Patient-derived Xenograft Models Of Glioblastoma Research Articles

Efficacy of PARP Inhibitor Rucaparib in Orthotopic Glioblastoma Xenografts Is Limited by Ineffective Drug Penetration into the Central Nervous System.

PARP inhibition can enhance the efficacy of temozolomide and prolong survival in orthotopic glioblastoma (GBM) xenografts. The aim of this study was to evaluate the combination of the PARP inhibitor rucaparib with temozolomide and to correlate pharmacokinetic and pharmacodynamic studies with efficacy in patient-derived GBM xenograft models. The combination of rucaparib with temozolomide was highly effective in vitro in short-term explant cultures derived from GBM12, and, similarly, the combination of rucaparib and temozolomide (dosed for 5 days every 28 days for 3 cycles) significantly prolonged the time to tumor regrowth by 40% in heterotopic xenografts. In contrast, the addition of rucaparib had no impact on the efficacy of temozolomide in GBM12 or GBM39 orthotopic models. Using Madin-Darby canine kidney (MDCK) II cells stably expressing murine BCRP1 or human MDR1, cell accumulation studies demonstrated that rucaparib is transported by both transporters. Consistent with the influence of these efflux pumps on central nervous system drug distribution, Mdr1a/b(-/-)Bcrp1(-/-) knockout mice had a significantly higher brain to plasma ratio for rucaparib (1.61 ± 0.25) than wild-type mice (0.11 ± 0.08). A pharmacokinetic and pharmacodynamic evaluation after a single dose confirmed limited accumulation of rucaparib in the brain is associated with substantial residual PARP enzymatic activity. Similarly, matrix-assisted laser desorption/ionization mass spectrometric imaging demonstrated significantly enhanced accumulation of drug in flank tumor compared with normal brain or orthotopic tumors. Collectively, these results suggest that limited drug delivery into brain tumors may significantly limit the efficacy of rucaparib combined with temozolomide in GBM.

CBIO-04DAXX INHIBITION SUPPRESSES TUMOR GROWTH IN PTEN-DEFICIENT HUMAN GLIOBLASTOMAS

Glioblastoma (GBM) is the most lethal type of brain cancer, where deletions and/or mutations in the tumor suppressor gene PTEN (phosphatase and tensin homolog) are associated to responsiveness of cancer to radiotherapy and chemotherapy. Herein, we show that a PTEN-DAXX-H3.3 chromatin complex negatively regulates proliferation and tumor growth. PTEN directly regulates oncogene expression by coordinating DAXX and histone H3.3 deposition on chromatin and in turn, DAXX protects PTEN from acetylation and ubiquitination. Furthermore, DAXX disruption in PTEN-deficient glioblastoma patient derived xenograft models suppresses tumor growth and promotes therapy sensitivity. Since PTEN loss is a common alteration in cancer, DAXX inhibition presents an approach to ablate the growth of such cancers.

In vivo RNAi screen identifies NLK as a negative regulator of mesenchymal activity in glioblastoma.

Glioblastoma (GBM) is the most lethal brain cancer with profound genomic alterations. While the bona fide tumor suppressor genes such as PTEN, NF1, and TP53 have high frequency of inactivating mutations, there may be the genes with GBM-suppressive roles for which genomic mutation is not a primary cause for inactivation. To identify such genes, we employed in vivo RNAi screening approach using the patient-derived GBM xenograft models. We found that Nemo-Like Kinase (NLK) negatively regulates mesenchymal activities, a characteristic of aggressive GBM, in part via inhibition of WNT/β-catenin signaling. Consistent with this, we found that NLK expression is especially low in a subset of GBMs that harbors high WNT/mesenchymal activities. Restoration of NLK inhibited WNT and mesenchymal activities, decreased clonogenic growth and survival, and impeded tumor growth in vivo. These data unravel a tumor suppressive role of NLK and support the feasibility of combining oncogenomics with in vivo RNAi screen.

CONTEXT OF VULNERABILITY OF GBM: DESCRIPTIVE GENOMICS LEADING TO EMPIRIC THERAPEUTICS

BACKGROUND: The goal of genomics enabled medicine is to understand how specific molecular pathology of an individual tumor informs therapy tailored to specific vulnerabilities. Exploiting large datasets of human glioblastoma (GBM) and relevant, molecularly-profiled preclinical models enables an iterative learning-loop by which such profiling can produce hypotheses of effective, targeted treatment. Genomic, transcriptomic, and epigenomic profiling of glioblastoma enables depiction of driver-passenger relationships of molecular events among subgroupings of cases of the disease. The present study queries genomic profiles of GBM (TCGA) as well as a panel of patient-derived xenograft (PDX) models. Predictions of targets, and targeted drugs effective against those targets are tested empirically using short-term cultures of GBM PDX models. METHODS: Datamining of TCGA GBM combined with similar genomic profiling of a panel of orthotopic GBM PDX models establishes a data warehouse for discovery of subsets of the disease. These subsets, defined by driver-passenger relationships, partition GBM cases into discreet clusters, which enable follow-on bioinformatic queries for nodes of vulnerability. Short-term cultures of GBM PDX models afford testing cell proliferation, survival, migration, invasion, and differentiation responses to a collection of annotated chemical entities characterized as inhibition a wide spectrum of cancer-related targets (n = 600), establishing a “chemical biology fingerprint” of GBMs of distinct contexts. The chemical biology fingerprints can be mapped against the genomic profiling to find and prioritize candidate therapeutic targets matched to the molecular profile. RESULTS: Human GBM profiling bins tumors into molecular contexts based on driver-passenger relationships; GBM PDX models reside in the same or analogous contexts as primary tumor samples. Within such contexts, biochemical signaling and pathway functions portray associated distinct prioritized candidate targets, which align to varying degrees with Chemical Biology Fingerprints (CBFs). CONCLUSIONS: Multi-omics characterization of human GBM uncovers subclusters of the disease with distinct and unique chemovulnerabilities. Single- and multi-agent studies will afford matching effective interventions tailored to individual tumors. Supported by NIH NCI U01CA168397. SECONDARY CATEGORY: Tumor Biology.