An extensive genomic study of the childhood cancer neuroblastoma reinforces the challenges of treating the most aggressive forms of this disease. Contrary to expectations, the researchers found relatively few recurrent gene mutations — mutations that would suggest new targets for neuroblastoma treatment. Instead, the investigators have refocused on how neuroblastoma tumors evolve in response to medicine and other factors.
"This research underscores the fact that tumor cells often change rapidly over time, so more effective treatments for this aggressive cancer will need to account for the dynamic nature of neuroblastoma," said the study's leader John M. Maris, MD, director of The Children's Hospital of Philadelphia's Center for Childhood Cancer Research.
Dr. Maris led a multicenter research project, as part of the National Cancer Institute's TARGET (Therapeutically Applicable Research to Generate Effective Treatments) initiative, which published its findings recently in Nature Genetics.
The largest genomic study of a childhood cancer to date, the TARGET project analyzed DNA from 240 children with high-risk neuroblastomas. Using a combination of whole-exome, whole-genome, and transcriptome sequencing, the study compared DNA from tumors with DNA in normal cells from the same patients, with the goal of mapping out a limited number of treatment strategies. This approach would have represented a significant step forward in the personalization of neuroblastoma therapy.
Along these lines, researchers at Children's Hospital and other centers had previously discovered neuroblastoma-causing mutations, such as those in the ALK gene. In the subset of patients carrying this mutation, oncologists can provide effective treatments tailored to their genetic profile.
In the absence of frequently altered oncogenes that drive high-risk neuroblastomas, the investigators concluded that some cases may result from other changes, such as rare germline mutations or epigenetic modifications during tumor evolution. Going forward, researchers may need to turn to functional genomics, to learn which tumors will or won't respond to treatments while also going beyond a static picture of a cancer cell with fixed genetic contents.