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Aberrant chromatin remodeling and activation of the PI3K pathway have been identified as important mediators of pediatric high-grade glioma (pHGG) and diffuse intrinsic pontine glioma (DIPG) pathogenesis. As inhibition of these pathways are promising therapeutic avenues and radiation is the only modality to prolong survival of patients with DIPG, we sought to explore radiosensitizing functions of such inhibition and to explore mechanisms of action of such agents. Here, we demonstrate that combined treatment with radiotherapy and CUDC-907, a novel first-in-class dual inhibitor of histone deacetylases (HDAC) and PI3K, evokes a potent cytotoxic response in pHGG and DIPG models. CUDC-907 modulated DNA damage response by inhibiting radiation-induced DNA repair pathways including homologous recombination and nonhomologous end joining. The radiosensitizing effects of CUDC-907 were mediated by decreased NFκB/Forkhead box M1 (FOXM1) recruitment to promoters of genes involved in the DNA damage response; exogenous expression of NFκB/FOXM1 protected from CUDC-907-induced cytotoxicity. Together, these findings reveal CUDC-907 as a novel radiosensitizer with potent antitumor activity in pHGG and DIPG and provide a preclinical rationale for the combination of CUDC-907 with radiotherapy as a novel therapeutic strategy against pHGG and DIPG. More globally, we have identified NFκB and FOXM1 and their downstream transcriptional elements as critical targets for new treatments for pHGG and DIPG.Significance: These findings describe the radiosensitizing effect of a novel agent in pediatric high-grade gliomas, addressing a critical unmet need of increasing the radiation sensitivity of these highly aggressive tumors. Cancer Res; 78(14); 4007-21. ©2018 AACR.

BackgroundMRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB).MethodsOne hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients.ResultsSurvival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy.ConclusionsBy employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients' molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification.