From 1983 to 2004, James Doroshow was chairman of the City of Hope Comprehensive Cancer Center's Department of Medical Oncology and Therapeutics Research. Now director of the Division of Cancer Treatment and Diagnosis at the National Cancer Institute, he oversees pre-clinical drug discovery, development, and early phase clinical trials, as well as NCI's network for randomized phase 3 trials. GT's Jeanene Swanson caught up with him to see how genomics and proteomics are playing a role in his work.
Genome Technology: Are genomic and proteomic tools affecting clinical trial design?
James Doroshow: We are much further along in the genomic era than we are in the proteomic era. That doesn't mean there's a lack of interest in developing proteomic tools that will have an impact on trials — [for] the technology the NCI is trying to develop to use, there are a few very interesting examples where state-of-the-art proteomic tools have already been shown to be useful in the clinical trials setting.
Where I can tell you that things are ongoing and already proven successful is in the use and development of genomic signatures as potential predictive markers of outcome and/or for the choice of therapies for individual patients. [The Oncotype DX test for breast cancer] is a very clear example — it's probably the first example — of one of the ways that the NCI was able to support the application of genomic and personalized medicine. If we had not had the randomized clinical trials very, very well annotated to go along with the clinical specimens, it would never have been possible to develop that test.
We are now about halfway through the — I like to joke and call it the daughter of Oncotype DX — TAILORx trial. It has recruited several thousand women to try to understand whether or not we can learn even more about who does and who does not need chemotherapy for breast cancer.
There is, also in the breast cancer area, a considerable amount of interest and a great deal of planning to try to develop trials that utilize other genomic signatures to try to develop new therapies for patients with so-called triple negative breast cancer. There's very good agreement that one can segregate the patients on the basis of array data into at least six or seven categories of breast cancer. And we are trying to utilize that information to develop more specific types of interventions.
GT: Aside from gene signatures, what else is being used?
JD: If you look into the future, the enormous investment that the National Cancer Institute is making in The Cancer Genome Atlas has already brought forward examples in which we didn't know there were specific mutations — in glioblastoma, for example. I think that as that effort progresses from disease to disease it will set the stage for a need for very significant additional biology to understand [whether] those high-frequency mutations [are] associated with a specific biology. And, therefore, should they direct our drug discovery efforts, or would they potentially underlie novel drug discovery efforts?
GT: Where do you see the new paradigm headed?
JD: It depends how far out you want to look. The question really will become, how fast and how inexpensive will it be to sequence an entire tumor's genome? Will that become part of standard practice? And will the bioinformatic tools become available to develop additional signatures depending on the type of disease? Will it become routine for pathologists to use those signatures to classify these [tumors] in a different way? We still are classifying most disease using a paradigm that's 150 years old, but I think it's just a matter of, with the new technologies, will it become inexpensive to do this so that it becomes routine?