Abstract 289: Probing the non-enhancing component of glioblastoma: Targeting what is left behind

While genomic profiling and therapeutic selection support individualized GBM treatment, such therapeutic decision-making is usually made with reference to tumor obtained from the enhancing core region. GBM is known to be heterogeneous and exhibits a high resistance to standard therapies. To address whether non-enhancing tumor (representing the majority of tumor left behind after surgery) shows distinct genomic characteristics and therapeutic targets compared to the enhancing tumor core, we performed genome-wide exome-sequencing and RNA-sequencing for 12 patients with matched enhancing region and at least one non-enhancing region. Non-enhancing biopsies show a surprisingly high level of tumor content, with a median of 28% tumor cells and 6 of the 22 samples having greater than 50% tumor cells. Cognate non-enhancing and enhancing specimens demonstrated overall concordance in therapeutically actionable alterations (single nucleotide variants) and copy number alterations. However, non-enhancing regions were not genetically identical and did reveal additional and distinct variants compared to enhancing cores. For example, the non-enhancing region of patient 1 showed two nonsense NF1 mutations (R1534X; R2517X) while the enhancing region showed an NF1 frameshift mutation (F1247fs). Clonality analysis by LumosVar also indicated that 7 out of 12 patients harbored dissimilar cellular prevalence patterns between enhancing and non-enhancing regions. In addition, comparison of alternative polyadenylation between enhancing and non-enhancing regions uncovered distinct 3' UTR usage: e.g. SGMS2 and TOB1 tended to have longer 3' UTR in enhancing regions whereas longer 3' UTR of SYNPO and NOS1AP were

prevalent in non-enhancing regions. We posit that the enhancing component of glioblastoma probably underrepresents the genomic alterations in patients' tumors. Given non-enhancing tumor is left behind after surgical debulking, genomic profiling of this region would potentially reveal more accurate tumor vulnerabilities and lead to more effective therapy.

Supported by a grant from the Ben & Catherine Ivy Foundation.

Citation Format: Sen Peng, Rebecca Halperin, Harshil Dhruv, Sara Byron, Christophe Legendre, Joanna Phillips, Michael Prados, Michael Berens, Nhan Tran. Probing the non-enhancing component of glioblastoma: Targeting what is left behind [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 289.

Abstract 4798: Targeting hTERT for treatment of glioblastoma (GBM)

Approximately 86% of GBM tumors exhibit a mutation at -124 or -146 bp upstream from the ATG start site in the transcription activating promoter region of Human telomerase reverse transcriptase (hTERT). Mutations in the hTERT promoter are known to impair repression, leading to overexpression of hTERT and tumor maintenance. Yet, complete understanding of mechanistic implications is still necessary. While overexpressed hTERT is associated with oncogenesis and resistance to apoptosis, we also observe phenotypes unrelated to the reverse transcription function. We characterized long-term glioma cell lines and glioma PDX models by hTERT promoter mutation status, hTERT mRNA expression, and hTERT protein expression in subcellular fractions. The -124 and -146 mutations are located in the major 5-12 G-quadruplex and result in misfolding of the silencer element, thus causing over-expression of hTERT. Using a diverse small molecule library, we identified a small drug-like pharmacological chaperone (pharmacoperone) molecule, TG-4260, which binds to the 26 mer base-pair heteroduplex loop, which is the nucleation site for cooperative folding of the major 5-12 G-quadruplex. The chaperone effect of TG-4260 corrects DNA hTERT G-quadruplex misfolding resulting from the promoter mutations and restores the silencer function of the G-quadruplex. TG-4260 directly decreases the transcription activity of the −124, −124/125, −138/139, and −146 mutants to a similar extent and suppresses the downstream gene BCL2, which activates caspase-3 and produces cell-cycle arrest, leading to cell death. This is the first example of the use of a pharmacoperone molecule to correct the misfolding of a DNA G-quadruplex element resulting from mutations in an early folding intermediate. We screened GBM cell models against this novel small molecule inhibitor that interferes with mutated hTERT promoter and demonstrated that TG-4260 selectively suppresses glioma cell viability without affecting non-transformed normal human astrocytes. Finally, telomere phenotypes from treated cells indicate non-canonical functions of telomerase may also contribute to glioma pathogenesis.

