis and position within each chromosome along the other. The frequency with which mutations are encountered is shown by height above this plane, creating a mutation “landscape” with mountains (sites where mutations are found in a large proportion of the tumors in the sample), hills (where mutations are found less frequently but still more often than would be expected for a random scattering over the genome), and hillocks (sites of occasional mutations, occurring at a frequency no higher than would be expected for mutations scattered at random in each individual tumor). The mountains and the hills are strong candidates to be the sites of driver mutations—in other words, sites of cancer-critical genes; the hillocks are likely to correspond to passengers. Indeed, many of the mountains and hills turn out to be sites of known oncogenes or tumor suppressor genes, whereas the hillocks mostly correspond to genes that have no known or probable role in causation of cancer. Of course, some hillocks may correspond to genes that are mutated in only a few rare patients but are nevertheless cancer-critical for them.
About One Percent of the Genes in the human Genome Are Cancer-Critical
From studies such as the one just described, it is estimated that the number of driver mutations for an individual case of cancer (the sum of meaningful epigenetic and genetic changes in both coding sequences and regulatory regions) is typically on the order of 10, explaining why cancer progression generally involves an increase in genetic and/or epigenetic instability that enhances the rate of such changes.