The Experts below are selected from a list of 1860 Experts worldwide ranked by ideXlab platform
Michael E. Berens - One of the best experts on this subject based on the ideXlab platform.
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ATPS-16IDENTIFICATION OF CLINICALLY RELEVANT DRUGS FOR GLIOBLASTOMA WITH SPECIFIC MOLECULAR Signature USING CHEMICAL BIOLOGY FINGERPRINTING
Neuro-Oncology, 2015Co-Authors: Harshil Dhruv, Darren Finlay, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Kristiina Vouri, Michael E. BerensAbstract:Translating molecular subtyping of glioblastoma (GBM) into therapeutically actionable guidance remains an unfulfilled opportunity. A central challenge is to discover therapeutic targets matched to agents (drugs or tool compounds) that are predictably present in subclasses of GBM. Transcriptional patterns in GBM cases in TCGA can identify 12 distinct molecular contexts (mC), where, across a subset of genes and tumors, conditional dependencies of gene expression (signal & echo relationships) consistently emerge. We deployed a panel of 64 patient-derived xenografts from GBM patients whose gene expression profiles allow mapping against the same 12 molecular contexts identified from TCGA cases. These clinically-relevant, preclinical models afford bioinformatics mining for actionable targets as well as chemical library screening for activity against short-term cultures derived from the PDX models. Utilizing a technique we term Chemical Biology Fingerprinting (CBF), we interrogated a series of GBM PDX models using a small chemical library (650 compounds) of clinically relevant anti-cancer agents to uncover context-specific chemovulnerabilities. Preliminary data demonstrated that, mC14 GBM (GBM59, SF7300, GBM116), characterized by mt-P53 and transcriptional similarity to GBM proneural subtype, show distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4 GBM (GBM91, GBM102), characterized by CHEK2 and NF1 mutations and transcriptional patterns similar to GBM mesenchymal subtype. To clinically-validate an ATO Vulnerability Signature, we acquired 22 treatment naive archival patient samples, who were part of Phase I/II clinical trial to study efficacy of ATO and Temozolomide (TMZ) in combination with radiation in treatment of high grade gliomas (NCT00275067) and which exhibited varied survival with ATO treatment (91 days to >1000 days), and determined their molecular context classification using whole transcriptome sequencing (RNAseq). In summary, we demonstrate a subclassification of GBM into novel contexts and we also show that these contexts are differentially sensitive to clinically relevant drugs. Supported by NIH U01CA168397.
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Abstract 4671: Identification of novel drugs for glioblastoma using chemical biology fingerprinting
Clinical Research (Excluding Clinical Trials), 2015Co-Authors: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Michael E. Berens, Kristiina VuoriAbstract:Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Glioblastoma Multiforme (GBM) is an aggressive brain tumor with very poor prognosis and extremely limited therapeutic options. GBM is the most common malignant brain tumor and the search for novel targets and/ or the repurposing of already extant drugs to treat the disease is therefore of utmost importance. We describe here a comprehensive multidisciplinary approach to identifying said targets and ergo potential therapies. We have applied a novel analytical strategy to The Cancer Genome Atlas (TCGA) GBM expression data to stratify GBM into novel subtypes we call molecular contexts, or mCs. Subsequently, a panel of patient-derived GBM xenografts was ascribed to our novel mCs. Utilizing a technique we term Chemical Biology Fingerprinting, or CBF, short-term cultures derived from these clinically-relevant preclinical models were screened for chemosensitivity with a deeply annotated, yet clinically relevant, chemical library. Agents that were statistically more toxic to one context than another were then re-tested in true drug dose response experiments to confirm sensitivity. Preliminary data demonstrated that mC14, characterized by mutant p53 and transcriptionally similar to the GBM proneural subtype, showed distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4, enriched for NF1 mutations and with transcriptional patterns similar to the GBM mesenchymal subtype. To validate the ATO Vulnerability Signature in GBM, we acquired 20 treatment naive archival patient samples, that were part of a Phase I/II clinical trial to study the efficacy of ATO and Temozolomide in combination with ionizing radiation ([NCT00275067][1]). Participants in the trial exhibited varied survival with ATO treatment (91 days to >1000 days) and the clinical samples were subtyped into our molecular contexts using RNAseq data. In summary, we demonstrate a subclassification of GBM into novel molecular contexts (mCs) and show that these contexts are differentially sensitive to clinically relevant drugs. Citation Format: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey Raizer, Michael Berens, Kristiina Vuori. Identification of novel drugs for glioblastoma using chemical biology fingerprinting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4671. doi:10.1158/1538-7445.AM2015-4671 [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00275067&atom=%2Fcanres%2F75%2F15_Supplement%2F4671.atom
Harshil Dhruv - One of the best experts on this subject based on the ideXlab platform.
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ATPS-16IDENTIFICATION OF CLINICALLY RELEVANT DRUGS FOR GLIOBLASTOMA WITH SPECIFIC MOLECULAR Signature USING CHEMICAL BIOLOGY FINGERPRINTING
Neuro-Oncology, 2015Co-Authors: Harshil Dhruv, Darren Finlay, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Kristiina Vouri, Michael E. BerensAbstract:Translating molecular subtyping of glioblastoma (GBM) into therapeutically actionable guidance remains an unfulfilled opportunity. A central challenge is to discover therapeutic targets matched to agents (drugs or tool compounds) that are predictably present in subclasses of GBM. Transcriptional patterns in GBM cases in TCGA can identify 12 distinct molecular contexts (mC), where, across a subset of genes and tumors, conditional dependencies of gene expression (signal & echo relationships) consistently emerge. We deployed a panel of 64 patient-derived xenografts from GBM patients whose gene expression profiles allow mapping against the same 12 molecular contexts identified from TCGA cases. These clinically-relevant, preclinical models afford bioinformatics mining for actionable targets as well as chemical library screening for activity against short-term cultures derived from the PDX models. Utilizing a technique we term Chemical Biology Fingerprinting (CBF), we interrogated a series of GBM PDX models using a small chemical library (650 compounds) of clinically relevant anti-cancer agents to uncover context-specific chemovulnerabilities. Preliminary data demonstrated that, mC14 GBM (GBM59, SF7300, GBM116), characterized by mt-P53 and transcriptional similarity to GBM proneural subtype, show distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4 GBM (GBM91, GBM102), characterized by CHEK2 and NF1 mutations and transcriptional patterns similar to GBM mesenchymal subtype. To clinically-validate an ATO Vulnerability Signature, we acquired 22 treatment naive archival patient samples, who were part of Phase I/II clinical trial to study efficacy of ATO and Temozolomide (TMZ) in combination with radiation in treatment of high grade gliomas (NCT00275067) and which exhibited varied survival with ATO treatment (91 days to >1000 days), and determined their molecular context classification using whole transcriptome sequencing (RNAseq). In summary, we demonstrate a subclassification of GBM into novel contexts and we also show that these contexts are differentially sensitive to clinically relevant drugs. Supported by NIH U01CA168397.
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Abstract 4671: Identification of novel drugs for glioblastoma using chemical biology fingerprinting
Clinical Research (Excluding Clinical Trials), 2015Co-Authors: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Michael E. Berens, Kristiina VuoriAbstract:Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Glioblastoma Multiforme (GBM) is an aggressive brain tumor with very poor prognosis and extremely limited therapeutic options. GBM is the most common malignant brain tumor and the search for novel targets and/ or the repurposing of already extant drugs to treat the disease is therefore of utmost importance. We describe here a comprehensive multidisciplinary approach to identifying said targets and ergo potential therapies. We have applied a novel analytical strategy to The Cancer Genome Atlas (TCGA) GBM expression data to stratify GBM into novel subtypes we call molecular contexts, or mCs. Subsequently, a panel of patient-derived GBM xenografts was ascribed to our novel mCs. Utilizing a technique we term Chemical Biology Fingerprinting, or CBF, short-term cultures derived from these clinically-relevant preclinical models were screened for chemosensitivity with a deeply annotated, yet clinically relevant, chemical library. Agents that were statistically more toxic to one context than another were then re-tested in true drug dose response experiments to confirm sensitivity. Preliminary data demonstrated that mC14, characterized by mutant p53 and transcriptionally similar to the GBM proneural subtype, showed distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4, enriched for NF1 mutations and with transcriptional patterns similar to the GBM mesenchymal subtype. To validate the ATO Vulnerability Signature in GBM, we acquired 20 treatment naive archival patient samples, that were part of a Phase I/II clinical trial to study the efficacy of ATO and Temozolomide in combination with ionizing radiation ([NCT00275067][1]). Participants in the trial exhibited varied survival with ATO treatment (91 days to >1000 days) and the clinical samples were subtyped into our molecular contexts using RNAseq data. In summary, we demonstrate a subclassification of GBM into novel molecular contexts (mCs) and show that these contexts are differentially sensitive to clinically relevant drugs. Citation Format: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey Raizer, Michael Berens, Kristiina Vuori. Identification of novel drugs for glioblastoma using chemical biology fingerprinting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4671. doi:10.1158/1538-7445.AM2015-4671 [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00275067&atom=%2Fcanres%2F75%2F15_Supplement%2F4671.atom
Darren Finlay - One of the best experts on this subject based on the ideXlab platform.
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ATPS-16IDENTIFICATION OF CLINICALLY RELEVANT DRUGS FOR GLIOBLASTOMA WITH SPECIFIC MOLECULAR Signature USING CHEMICAL BIOLOGY FINGERPRINTING
Neuro-Oncology, 2015Co-Authors: Harshil Dhruv, Darren Finlay, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Kristiina Vouri, Michael E. BerensAbstract:Translating molecular subtyping of glioblastoma (GBM) into therapeutically actionable guidance remains an unfulfilled opportunity. A central challenge is to discover therapeutic targets matched to agents (drugs or tool compounds) that are predictably present in subclasses of GBM. Transcriptional patterns in GBM cases in TCGA can identify 12 distinct molecular contexts (mC), where, across a subset of genes and tumors, conditional dependencies of gene expression (signal & echo relationships) consistently emerge. We deployed a panel of 64 patient-derived xenografts from GBM patients whose gene expression profiles allow mapping against the same 12 molecular contexts identified from TCGA cases. These clinically-relevant, preclinical models afford bioinformatics mining for actionable targets as well as chemical library screening for activity against short-term cultures derived from the PDX models. Utilizing a technique we term Chemical Biology Fingerprinting (CBF), we interrogated a series of GBM PDX models using a small chemical library (650 compounds) of clinically relevant anti-cancer agents to uncover context-specific chemovulnerabilities. Preliminary data demonstrated that, mC14 GBM (GBM59, SF7300, GBM116), characterized by mt-P53 and transcriptional similarity to GBM proneural subtype, show distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4 GBM (GBM91, GBM102), characterized by CHEK2 and NF1 mutations and transcriptional patterns similar to GBM mesenchymal subtype. To clinically-validate an ATO Vulnerability Signature, we acquired 22 treatment naive archival patient samples, who were part of Phase I/II clinical trial to study efficacy of ATO and Temozolomide (TMZ) in combination with radiation in treatment of high grade gliomas (NCT00275067) and which exhibited varied survival with ATO treatment (91 days to >1000 days), and determined their molecular context classification using whole transcriptome sequencing (RNAseq). In summary, we demonstrate a subclassification of GBM into novel contexts and we also show that these contexts are differentially sensitive to clinically relevant drugs. Supported by NIH U01CA168397.
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Abstract 4671: Identification of novel drugs for glioblastoma using chemical biology fingerprinting
Clinical Research (Excluding Clinical Trials), 2015Co-Authors: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Michael E. Berens, Kristiina VuoriAbstract:Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Glioblastoma Multiforme (GBM) is an aggressive brain tumor with very poor prognosis and extremely limited therapeutic options. GBM is the most common malignant brain tumor and the search for novel targets and/ or the repurposing of already extant drugs to treat the disease is therefore of utmost importance. We describe here a comprehensive multidisciplinary approach to identifying said targets and ergo potential therapies. We have applied a novel analytical strategy to The Cancer Genome Atlas (TCGA) GBM expression data to stratify GBM into novel subtypes we call molecular contexts, or mCs. Subsequently, a panel of patient-derived GBM xenografts was ascribed to our novel mCs. Utilizing a technique we term Chemical Biology Fingerprinting, or CBF, short-term cultures derived from these clinically-relevant preclinical models were screened for chemosensitivity with a deeply annotated, yet clinically relevant, chemical library. Agents that were statistically more toxic to one context than another were then re-tested in true drug dose response experiments to confirm sensitivity. Preliminary data demonstrated that mC14, characterized by mutant p53 and transcriptionally similar to the GBM proneural subtype, showed distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4, enriched for NF1 mutations and with transcriptional patterns similar to the GBM mesenchymal subtype. To validate the ATO Vulnerability Signature in GBM, we acquired 20 treatment naive archival patient samples, that were part of a Phase I/II clinical trial to study the efficacy of ATO and Temozolomide in combination with ionizing radiation ([NCT00275067][1]). Participants in the trial exhibited varied survival with ATO treatment (91 days to >1000 days) and the clinical samples were subtyped into our molecular contexts using RNAseq data. In summary, we demonstrate a subclassification of GBM into novel molecular contexts (mCs) and show that these contexts are differentially sensitive to clinically relevant drugs. Citation Format: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey Raizer, Michael Berens, Kristiina Vuori. Identification of novel drugs for glioblastoma using chemical biology fingerprinting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4671. doi:10.1158/1538-7445.AM2015-4671 [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00275067&atom=%2Fcanres%2F75%2F15_Supplement%2F4671.atom
Junchen Jiang - One of the best experts on this subject based on the ideXlab platform.
