Superhighway or Blind Alley? The Cancer Genome Atlas Releases First Results
Authors: Karyn Hede
Journal: JNCI Journal of the National Cancer Institute (2008)

Three years ago, when the National Cancer Institute proposed its monumental $1.5 billion effort to catalog the genomic changes involved in cancer, the goal was to improve the ability to diagnose, treat, and prevent cancer. Now the first results of The Cancer Genome Atlas project (TCGA) have arrived, and they reinforce the emerging realization that the more researchers investigate the genetics of cancer, the farther away new therapeutic applications seem to get. In response, some researchers say that it's time to step back and look at cancer holistically, searching not for the individual genetic mutations but instead for unifying principles that underlie the disease process.

“This idea of digging deep into the genome gives you a lot of information, and it's an important tool, but it's really only one tool,” said Lynn Hlatky, Ph.D., director of the Center of Cancer Systems Biology at Tufts University in Boston. “If your goal is making progress therapeutically, I think it's not the best investment to dig this deep until you have a larger overriding principle that really works for you. You need unifying principles that are predictive.”

TCGA was intended to catalog all the genetic alterations involved in cancer and provide a roadmap for understanding how cancer develops and spreads. But the genetic complexity of cancer is making it more difficult to sort out which genetic changes actually cause disease. The NCI's $100 million pilot project—examining genetic changes in glioblastoma multiforme, the most common adult brain cancer—revealed a genetic train wreck of gross genomic alterations and mutations and signaling gone awry. But perhaps most surprisingly, the brain tumor study, which appeared in the journals Science and Nature in the first week of September, revealed new mutations associated with chemotherapeutic treatment for glioblastoma multiforme. The finding reveals that the array of mutations shifts with treatment, making genetic characterization a moving target.

Parallel studies still under way that are looking at genetic changes in ovarian and lung cancers, have not yet been published.

In the brain cancer project, the investigators studied 206 previously collected primary tumor samples, including 21 samples collected after treatment. Of these, the investigators selected 91 tumor samples for mutation analysis in 601 genes known to be important in cancer. They found 453 mutations in 223 genes, one-third of which had multiple mutations. Moreover, the tumors had an array of genetic differences among them, with most mutations occurring in only a few cases.

Lynda Chin, M.D., the co-principal investigator of TCGA's center at Harvard Medical School, acknowledges the challenges of evaluating so many data but says that newly developed computational tools are proving that finding potentially clinically relevant information is possible.

“Among the complexity of the genome, the question is: can we identify things that are of value, that are not just noise?” she said. “I think the answer is yes. I think the data now show, even with today's technology, we are able to detect these biologically important events, and they are already changing the way we think about cancer.”

Chin, scientific director of the Belfer Cancer Genomics Center at Dana-Farber Cancer Institute, points to the unexpected revelation, described in TCGA research network's Sept. 4, 2008, Nature article, that tumors from patients with recurrent glioblastoma multiforme develop genetic resistance to temozolomide, a common chemotherapy treatment for treating the disease. Genomic analysis of recurrent glioblastoma multiforme revealed that patients had developed a secondary mutation in a key DNA mismatch repair gene that allowed the tumor cells to evade the killing action of temozolomide. That information, she said, will allow researchers to test whether calcium channel blockers, a class of compounds recently found to inhibit the growth cells with mismatch repair defects, could prevent the emergence of drug resistance.

The research also reveals a pattern of gene mutations in a network of biochemical pathways, some of which were known to be involved in glioblastoma development, but with new insight into mutations in genes, such as PIK3R1, that were not previously known to be important in the disease. Before this study, scientists had identified mutations in the catalytic domain, p110a, which help drive tumor growth. Now investigators are reporting new mutations that probably interfere with the regulatory region of PI3K, a popular target for drug companies that are developing new cancer treatments.

“There are at least 10 drug companies developing inhibitors to PI3 kinase, and they all want to know how to pick patients who will respond,” said Chin. “They say, ‘Perhaps it should be patients with mutations in the catalytic domain?’ But now we know the cancer can activate the enzyme by mutating not only the catalytic domain, the p110a, but also the regulatory domain.”

The finding adds another level of complexity to the effort to predict who will respond to PI3K inhibitors, she said. It suggests that looking at mutations in the catalytic domain are not enough. Some cancers appear to find other ways to activate this crucial growth pathway.

The complexity of the problem is discouraging to those investigators looking to find the next targeted therapy for cancer. Right now, “we need 20 different drugs against 20 different targets,” said Chin. “We need to get to that point of having a collection of effective drugs and knowing when to use which combination based on a genetic profile of each patient. Obviously, we are not there yet.”