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  • 1
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    Abstract 248: Dynamic multi-OMICS analysis of glioblastoma cells reveals context of vulnerability to neddylation inhibition by pevonedistat
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 248-248
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 248-248
    Abstract: As the most lethal primary brain tumor, glioblastoma multiforme (GBM) calls for novel therapeutic development. Global over activation of neddylation (a post-translational modification) has recently been found in GBM patients and has correlated with shorter patient survival. Significant accumulation of neddylation in recurrent GBM tissues indicates its importance in tumorigenesis and tumor progression. Analogous to the ubiquitination pathway, neddylation is essential to many protein regulation and biological processes. Although most well-characterized substrates of neddylation are the cullin subunits of Cullin-RING ligases (CRLs), non-cullin NEDD8 substrates have been investigated in recent years. Neddylation and subsequent degradation of PARC, p53, MDM2 and EGFR exemplify the broader functional role of neddylation. The neddylation inhibitor MLN4924 targets NEDD8 Activating Enzyme (NAE), an upstream activator of neddylation, and, as a result, induces cell cycle arrest, apoptosis and senescence in cancer cells. In this work, we investigated the context of vulnerability to Pevonedistat (MLN4924) in GBM by comparing the dynamic response of sensitive and non-sensitive cells using transcriptomics and proteomics profiling, using long-established and patient derived glioma cell lines. Efficacy of MLN4924 in glioma cell models was evaluated by measuring cell viability (CellTiterGlo®), colony formation efficiency, and cell cycle progression (flow cytometry with propidium iodide staining). GB1 (IC50= 0.28 μM), LN18 (IC50 = 0.19 μM), and GBM43 (IC50= 0.45 μM) were established as sensitive and M059K (IC50= 5.5 μM), SNU1105 (IC50 = 20.9 μM), and GBM39 (IC50= 10.3 μM) as non-sensitive cell lines based on IC50 values. Western blot analysis of known cell cycle regulatory pathways and DNA damage response pathway did not show significant dynamic differences between sensitive and non-sensitive glioma cell models. To discover genomic and/or proteomic markers of differential response we collected RNA and protein for LN18 (sensitive) and SNU1105 (Non-sensitive) cells after 0, 2, 8 and 24 h treatment with MLN4924 at 100 nM and 500 nM concentration for transcriptomics and proteomics analysis. RNA sequencing was utilized for dynamic transcriptomic analysis. Cell lysates were processed using bottom-up proteomics workflow and the data was acquired on a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer. Proteins were identified by querying spectral data against canonical and RNA-Seq predicted proteins and differential analysis was carried out to identify candidate determinats of vulnerability. An understanding of determinants of vulnerability to MLN4924 will expand knowledge of neddylation’s role in cancer and may point to signatures of GBM patients most likely to respond to this targeted intervention. Citation Format: Shayesteh R. Ferdosi, Brett Taylor, Nanyun Tang, Rita Bybee, Sen Peng, Victoria David-Dirgo, Krystine Garcia-Mansfield, Ritin Sharma, Patrick Pirrotte, Michael Berens, Harshil Dhruv. Dynamic multi-OMICS analysis of glioblastoma cells reveals context of vulnerability to neddylation inhibition by pevonedistat [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 248.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2019-248
    RVK:
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    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 2
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    Abstract 2745: Tumor microenvironment and host genetics impact glioma progression in a Collaborative Cross-based orthotopic allograft model
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 2745-2745
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 2745-2745
    Abstract: Gliomas are diffusely invasive brain tumors with fatal outcomes and few effective treatments. Precision medicine focuses on targeting the genetics of individual tumors, but not host genetics, despite studies that have linked germline polymorphisms with glioma risk. Accordingly, glioma survival studies in mice utilize genetically variable tumors on identical host genetic backgrounds, which fails to differentiate between cancer cell-autonomous (CCA) and tumor microenvironment (TME) effects on glioma progression and host survival. The Collaborative Cross (CC) is a panel of genetically diverse mouse strains derived from both wild- and traditional inbred laboratory strains that facilitates high-resolution genetic mapping in models of complex disease. Here, we implement a novel platform to discover genetic modifiers of both CCA and TME phenotypes using genetically defined orthotopic murine allograft gliomas and CC hosts. We stereotactically injected Nf1;Trp53-/-oligodendrocyte progenitor-derived mouse tumor cells into syngeneic C57BL/6 control mice and 14 different CC strains. Seven strains survived significantly longer than controls (P & lt;0.05), suggesting slower tumor growth (Gs, growth slow). The remaining 7 strains survived similarly to controls, suggesting fast growth (Gf, growth fast). Variable tumor growth in CC mice suggests that genetic background influences molecular processes in the TME that inhibit or potentiate tumor growth, respectively. To identify candidate genes, we performed RNA sequencing on 36 tumors from 3 Gf strains, 4 Gs strains, and controls. 