For disease as complex as cancer, knowing a single mutation is sometimes not enough. In the case of lethal brain tumors, scientists have found many individual mutations, but effective treatments remain elusive.
Instead of honing in on individual mutations, scientists from Yale University are taking a broader approach. That is, they’re interested in how individual mutations coalesce to trigger cancer and drive its progression.
“The human cancer genome is now mapped and thousands of new mutations were associated with cancer, but it has been difficult to prove which ones or their combinations actually cause cancer,” said Sidi Chen, assistant professor of genetics and at Yale’s Systems Biology Institute, and the study’s senior author. “We can also use this information to determine which existing drugs are most likely to have therapeutic value for individual patients, a step towards personalized cancer therapy.’’
In particular, the team is interested in glioblastoma, a highly aggressive cancer of the brain with a meager five-year survival rate of only 10 percent. Chen’s team started with hundreds of genetic mutations already implicated for glioblastoma. They then used CRISPR gene editing and screening technology to study the combinations of these hundreds of mutations. All told, they analyzed over 1500 genetic permutations that could drive glioblastoma progression in mice.
The team found several combinations of mutations, such as B2m–Nf1, Mll3–Nf1 and Zc3h13–Rb1, that could fuel glioblastoma growth. In addition, they identified two mutations in Zc3h13 or Pten, which could explain why some tumors become resistant to the chemotherapy temozolomide.
The results reiterate the complexity of cancer as a genetic disease – that the combination of multiple mutations and pathways are involved in the progression of cancer. Because the whole is greater than the sum of the individual mutations with cancer, this may explain why treatments that only target individual mutations may come up short.
The team hopes their research could help make personalized cancer treatment a reality soon. Furthermore, they say their screening technique could be applied to other cancer types. “Taken together, our study provides a systematic and unbiased molecular landscape of functional tumor suppressors in an autochthonous mouse model of GBM, opening new paths for high-throughput analysis of cancer genetics directly in vivo,” they wrote.