Ying Chao

Novel anti-glioblastoma agents and therapeutic combinations identified from a collection of FDA approved drugs

BackgroundGlioblastoma (GBM) is a therapeutic challenge, associated with high mortality. More effective GBM therapeutic options are urgently needed. Hence, we screened a large multi-class drug panel comprising the NIH clinical collection (NCC) that includes 446 FDA-approved drugs, with the goal of identifying new GBM therapeutics for rapid entry into clinical trials for GBM.MethodsScreens using human GBM cell lines revealed 22 drugs with potent anti-GBM activity, including serotonergic blockers, cholesterol-lowering agents (statins), antineoplastics, anti-infective, anti-inflammatories, and hormonal modulators. We tested the 8 most potent drugs using patient-derived GBM cancer stem cell-like lines. Notably, the statins were active in vitro; they inhibited GBM cell proliferation and induced cellular autophagy. Moreover, the statins enhanced, by 40-70 fold, the pro-apoptotic activity of irinotecan, a topoisomerase 1 inhibitor currently used to treat a variety of cancers including GBM. Our data suggest that the mechanism of action of statins was prevention of multi-drug resistance protein MDR-1 glycosylation. This drug combination was synergistic in inhibiting tumor growth in vivo. Compared to animals treated with high dose irinotecan, the drug combination showed significantly less toxicity.ResultsOur data identifies a novel combination from among FDA-approved drugs. In addition, this combination is safer and well tolerated compared to single agent irinotecan.ConclusionsOur study newly identifies several FDA-approved compounds that may potentially be useful in GBM treatment. Our findings provide the basis for the rational combination of statins and topoisomerase inhibitors in GBM.

Recognition of dextran-superparamagnetic iron oxide nanoparticle conjugates (Feridex) via macrophage scavenger receptor charged domains.

Dextran-coated superparamagnetic iron oxide nanoparticles (dextran-SPIO conjugates) offer the attractive possibility of enhancing MRI imaging sensitivity so that small or diffuse lesions can be detected. However, systemically injected SPIOs are rapidly removed by macrophages. We engineered embryonic cells (HEK293T) to express major macrophage scavenger receptor (SR) subtypes including SR-AI, MARCO, and endothelial receptor collectin-12. These SRs possess a positively charged collagen-like (CL) domain and they promoted SPIO uptake, while the charge neutral lipoprotein receptor SR-BI did not. In silico modeling indicated a positive net charge on the CL domain and a net negative charge on the cysteine-rich (CR) domain of MARCO and SR-AI. In vitro experiments revealed that CR domain deletion in SR-AI boosted uptake of SPIO 3-fold, while deletion of MARCOs CR domain abolished this uptake. These data suggest that future studies might productively focus on the validation and further exploration of SR charge fields in SPIO recognition.

Direct Recognition of Superparamagnetic Nanocrystals by Macrophage Scavenger Receptor SR-AI

Scavenger receptors (SRs) are molecular pattern recognition receptors that have been shown to mediate opsonin-independent uptake of therapeutic and imaging nanoparticles, underlying the importance of SRs in nanomedicine. Unlike pathogens, engineered nanomaterials offer great flexibility in control of surface properties, allowing addressing specific questions regarding the molecular mechanisms of nanoparticle recognition. Recently, we showed that SR-type AI/II mediates opsonin-independent internalization of dextran superparamagnetic iron oxide (SPIO) nanoparticles via positively charged extracellular collagen-like domain. To understand the mechanism of opsonin-independent SPIO recognition, we tested the binding and uptake of nanoparticles with different surface coatings by SR-AI. SPIO coated with 10 kDa dextran was efficiently recognized and taken up by SR-AI transfected cells and J774 macrophages, while SPIO with 20 kDa dextran coating or cross-linked dextran hydrogel avoided the binding and uptake. Nanoparticle negative charge density and zeta-potential did not correlate with SR-AI binding/uptake efficiency. Additional experiments and computer modeling revealed that recognition of the iron oxide crystalline core by the positively charged collagen-like domain of SR-AI is sterically hindered by surface polymer coating. Importantly, the modeling revealed a strong complementarity between the surface Fe-OH groups of the magnetite crystal and the charged lysines of the collagen-like domain of SR-AI, suggesting a specific recognition of SPIO crystalline surface. These data provide an insight into the molecular recognition of nanocrystals by innate immunity receptors and the mechanisms whereby polymer coatings promote immune evasion.