Citation Format: Saumya Reddy Bollam, Hyun-Jin Kang, Sen Peng, Vijay Gokhale, Laurence Hurley, Michael Berens, Harshil Dhruv. Targeting hTERT for treatment of glioblastoma (GBM) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4798.

Abstract 1169: mtTERT promoter as a target for treatment of glioblastoma

Approximately 86% of GBM tumors exhibit mutation at -124 or -146 bases upstream of the ATG start site in the transcription activating promoter region of Human telomerase reverse transcriptase (hTERT). Mutation in the promoter region of hTERT impairs repression, leading to overexpression of hTERT; inappropriate hTERT is associated with oncogenesis, tumor maintenance, and resistance to apoptosis. We surveyed long-term glioma cell lines and glioma PDX models for mt-hTERT; mRNA and protein expression of hTERT were assessed by qPCR and western blot. The -124 and -146 mutations are located in the major 5-12 G-quadruplex and result in misfolding of the silencer element and consequent over-expression of hTERT. Using a diverse small molecule library, we identified a small drug-like pharmacological chaperone (pharmacoperone) molecule, TG-4260, which binds to the 26 mer base-pair heteroduplex loop, which is the nucleation site for cooperative folding of the major 5-12 G-quadruplex. The chaperone effect of TG-4260 corrects DNA hTERT G-quadruplex misfolding resulting from the somatic mutations and restores the silencer function of the G-quadruplex thereby reducing hTERT activity. TG-4260 directly decreases the transcription activity of the WT and the −124, −124/125, −138/139, and −146 mutants to a similar extent and suppresses the downstream gene BCL2, which activates caspase-3 and produces cell-cycle arrest, leading to cell death. Finally, TG-4260 significantly inhibits telomerase and shortens telomere length after five days of treatment and induces a senescence-like phenotype. This is the first example of the use of a pharmacoperone molecule to correct the misfolding of a DNA G-quadruplex element resulting from mutations in an early folding intermediate. Finally, we screened GBM cell models against a novel small molecule inhibitor that interferes with mutated hTERT promoter and demonstrated that TG-4260 selectively suppresses glioma cell viability without affecting non-transformed normal human astrocytes.

Citation Format: Saumya R. Bollam, Harshil D. Dhruv, Hyun-Jin Kang, Sen Peng, Vijay Gokhale, Laurence Hurley, Michael Berens. mtTERT promoter as a target for treatment of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1169. doi:10.1158/1538-7445.AM2017-1169

Abstract 1083: Synergistic drug combination prediction through drug differential dependency network analysis

In an effort to discover strategies which identify effective drug combinations, we analyzed 39 of the 480 compounds screened in the Cancer Therapeutics Response Portal (CTRP) where combinations of two compounds were tested against 860 cancer cell lines; this enabled a comparison of the drug sensitivity of the combinations versus that of the individual compounds. More than half of the drug combinations (n=21) did not significantly improve the drug sensitivity, compared to individual compounds alone. In fact, some of the combinations showed reduced drug sensitivity. In EDDY-CTRP* analysis, the Cancer Cell Line Encyclopedia (CCLE) RNAseq data and CTRP compound response measurements were analyzed to discover both 1) pathways enriched with differential dependencies between sensitive and non-sensitive cell lines for each compound and 2) the mediators of cell line response to a drug. A mediator is a gene in a pathway that plays a significantly different role between sensitive and non-sensitive conditions. The significance is assessed for either essentiality, measured as a node’s centrality change, or specificity, measured as the difference in condition specific edges. These drug-pathway-mediator connections are predicted to reveal crucial molecular determinants of drug sensitivity that otherwise are hidden in the complexities of the molecular networks of the cell (Speyer et al., PSB 22:497-508, 2017). We further investigated whether mediators identified for single compounds could predict sensitivity to drug combinations. This analysis revealed that if two single compounds share the same specificity mediators, i.e. the genes with the most significant re-wiring of gene dependencies between sensitive and non-sensitive cell lines, combination of these two compounds correlate with improved sensitivity. The converse was also found: compounds that do not share mediators rarely show synergy. Further analysis and empirical testing of predicted combinations promises to prioritize synergistic drug combinations. We believe that this methodology may predict synergistic drug combinations from cancer cell line drug screening data. Supported by NIH U01CA168397.

