Diffuse Intrinsic Pontine Glioma (DIPG) is a fatal childhood cancer. We performed a chemical screen in patient-derived DIPG cultures along with RNAseq analyses and integrated computational modeling to identify potentially effective therapeutic strategies. The multi-histone deacetylase inhibitor panobinostat demonstrated efficacy in vitro and in DIPG orthotopic xenograft models. Combination testing of panobinostat with histone demethylase inhibitor GSKJ4 revealed synergy. Together, these data suggest a promising therapeutic strategy for DIPG.
BackgroundThe success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patient’s tumor. The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies. The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample. We propose a novel sensitivity prediction approach based on functional perturbation data that incorporates the drug protein interaction information and sensitivities to a training set of drugs with known targets.ResultsWe illustrate the high prediction accuracy of our framework on synthetic data generated from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and an experimental dataset of four canine osteosarcoma tumor cultures following application of 60 targeted small-molecule drugs. We achieve a low leave one out cross validation error of <10% for the canine osteosarcoma tumor cultures using a drug screen consisting of 60 targeted drugs.ConclusionsThe proposed framework provides a unique input-output based methodology to model a cancer pathway and predict the effectiveness of targeted anti-cancer drugs. This framework can be developed as a viable approach for personalized cancer therapy.
Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood with an unmet clinical need for decades. A single oncogenic fusion gene is associated with treatment resistance and a 40 to 45% decrease in overall survival. We previously showed that expression of this PAX3:FOXO1 fusion oncogene in alveolar RMS (aRMS) mediates tolerance to chemo- and radiotherapy and that the class I–specific histone deacetylase (HDAC) inhibitor entinostat reduces PAX3:FOXO1 protein abundance. Here, we established the antitumor efficacy of entinostat with chemotherapy in various preclinical cell and mouse models and found that HDAC3 inhibition was the primary mechanism of entinostat-induced suppression of PAX3:FOXO1 abundance. HDAC3 inhibition by entinostat decreased the activity of the chromatin remodeling enzyme SMARCA4, which in turn de-repressed the microRNA miR-27a. This re-expression of miR-27a led to PAX3:FOXO1 mRNA destabilization and chemotherapy sensitization in aRMS cells in culture and in vivo. Furthermore, a phase I clinical trial (ADVL1513) has shown that entinostat is tolerable in children with relapsed or refractory solid tumors and is planned for phase I-b cohort expansion or phase II clinical trials. Together, these results implicate an HDAC3–SMARCA4–miR-27a–PAX3:FOXO1 circuit as a driver of chemoresistant aRMS and suggest that targeting this pathway with entinostat may be therapeutically effective in patients.
Diffuse intrinsic pontine gliomas (DIPGs) represent a particularly lethal type of pediatric brain cancer with no effective therapeutic options. Our laboratory has previously reported the development of genetically engineered DIPG mouse models using the RCAS/tv-a system, including a model driven by PDGF-B, H3.3K27M, and p53 loss. These models can serve as a platform in which to test novel therapeutics prior to the initiation of human clinical trials. In this study, an in vitro high-throughput drug screen as part of the DIPG preclinical consortium using cell-lines derived from our DIPG models identified BMS-754807 as a drug of interest in DIPG. BMS-754807 is a potent and reversible small molecule multi-kinase inhibitor with many targets including IGF-1R, IR, MET, TRKA, TRKB, AURKA, AURKB. In vitro evaluation showed significant cytotoxic effects with an IC50 of 0.13 μM, significant inhibition of proliferation at a concentration of 1.5 μM, as well as inhibition of AKT activation. Interestingly, IGF-1R signaling was absent in serum-free cultures from the PDGF-B; H3.3K27M; p53 deficient model suggesting that the antitumor activity of BMS-754807 in this model is independent of IGF-1R. In vivo, systemic administration of BMS-754807 to DIPG-bearing mice did not prolong survival. Pharmacokinetic analysis demonstrated that tumor tissue drug concentrations of BMS-754807 were well below the identified IC50, suggesting that inadequate drug delivery may limit in vivo efficacy. In summary, an unbiased in vitro drug screen identified BMS-754807 as a potential therapeutic agent in DIPG, but BMS-754807 treatment in vivo by systemic delivery did not significantly prolong survival of DIPG-bearing mice.
Drugs targeting specific kinases are becoming common in cancer research and are a basis for personalized cancer therapy. Some of these drugs have the capacity to target multiple kinases. Promiscuous kinase inhibitors can be effective but the "off-target" effects can bring in toxicity for the patient. Thus the success of targeted cancer therapies with nominal harmful side effects is dependent on administering a single or multiple combinations of kinase inhibitors that targets the minimum number of kinases required to inhibit the tumor pathways. This requires a framework to predict the tumor sensitivities of a drug or drug combination based on the knowledge of the kinase inhibitors of a drug. In this article, we present a novel approach to predict the tumor sensitivities of a drug based on the generation of deterministic and stochastic Kinase Inhibition Maps. We build sensitivity maps or truth tables for a cell line from experimentally generated tumor sensitivities to kinase inhibitor drugs and use them to predict the sensitivity of a new drug or drug combinations based on known kinase inhibitor targets. We test our algorithms on a dataset of a dog osteosarcoma cell line with 317 possible kinase inhibitor targets after application of 36 targeted drugs. Our proposed algorithms are able to predict the sensitivities with high accuracy based on the given kinase inhibitor targets.
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