Role of Carbohydrate Receptors in the Macrophage Uptake of Dextran-Coated Iron Oxide Nanoparticles

Superparamagnetic iron oxide (SPIO, Ferumoxides, Feridex), an important MRI intravenous contrast reagent, is efficiently recognized and eliminated by macrophages in the liver, spleen, lymph nodes and atherosclerotic lesions. The receptors that recognize nanoparticles are poorly defined and understood. Since SPIO is coated with bacterial polysaccharide dextran, it is important to know whether carbohydrate recognition plays a role in nanoparticle uptake by macrophages. Lectin-like receptors CD206 (macrophage mannose receptor) and SIGNR1 were previously shown to mediate uptake of bacterial polysaccharides. We transiently expressed receptors MGL-1, SIGNR-1 and msDectin-1 in non-macrophage 293T cells using lipofection. The expression was confirmed by reverse transcription PCR. Following incubation with the nanoparticles, the uptake in receptor-expressing cells was not statistically different compared to control cells (GFP-transfected). At the same time, expression of scavenger receptor SR-A1 increased the uptake of nanoparticles three-fold compared to GFP-transfected and control vector-transfected cells. Blocking CD206 with anti-CD206 antibody or with the ligand mannan did not affect SPIO uptake by J774.A1 macrophages. Similarly, there was no inhibition of the uptake by anti-CD11b (Mac-1 integrin) antibody. Polyanionic scavenger receptor ligands heparin, polyinosinic acid, fucoidan and dextran sulfate decreased the uptake of SPIO by J774A.1 macrophages and Kupffer cells by 60-75%. These data unambiguously show that SPIO is taken up via interaction by scavenger receptors, but not via dextran recognition by carbohydrate receptors. Understanding of nanoparticle-receptor interaction can provide guidance for the design of long circulating, non-toxic nanomedicines.

High-efficiency liposomal encapsulation of a tyrosine kinase inhibitor leads to improved in vivo toxicity and tumor response profile

Staurosporine (STS) is a potent pan-kinase inhibitor with marked activity against several chemotherapy-resistant tumor types in vitro. The translational progress of this compound has been hindered by poor pharmacokinetics and toxicity. We sought to determine whether liposomal encapsulation of STS would enhance antitumor efficacy and reduce toxicity, thereby supporting the feasibility of further preclinical development. We developed a novel reverse pH gradient liposomal loading method for STS, with an optimal buffer type and drug-to-lipid ratio. Our approach produced 70% loading efficiency with good retention, and we provide, for the first time, an assessment of the in vivo antitumor activity of STS. A low intravenous dose (0.8 mg/kg) inhibited U87 tumors in a murine flank model. Biodistribution showed preferential tumor accumulation, and body weight data, a sensitive index of STS toxicity, was unaffected by liposomal STS, but did decline with the free compound. In vitro experiments revealed that liposomal STS blocked Akt phosphorylation, induced poly(ADP-ribose) polymerase cleavage, and produced cell death via apoptosis. This study provides a basis to explore further the feasibility of liposomally encapsulated STS, and potentially related compounds for the management of resistant solid tumors.

In silico modeling predicts drug sensitivity of patient-derived cancer cells

BackgroundGlioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling (“omics”) data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach.MethodsHere, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents.ResultsAmong the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings.ConclusionsThese results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

Multiple spatially related pharmacophores define small molecule inhibitors of OLIG2 in glioblastoma.

Transcription factors (TFs) are a major class of protein signaling molecules that play key cellular roles in cancers such as the highly lethal brain cancer—glioblastoma (GBM). However, the development of specific TF inhibitors has proved difficult owing to expansive protein-protein interfaces and the absence of hydrophobic pockets. We uniquely defined the dimerization surface as an expansive parental pharmacophore comprised of several regional daughter pharmacophores. We targeted the OLIG2 TF which is essential for GBM survival and growth, we hypothesized that small molecules able to fit each subpharmacophore would inhibit OLIG2 activation. The most active compound was OLIG2 selective, it entered the brain, and it exhibited potent anti-GBM activity in cell-based assays and in pre-clinical mouse orthotopic models. These data suggest that (1) our multiple pharmacophore approach warrants further investigation, and (2) our most potent compounds merit detailed pharmacodynamic, biophysical, and mechanistic characterization for potential preclinical development as GBM therapeutics.