134 genes were differentially expressed among Gf, Gs, and control tumors (P & lt;0.05). Hierarchical clustering on these genes revealed that Gs strains clustered separately from Gf and controls. Gene ontology analysis using GOrilla showed 30 enriched processes, (FDR q & lt;0.001), all of which were involved in immune responses or extracellular matrix biology. These results suggest that Gs strains activate immune and TME processes that slow tumor growth. Quantitative trait locus (QTL) analyses of host genetics and tumor data are pending and will facilitate identification of genetic variants that influence TME effects on tumor progression. Citation Format: Kasey Skinner, Martin Ferris, Ryan Bash, Abigail Shelton, Erin Smithberger, Steve Angus, Brian Golitz, Noah Sciaky, Jeremy Simon, Jason Stein, Glenn Matsushima, Quinn Ostrom, Lindsay Stetson, Jill Barnholtz-Sloan, Harshil Dhruv, Michael Berens, Fernando Pardo Manuel de Villena, C. Ryan Miller. Tumor microenvironment and host genetics impact glioma progression in a Collaborative Cross-based orthotopic allograft model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2745.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2019-2745
    RVK:
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    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 3
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    Abstract 3757: Probing glioblastoma and its microenvironment at single cell resolution
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 3757-3757
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 3757-3757
    Abstract: Single-cell sequencing (scSeq) is a powerful tool to investigate cancer genomics at single cell resolution. Multiple studies have recently illuminated intratumoral heterogeneity in glioblastoma, however, the majority focused on molecular complexity of tumor cells, without taking into account unexplored host cell types that contribute to the microenvironment around GBM tumor. To address the glioblastoma microenvironment composition and potential tumor-host interactions, we performed deep coverage (176k average reads per cell) scSeq of freshly resected primary GBM patient tissue without implementing any tumor cell enrichment strategies. scSeq libraries for 902 cells were prepared using 10X Gemcode platform and sequenced on Illumina NextSeq 500. This run was of high quality with 2,663 median genes per cell and low mitochondrial gene percentage (median & lt; 5%). We used Cell Ranger analysis pipelines and Seurat packages to classify individual cells into 10 clusters and visualize them using t-SNE two-dimensional projections. We then identified the signature gene set for each cluster, relative to all other cells. Pathway analysis of each cluster signature along with known GBM microenvironment cell signatures revealed glioma tumor population along with surrounding microglia/marcophages, astrocytes, pericytes, oligodendrocytes, T cells and endothelial cells. Cell type markers identified by single cell transcriptomics were validated by IHC analysis. Microenvironmental composition and single cell signature will be confirmed through single nuclei sequencing of preserved (Frozen) tumor sample. Our results demonstrate the cellular diversity of brain tumor microenvironment and lay a foundation to further investigate the individual tumor and host cell transcriptomes that are influenced not only by their cell identity but also by their interaction with surrounding microenvironment. Citation Format: Sen Peng, Sanhita Rath, Saumya Bollam, Jenny Eschbacher, Shwetal Mehta, Nader Sanai, Michael Berens, Seungchan Kim, Harshil Dhruv. Probing glioblastoma and its microenvironment at single cell resolution [abstract] . In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3757.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2019-3757
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 4
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    Abstract 2007: Development of a clinical assay for predicting glioblastoma (GBM) patients most likely to respond to arsenic trioxide (ATO)
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 2007-2007
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 2007-2007
    Abstract: Glioblastoma (GBM) is the most frequently reported primary malignant brain tumor (29.6%). The prognosis for patients who develop GBM is bleak, with average survival after diagnosis of 12-16 months. Although conventional treatment with surgery, irradiation, and temozolomide postpones tumor progression and extends patients survival, these tumors universally recur and unrelentingly result in patient death. Personalized therapies against molecular targets that drive the growth of the bulk of primary tumors have so far been unsuccessful in clinical trials, due to lack of biomarker driven approaches. Thus, there is significant unmet need to begin biomarker driven precision medicine trials for treatment of GBM. Arsenic trioxide (ATO) is an inorganic compound that induces apoptosis via multiple pathways. Arsenic trioxide (TRISENOX®) is approved by the FDA for patients with acute promyelocytic leukemia (APL). Pre-clinical studies in brain tumors suggest that ATO is synergistic with radiation therapy (RT) and may enhance effects of radiation. In an earlier Phase II clinical trial (NCT00275067) using intravenous ATO and temozolomide in combination with radiation therapy for patients with newly diagnosed malignant gliomas, a subset of patients demonstrated notable benefit (Progression free survival (avg. = 638 days) and overall survival (avg. = 967 days)). Comparing RNAseq data from preclinical models and specimen from the Phase II clinical trial, the responder group could be confidently distinguished from the non-responder cohort leading to gene signatures of differential ATO sensitivity. Applying a Relative Expression Ordering (REO) Analysis framework, we pinpointed a probability-based roster of 28 top scoring pairs (TSP) as the classifier by which to identify patients with a higher likelihood to benefit from including ATO in combination with TMZ and radiation. This method is completely independent of platform on which data is collected and can be used for analysis of individual, newly-enrolled, n = 1 patients. We are advancing a protocol using the above gene classifier as enrollment criteria for an Adaptive clinical trial testing an oral formulation of ATO for newly diagnosed IDH1 WT Primary GBM patients; the trial will test whether patients whose tumors with ATO Classifier show 6-month PFS benefit by addition of ATO to Standard-of-Care. The trial will validate and refine the comprehensive biomarker panel that could identify most likely GBM responders to ATO and TMZ treatment in combination with radiation. Supported by a grant from the Baylor Scott & White Foundation. Citation Format: Sen Peng, Jinghua Gu, Xuan Wang, Sanhita Rath, Jacob Cardenas, Nicholas Schork, George Snipes, Harshil Dhruv, Karen Fink, Michael Berens. Development of a clinical assay for predicting glioblastoma (GBM) patients most likely to respond to arsenic trioxide (ATO) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2007.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2020-2007
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 5
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    Abstract C223: Network-based approach aids in the discovery of context-specific druggable targets for treatment of glioblastoma.
    American Association for Cancer Research (AACR) ; 2013
    In:  Molecular Cancer Therapeutics Vol. 12, No. 11_Supplement ( 2013-11-01), p. C223-C223
    Details
    In: Molecular Cancer Therapeutics, American Association for Cancer Research (AACR), Vol. 12, No. 11_Supplement ( 2013-11-01), p. C223-C223
    Abstract: Systematic discovery of actionable cancer targets could fill an unmet need for improved approaches to manage Glioblastoma Multiforme (GBM). Critical barriers to the discovery of druggable targets in cancer include: 1) molecular heterogeneity of the disease across patients, 2) implementation of a systematic approach to utilize existing knowledge and molecular data for target discovery, and 3) the lack of a relevant, rapid and systematic pipeline to enable empirical testing of hypotheses. We describe a network-based method to stratify GBM clinical samples into molecularly homogeneous subsets based on gene expression, gene copy number, and miRNAs, which we term “molecular contexts," or mCs. Within these mCs, a knowledge-based topological analysis of pathway elements was used to uncover discrete mC-specific targets. An empiric chemical screen probed the target space across different mCs, mC-4 and mC-14, which were molecularly orthogonal to each other. Specimens in mC-4 were enriched with samples previously classified as Mesenchymal-type GBM, while mC-14 was enriched with Proneural-subtype GBM. The chemical screen was carried out using short-term in vitro cultures derived from patient-derived GBM xenografts, which mapped to mC4 and mC14 based on gene expression. We employed a network-based topological approach to discern targets and pathways as candidate druggable vulnerabilities. The chemical validation screen was carried out with an assembled chemical biology fingerprint (CBF) library comprised of 650 small molecules targeting multiple cancer-associated pathways and processes. Matching specific chemical hits to their respective targets allowed for validation of specific gene hits from the topological analysis. Of particular interest were two molecular context specific lethal compounds in the screen, tamoxifen citrate and arsenic trioxide, specific to the mC4 and mC14, respectively. PKC, among the targets of Tamoxifen citrate, was a predicted node of vulnerability for mC-4 GBM, while PML gene, a target of Arsenic Trioxide, was a predicted target for mC-14. In summary, our results suggest that context analysis coupled with knowledge-based enrichment and topological analysis identifies specific GBM contexts with novel unique drugable targets. Supported by NIH U01 CA168397. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C223. Citation Format: Harshil D. Dhruv, Seungchan Kim, Jeff Kiefer, Dorothea Emig-Agius, Darren Finlay, Sungwon Jung, Kristiina Vuori, Michael Berens. Network-based approach aids in the discovery of context-specific druggable targets for treatment of glioblastoma. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C223.
    Type of Medium: Online Resource
    ISSN: 1535-7163 , 1538-8514
    URL: Article
    DOI: 10.1158/1535-7163.TARG-13-C223
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2013
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  • 6
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    EXTH-59. mtTERT PROMOTER AS A TARGET FOR TREATMENT OF GLIOBLASTOMA
    Oxford University Press (OUP) ; 2016
    In:  Neuro-Oncology Vol. 18, No. suppl_6 ( 2016-11-01), p. vi72-vi72
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 18, No. suppl_6 ( 2016-11-01), p. vi72-vi72
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/now212.301
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
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  • 7
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    EXTH-53. IMPACT OF EGFRvIII AND PTEN DELETION MUTATIONS ON RESPONSE OF Ink4a/Arf-NULL MURINE ASTROCYTES TO EGFR TYROSINE KINASE INHIBITORS
    Oxford University Press (OUP) ; 2017
    In:  Neuro-Oncology Vol. 19, No. suppl_6 ( 2017-11-06), p. vi84-vi84
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 19, No. suppl_6 ( 2017-11-06), p. vi84-vi84
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/nox168.345
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
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  • 8
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    RTID-08. MACHINE LEARNING TO UNCOVER SIGNATURES OF VULNERABILITY IN GLIOBLASTOMA UMBRELLA SIGNATURE TRIAL (GUST)
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi194-vi194
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    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi194-vi194
    Abstract: Glioblastoma is characterized by intra- and inter-tumoral heterogeneity. A glioblastoma umbrella signature trial (GUST) posits multiple investigational treatment arms based on corresponding biomarker signatures. A contingency of an efficient umbrella trial is a suite of orthogonal signatures to classify patients into the likely-most-beneficial arm. Assigning optimal thresholds of vulnerability signatures to classify patients as “most-likely responders” for each specific treatment arm is a crucial task. We utilized semi-supervised machine learning, Entropy-Regularized Logistic Regression, to predict vulnerability classification. By applying semi-supervised algorithms to the TCGA GBM cohort, we were able to transform the samples with the highest certainty of predicted response into a self-labeled dataset and thus augment the training data. In this case, we developed a predictive model with a larger sample size and potential better performance. Our GUST design currently includes four treatment arms for GBM patients: Arsenic Trioxide, Methoxyamine, Selinexor and Pevonedistat. Each treatment arm manifests its own signature developed by the customized machine learning pipelines based on selected gene mutation status and whole transcriptome data. In order to increase the robustness and scalability, we also developed a multi-class/label classification ensemble model that’s capable of predicting a probability of “fitness” of each novel therapeutic agent for each patient. Such a multi-class model would also enable us to rank each arm and provide sequential treatment planning. By expansion to four independent treatment arms within a single umbrella trial, a “mock” stratification of TCGA GBM patients labeled 56% of all cases into at least one “high likelihood of response” arm. Predicted vulnerability using genomic data from preclinical PDX models correctly placed 4 out of 6 models into the “responder” group. Our utilization of multiple vulnerability signatures in a GUST trial demonstrates how a precision medicine model can support an efficient clinical trial for heterogeneous diseases such as GBM. Surgical Therapies
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.770
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 9
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    GENO-13NETWORK-BASED APPROACH AIDS IN THE DISCOVERY OF CONTEXT-SPECIFIC DRUGGABLE TARGETS FOR TREATMENT OF GLIOBLASTOMA
    Oxford University Press (OUP) ; 2015
    In:  Neuro-Oncology Vol. 17, No. suppl 5 ( 2015-11), p. v94.1-v94
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 17, No. suppl 5 ( 2015-11), p. v94.1-v94
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/nov215.13
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
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  • 10
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    Abstract 289: Probing the non-enhancing component of glioblastoma: Targeting what is left behind
    American Association for Cancer Research (AACR) ; 2018
    In:  Cancer Research Vol. 78, No. 13_Supplement ( 2018-07-01), p. 289-289
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 289-289
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2018-289
    RVK:
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    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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