*available at http://biocomputing.tgen.org/software/EDDY/CTRP

Citation Format: Seungchan Kim, Gil Speyer, Harshil Dhruv, Jeff Kiefer, Michael Berens. Synergistic drug combination prediction through drug differential dependency network analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1083. doi:10.1158/1538-7445.AM2017-1083

Abstract 1177: Targeting NEDD8 to uncover an exceptional responder molecular subtype in glioblastoma

Neddylation is a post-translational mechanism that marks proteins for degradation through activity of NEDD8 Activating Enzyme (NAE). NAE blocks cullin-RING ligases from initiating proteosomal degradation of select substrates including cell cycle regulators and apoptosis modulators. MLN4924, or Pevonedistat, targets NAE and inhibits Neddylation and induces apoptosis in sensitive cells. We have discovered, in a cohort of glioblastoma PDX models, an exceptional responder to MLN4924 (GBM102). Most pertinently the effects we observe are in a PDX cultured as 3D neurospheres that more closely resemble the true tumor architecture, heterogeneity, and “stem-like” phenotype characteristic of tumor growth. We have leveraged RNAseq expression data from Cancer Cell Line Encyclopedia (CCLE) and Pevonedistat response data from The Cancer Therapeutics Response Portal (CTRP) to apply a network-based analysis to identify pathways enriched with differential dependencies between cell lines sensitive and non-sensitive to MLN4924. The analysis also identifies potential mediating genes that appear to play critical roles in such differential dependency networks. Identified differential networks and mediators provide insight for cellular mechanisms underlying drug response. Additionally, we also investigated the efficacy of MLN4924 against orthotopic glioma PDX models (GBM102 and GBM116) in vivo to validate our findings in vitro. Thus genomic characterization of patient samples may lead to the identification of a molecular signature which is associated with a subset of GBMs vulnerable to MLN4924. As the treatment options for GBM are extremely limited, this may highlight a novel alternative opportunity to treat a select fraction of patients with this aggressive disease.

Citation Format: Lauren K. Hartman, Darren Finlay, Peiwen Pan, Seungchan Kim, Gil Speyer, Jeff Kiefer, Harshil Dhruv, Kristiina Vuori, Michael Berens. Targeting NEDD8 to uncover an exceptional responder molecular subtype in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1177. doi:10.1158/1538-7445.AM2017-1177

Abstract 883: Elucidating cancer hallmark context from glioma MR imaging and RNA expression data

Radiogenomics or radiomics is an emerging field where tumor genomic data is correlated with radiology image features, thereby potentially providing more biological information about the tumor phenotype. A central challenge is the potential for model over-fitting due to analysis of many thousands of genomic data-points with hundreds of corresponding patient image features. Biological interpretation of the imaging feature correlations is also challenged by overlapping pathways and common gene effects. Our goals were: i) to explore correlations between gene expression and corresponding Magnetic Resonance (MR) Apparent Diffusion Coefficient (ADC) derived imaging features in low grade glioma (LGG); ii) to classify significant gene and imaging correlates by cancer hallmark1. RNA expression data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and matched with corresponding MR image data from The Cancer Imaging Archive (TCIA). Among 32 patients, 18 were males (56%), and ages ranged from 21 to 74 years (mean age 44). Tumor and normal regions in the MR images were annotated by an expert radiologist using ITK-Snap. The normal reference region was used normalize image intensities in corresponding tumor regions. Tumor texture features were computed on ADC Maps at each voxel location within the disease region (including first and second order statistics, Run Length and co-occurrence matrix derived measures features. The voxel features were finally aggregated within the tumor region using statistical measures of mean, variance, median, kurtosis, and skewness. ADC imaging features (n=310) were correlated with each single gene expression value (11614 genes after MAD>0.4 filtering). Only image features and genes with pairwise correlations higher than 0.68 (0.68 is the 3-standard deviation above average correlation) and FDR (False Discovery Rate) <0.1 were used for follow-up analyses. Significant genes and MR image features were aggregated into 3 groups based on gene expression and correlated with cancer hallmarks. Seven Haralick image features (reflecting the average level of image intensity heterogeneity) were independently, significantly correlated with the Angiogenesis Hallmark (FDR all < 0.001). Three Haralick image features (reflecting asymmetric distribution of intensity) were significantly correlated with the Activating Invasion and Metastasis Hallmark (FDR all < 0.001). Validation of these findings in additional LGG cases with additional imaging protocols and features is ongoing. Radiogenomics informed by genomic profiling may usher in processes to infer cancer hallmarks to aid treatment planning and prognosis of glioma patients.1 Hanahan D and Weinberg RA (2011). Hallmarks of cancer: the next generation. Cell 144(5):646-74.

Citation Format: Yunxia Sui, Mirabela Rusu, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Graf John, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood, Sushravya Raghunath. Elucidating cancer hallmark context from glioma MR imaging and RNA expression data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 883. doi:10.1158/1538-7445.AM2017-883

Abstract 1886: mGluR1 drives invasion of proneural subtype of glioblastoma cells

A major cause for the therapeutic failure and subsequent morbidity and mortality of glioblastoma (GBM) is the aggressive invasion of malignant cells into the surrounding normal brain that effectively renders complete surgical resection impossible and virtually assures recurrent tumor growth. Multi-omic profiling of GBM led to their molecular sub-classification into two distinct molecular subtypes: proneural (PN) and mesenchymal (MES). However, very little is known about shared or distinct invasion processes of cells in these genomically different subtypes. Using Microarray gene expression profiles of microdissected paired stationary core and invasive rim samples from 19 patients, we demonstrated that invasive gene signature of MES subtypes differs from PN. Specifically, using three orthogonal but intersecting bioinformatic approaches, i.e. gene set variation analysis, causal network analysis, and iRegulon analysis; we discovered that genes differentially expressed between PN core and rim could, to a meaningful degree, be accounted for based on their annotation as “regulated by the transcription factor REST”. REST functions as a repressor of gene expression. Of the genes repressed by REST, mGluR1 (Metabotropic Glutamate Receptor 1) was significantly overexpressed at the rim of PN glioma cells as compared to the core. Finally, we also investigated the role of mGluR1 in glutamate induced glioma cell migration; our results show that glutamate stimulates migration of proneural-like glioma cells (A172) as compare to non-proneural-like glioma cell (T98G). mGluR1 activation by glutamate has shown to induce activation of Pyk2 and Src in astrocytes; knockout of mGluR1 is not embryonic lethal. In summary, our data demonstrate that glutamate-induced glioma cell migration of PN subtype of GBM is dependent on mGluR1 and thus raises the prospect that therapeutic targeting of mGluR1 may be a novel approach to controlling the invasion of this deadly disease.

Citation Format: Alena Gladwin, Sen Peng, Jeff Kiefer, Seugchan Kim, Michael Berens, Harshil D. Dhruv. mGluR1 drives invasion of proneural subtype of glioblastoma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1886. doi:10.1158/1538-7445.AM2017-1886

Abstract 1142: Novel target discovery for glioblastoma using chemical biology fingerprinting