Demonstration of Non-Gaussian Restricted Diffusion in Tumor Cells Using Diffusion Time-Dependent Diffusion-Weighted Magnetic Resonance Imaging Contrast

The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water mobility for probing microstructural properties of biological tissue and has become an effective tool for collecting information about the underlying pathology of cancerous tissue. Measurements using multiple b-values have indicated biexponential signal attenuation, ascribed to “fast” (high ADC) and “slow” (low ADC) diffusion components. In this empirical study, we investigate the properties of the diffusion time (Δ)-dependent components of the diffusion-weighted (DW) signal in a constant b-value experiment. A xenograft gliobastoma mouse was imaged using Δ = 11 ms, 20 ms, 40 ms, 60 ms, and b = 500–4000 s/mm2 in intervals of 500 s/mm2. Data were corrected for EPI distortions, and the Δ-dependence on the DW-signal was measured within three regions of interest [intermediate- and high-density tumor regions and normal-appearing brain (NAB) tissue regions]. In this study, we verify the assumption that the slow decaying component of the DW-signal is non-Gaussian and dependent on Δ, consistent with restricted diffusion of the intracellular space. As the DW-signal is a function of Δ and is specific to restricted diffusion, manipulating Δ at constant b-value (cb) provides a complementary and direct approach for separating the restricted from the hindered diffusion component. We found that Δ-dependence is specific to the tumor tissue signal. Based on an extended biexponential model, we verified the interpretation of the diffusion time-dependent contrast and successfully estimated the intracellular restricted ADC, signal volume fraction, and cell size within each ROI.

Abstract 5339: Deterministic in silico modeling predicts sensitivity of glioblastoma to targeted therapy

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Glioblastoma (GBM) is an aggressive disease associated with poor survival. The key to developing better diagnostic and therapeutic strategies is accounting for the complexity of GBM biology. This challenge of integrating vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. To this end, we employ an in silico modeling approach, wherein we utilize genomic and proteomic data to simulate tumor cells and predict drug responses. Using our in silico model, we replicated findings from a recent hypothesis-independent, empirical study that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. Among the drug-mutation associations reported in this study, our in silico model validated ∼85% of the associations. We also used the tumor model to predict sensitivity of patient-derived GBM lines to different targeted therapeutic agents. These analyses showed ∼75% agreement of in silico drug sensitivity with in vitro experimental findings. We aim to use this model to stratify patients for clinical trials. By accurately predicting responses of patient cancer cells to targeted agents a priori, the in silico tumor model provides an innovative approach to personalizing therapy for GBM and promises to improve clinical management of cancers. Citation Format: Sandeep C. Pingle, Sandra Pastorino, Rajesh Muktharavam, Pengfei Jiang, Ying Chao, Taher Abbasi, Shireen Vali, Santosh Kesari. Deterministic in silico modeling predicts sensitivity of glioblastoma to targeted therapy. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5339. doi:10.1158/1538-7445.AM2014-5339

Abstract 1856: Preclinical study for statins as anticancer drug.

Statins are a class of drug that inhibits 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase that is the rate-limiting enzyme of cholesterols synthesis. Now, statins are among the most prescribed drug used for treatment of hypercholesterolemia by lower down the serum cholesterol concentration and it is also a major means to preventing and reducing the risk for cardiovascular diseases. In addition to prevent and treat cardiovascular diseases, the emerging evidences indicated the statins usage has a number of beneficial effect such as anti-inflammation and lowering down the risk of cancer. To investigate the anti-cancer effect statins drugs that have been approved to clinical usage we evaluated the inhibition of breast cancer and GBM cell line growth. Statins are also potent inhibitors to stem cell-like primary GBM cells freshly isolated from patients. Unexpected, statins treatment induces strong cell autophagy death signals as weak if not at all cellular apoptosis. Tumor cells can be rescued after statin treatment by adding intermediated product of cholesterol synthesis (mevalonate and GGPP), it indicates that the target of the statins is specifically related the cholesterols synthesis pathway. Knock-down the expression of geranylgeranyl pyrophosphate synthetase-1 (GGPS-1), another key enzyme in cholesterol synthesis pathway, also stimulated strong cell autophagy and cell death in vitro, also dramatically reduced U87 tumor growth in vivo. In vivo data also showed that directly injection of statin is better than oral administrate to delay GBM tumor growth. This study showed statins are potent anti-cancer drug in vitro and in animal model. These safe and well-tolerate drugs are good candidates for clinical test as cancer chemotherapy reagents. Citation Format: Pengfei Jiang, Rajesh Mukthavavam, Ying Chao, Natsuko Nomura, Valentina Fogal, Sandra Pastorino, Ila Summit, Santosh Kesari. Preclinical study for statins as anticancer drug. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1856. doi:10.1158/1538-7445.AM2013-1856 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.