MDB-38. COMPUTATIONAL DRUG SENSITIVITY PREDICTS MEDULLOBLASTOMA SUBGROUP-SPECIFIC THERAPEUTICS

Medulloblastoma is the most common malignant pediatric brain tumor. Tumors are typically characterized as Group 3, Group 4, SHH, or WNT. Current standard-of-care includes surgery, radiation, and chemotherapy; however, treatment response and prognosis vary widely between subgroups. Additionally, surviving children frequently suffer from lifelong neurocognitive deficits. Therefore, novel therapeutic options are urgently needed. Despite extensive characterization of medulloblastoma tumors into four molecular subgroups, few subgroup specific therapies have advanced to the clinic. To address this, we have developed a novel platform called DrugSeq, which predicts drug sensitivities in patients and can stratify tumors by subgroup. DrugSeq also identifies key pharmacotranscriptomic differences between primary and recurrent tumors. We first calculated disease signatures for each patient by normalizing gene expression to low grade glioma samples from the posterior fossa. Using previously developed transcriptional consensus signatures (TCSs) that represent the affect that a drug has on the gene expression across a panel of cancer cell lines, we then calculated the discordance between each patient disease signature and drug TCS. Finally, using an ANOVA analysis we identified drugs which are predicted to differentially target each medulloblastoma subgroup. Among our top predicted anti-cancer compounds we found several kinase inhibitors, bromodomain inhibitors, and several psychiatric drugs with known brain penetrance. Additionally, we show distinct differences in drug sensitivity predictions between newly diagnosed and recurrent tumors, such as sensitivity to BET inhibition for recurrent Group 3 and Group 4 tumors despite no predicted sensitivity in the newly diagnosed tumors. Future studies with larger datasets may be able to further subdivide patients by age or molecular features within subgroups. Collectively, we show that DrugSeq may identify novel therapies and facilitate patient stratification in clinical trials, leading to more successful targeted medulloblastoma therapies.