The most common adult brain tumor is Glioblastoma Multiforme (GBM), an extremely aggressive cancer with only scant treatment options. Even with standard of care most patients present with a recurrence and the median survival is only circa 15 months. The need, therefore, for new therapeutic targets and treatment options is pressing. Here we describe here a multipronged approach to identifying said targets. We present an established methodology for the isolation and culture of patient derived GBM samples that retain the “stem-like” fraction thought to underlie resistance and recurrence. Furthermore we show genomically that these samples represent specific subtypes of the disease yet still form distinct groups in unbiased clustering analysis. Thus we have multiple representative patient derived cultures that are suitable for our drug discovery and chemical biology analyses. Using a process we term Chemical Biology Fingerprinting (CBF) we utilize small focused, and clinically relevant, chemical collections in order to identify patterns of chemovulnerabilities across multiple samples. This allows an unbiased yet cancer relevant sub-stratification and the identification of agents, and therefore targets, which may be relevant for GBM patient subtypes. Indeed our use of the highly annotated NCI CTD2 Informer Set of chemicals allows ready drug-to-target mapping and facilitates data sharing across the CTD2 network. Moreover, already defined subgroups can be clustered to find agents, or groups of agents, that show selective activity against traditional classifications (e.g. proneural, mesenchymal etc.). Finally our strategy is permissive for the identification of “exceptional responders”. That is, individual patient samples that respond to a specific drug whilst most samples are refractory. In sum we demonstrate generation of patient derived models and identify specific, and novel, drugs that may be relevant for specific GBM subtypes. Supported by NIH U01CA168397

Citation Format: Darren Finlay, Pedro Aza-Blanc, Harshil Dhruv, Alexey Eroshkin, Craig Hauser, Jeff Kiefer, Seungchan Kim, Tao Long, Robert G. Oshima, Sen Peng, Gil Speyer, Michael Berens, Kristiina Vuori. Novel target discovery for glioblastoma using chemical biology fingerprinting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1142. doi:10.1158/1538-7445.AM2017-1142

Abstract 882: Interpreting glioma MR imaging and somatic mutations in a cancer hallmark context

Extracting biologically relevant data from radiology images can enable better monitoring of disease progression and therapy response. The field of radiogenomics is providing new approaches for such genomic/radiology correlations. However, there are several challenges in validation and clinical translation in that few DNA mutations are shared between tumors from different individuals and the differences in scale between imaging and genomic features can limit interpretation of underlying mechanisms. The goals of this work were to i) analyze correlations between low grade glioma (LGG) DNA somatic mutations, using a novel DNA impact scoring approach, and MRI derived imaging features; and ii) to interpret results in context of cancer hallmarks1. Multi-parametric MRI and corresponding DNA data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). The cohort included 18 males (56%), with mean age of 44 years (range: 21-74 years). An expert radiologist outlined the normal and tumor regions of interest using ITK-Snap tool. The normal region was used as a reference to normalize image intensities in the tumor region. Tumor mean intensity and mean variance were computed from Apparent Diffusion Coefficient (ADC), T1 enhancement ratio (derived from T1 pre- and post- contract MRI), and Fluid-Attenuated Inversion Recovery (FLAIR) images. A novel algorithm was used to compute DNA impact scores for each somatic mutation. The score represents the probability of a DNA variant being pathogenic vs. nonpathogenic. First, the scoring algorithm computes a score for nucleotide base insertions, deletions, or single base changes and then computes the consequence of such changes on amino acid coding, binding sites, splice sites and protein phosphorylation sites. An impact score was then computed based on the individual DNA impact scores of mutations within the gene. Finally, an average DNA impact score was computed at the Cancer Hallmark level using a gene-cancer hallmark map. At gene level, significant positive correlations were found between the ATRX (p=0.0002), TP53 (p=0.02) and ADC mean intensity. At pathway level, regulation of TP53 expression and degradation, and DNA damage response, signal transduction by p53 class mediator, and DNA translocase activity were found to be enriched with genes that correlated with ADC and FLAIR. These pathways also contained genes that were enriched in the following cancer hallmarks: replicative immortality, evading growth suppression and genome instability. The ATRX gene is a member of all three hallmarks and TP53 a member of two. Since ADC is a measure of water diffusion and hence an indirect measure of cellularity, these findings demonstrate that mutations in replication and repair pathways are contributing to imaging features at the tumor level.1 Hanahan, D. and Weinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell 144(5):646-74.

Citation Format: John Graf, Mirabela Rusu, Yunxia Sui, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood. Interpreting glioma MR imaging and somatic mutations in a cancer hallmark context [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 882. doi:10.1158/1538-7445.AM2017-882