Despite some significant challenges in bringing effective personalized medicine approaches to the clinic, cancer treatment is one area in which personalized medicine and molecularly-targeted therapies have begun to come to fruition already. In addition to Gleevec, which effectively targets the BCR-ABL fusion protein that is characteristic of chronic myelogenous leukemia (CML) in humans, Herceptin is important in the treatment of the subset of breast cancer patients whose cancers have substantial expression of the "Her2/Neu" protein.
However, these molecularly targeted therapies that focus on specific proteins important to the causation and maintenance of a subset of cancer cases are only available for a small percentage of all cancers. To move closer to truly targeted therapy (which hopefully would have less severe side effects than current approaches with relatively non-specific cytotoxic drugs and radiation), we must know more about the relatively complicated changes in the DNA of the cancer cell.
Although it frequently seems like the more we learn about cancer the more complex the problem becomes, a paper published in the online edition of Science today makes a significant contribution to our knowledge of what goes awry in the development of breast and colon cancers. (Although the full paper is available only by subscription, there is a press release with more information about the findings at the HHMI website)
In this work, groups led by Giovanni Parmigiani, Kenneth Kinzler, Victor Velculescu, and Bert Vogelstein utilized a high-throughput DNA sequencing approach to look at the coding sequences (the DNA sequence coding for protein sequence) of more than 18,000 genes in each of 11 different breast cancer cases and 11 colon cancer cases. Utilizing a careful approach to determine that mutations occurred somatically in the development of the cancer (as opposed to being polymorphisms present in all of the DNA of the individual at birth), the study basically showed that an impressively high percentage of the genes had a non-silent mutation (i.e., one that altered the protein coding sequence) in at least 1 cancer case: 9.4% of the total number of genes analyzed. Putative cancer-related genes were evaluated in another set of cancers. There were a total of 280 genes (equally distributed between breast and colon cancers) in all that were validated by the presence of mutations in both the initial cancer set and the validation tumor set. Further analyses were performed to assess the plausibility that these genes were mutated more often in the cancers than would be predicted by chance.
Although it is not news that cancer is a genetic disease associated with the accumulation of mutations in several key genes (that presumably differ by tumor/tissue type), the high-throughput resequencing approach utilized in this work suggests much more complexity in the mutated cancer genome than was previously recognized. By plotting a score related to gene mutation frequency for each gene on something resembling a topographical map, the authors point out that while there are certainly still several "mountains" that appear to likely to be very important in cancer development, perhaps the more striking feature is an extremely high number of "hills" (genes mutated at a much lower frequency in a given cancer type, but which still likely play a role in causation or maintenance).
Although some may suggest that the authors are making mountains out of molehills, I believe that their interpretation is likely correct. The ability to sequence virtually the entire coding genome of a tumor is very exciting; with the coming drop in sequencing costs, this could be more realistically applied to individual patient tumors in a clinical context. The hard part will be devising strategies to act on this information in a way that improves clinical outcomes. However, as the authors suggest, many of the "hills" can be grouped into one of a small number of biochemical pathways. Future studies will no doubt assess whether drugs targeting these pathways can be applied on a rational basis based on high-throughput sequencing of patient tumor DNA.
