While investigating the biology of brain tumors in children, pediatric researchers are finding that crucial differences in how the same gene is mutated may call for different treatments. A new study offers glimpses into how scientists will be using the ongoing flood of gene-sequencing data to customize treatments based on very specific mutations in a child's tumor.

"By better understanding the basic biology of these tumors, such as how particular mutations in the same gene may respond differently to targeted drugs, we are moving closer to personalized medicine for children with cancer," said the study's first author, CHOP's Angela J. Sievert, MD, M.P.H.

The study, of which Dr. Sievert was a co-first author with Children's Hospital's Shih-Shan Lang, MD, was published recently in the Proceedings of the National Academy of Sciences.

The study, performed in cell cultures and animals, focused on a type of astrocytoma, the most common type of brain tumor in children. When surgeons can fully remove an astrocytoma (also called a low-grade glioma), a child can be cured. However, many astrocytomas are too widespread or in too delicate a site to be safely removed. So pediatric oncologists have been seeking better treatment options.

The current study focuses on mutations in the BRAF gene, one of the most commonly mutated genes in human cancers. Because the same gene is also mutated in certain adult cancers, the pediatric researchers were able to make use of recently developed drugs known as BRAF inhibitors that were already being tested in adults.

And by examining the molecular mechanisms behind drug resistance and working with the pharmaceutical industry, the current study's investigators identified a new, experimental second-generation BRAF inhibitor.

This work result lays a foundation for multicenter clinical trials to test the mutation-specific targeting of tumors by this class of drugs in children with astrocytomas, said Dr. Sievert. As this effort progresses, it will benefit from CHOP's commitment to resources and collaborations that support data-intense research efforts.