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netshield massive semantics based Vulnerability Signature matching for high speed networks
ACM Special Interest Group on Data Communication, 2010Co-Authors: Gao Xia, Hongyu Gao, Yi Tang, Yan Chen, Bin Liu, Junchen JiangAbstract:Accuracy and speed are the two most important metrics for Network Intrusion Detection/Prevention Systems (NIDS/NIPSes). Due to emerging polymorphic attacks and the fact that in many cases regular expressions (regexes) cannot capture the Vulnerability conditions accurately, the accuracy of existing regex-based NIDS/NIPS systems has become a serious problem. In contrast, the recently-proposed Vulnerability Signatures (a.k.a data patches) can exactly describe the Vulnerability conditions and achieve better accuracy. However, how to efficiently apply Vulnerability Signatures to high speed NIDS/NIPS with a large ruleset remains an untouched but challenging issue. This paper presents the first systematic design of Vulnerability Signature based parsing and matching engine, NetShield, which achieves multi-gigabit throughput while offering much better accuracy. Particularly, we made the following contributions: (i) we proposed a candidate selection algorithm which efficiently matches thousands of Vulnerability Signatures simultaneously requiring a small amount of memory; (ii) we proposed an automatic lightweight parsing state machine achieving fast protocol parsing. Experimental results show that the core engine of NetShield achieves at least 1.9+Gbps Signature matching throughput on a 3.8GHz single-core PC, and can scale-up to at least 11+Gbps under a 8-core machine for 794 HTTP Vulnerability Signatures.
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SIGCOMM - NetShield: massive semantics-based Vulnerability Signature matching for high-speed networks
Proceedings of the ACM SIGCOMM 2010 conference on SIGCOMM - SIGCOMM '10, 2010Co-Authors: Gao Xia, Hongyu Gao, Yi Tang, Yan Chen, Bin Liu, Junchen JiangAbstract:Accuracy and speed are the two most important metrics for Network Intrusion Detection/Prevention Systems (NIDS/NIPSes). Due to emerging polymorphic attacks and the fact that in many cases regular expressions (regexes) cannot capture the Vulnerability conditions accurately, the accuracy of existing regex-based NIDS/NIPS systems has become a serious problem. In contrast, the recently-proposed Vulnerability Signatures (a.k.a data patches) can exactly describe the Vulnerability conditions and achieve better accuracy. However, how to efficiently apply Vulnerability Signatures to high speed NIDS/NIPS with a large ruleset remains an untouched but challenging issue. This paper presents the first systematic design of Vulnerability Signature based parsing and matching engine, NetShield, which achieves multi-gigabit throughput while offering much better accuracy. Particularly, we made the following contributions: (i) we proposed a candidate selection algorithm which efficiently matches thousands of Vulnerability Signatures simultaneously requiring a small amount of memory; (ii) we proposed an automatic lightweight parsing state machine achieving fast protocol parsing. Experimental results show that the core engine of NetShield achieves at least 1.9+Gbps Signature matching throughput on a 3.8GHz single-core PC, and can scale-up to at least 11+Gbps under a 8-core machine for 794 HTTP Vulnerability Signatures.
Sen Peng - One of the best experts on this subject based on the ideXlab platform.
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ATPS-16IDENTIFICATION OF CLINICALLY RELEVANT DRUGS FOR GLIOBLASTOMA WITH SPECIFIC MOLECULAR Signature USING CHEMICAL BIOLOGY FINGERPRINTING
Neuro-Oncology, 2015Co-Authors: Harshil Dhruv, Darren Finlay, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Kristiina Vouri, Michael E. BerensAbstract:Translating molecular subtyping of glioblastoma (GBM) into therapeutically actionable guidance remains an unfulfilled opportunity. A central challenge is to discover therapeutic targets matched to agents (drugs or tool compounds) that are predictably present in subclasses of GBM. Transcriptional patterns in GBM cases in TCGA can identify 12 distinct molecular contexts (mC), where, across a subset of genes and tumors, conditional dependencies of gene expression (signal & echo relationships) consistently emerge. We deployed a panel of 64 patient-derived xenografts from GBM patients whose gene expression profiles allow mapping against the same 12 molecular contexts identified from TCGA cases. These clinically-relevant, preclinical models afford bioinformatics mining for actionable targets as well as chemical library screening for activity against short-term cultures derived from the PDX models. Utilizing a technique we term Chemical Biology Fingerprinting (CBF), we interrogated a series of GBM PDX models using a small chemical library (650 compounds) of clinically relevant anti-cancer agents to uncover context-specific chemovulnerabilities. Preliminary data demonstrated that, mC14 GBM (GBM59, SF7300, GBM116), characterized by mt-P53 and transcriptional similarity to GBM proneural subtype, show distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4 GBM (GBM91, GBM102), characterized by CHEK2 and NF1 mutations and transcriptional patterns similar to GBM mesenchymal subtype. To clinically-validate an ATO Vulnerability Signature, we acquired 22 treatment naive archival patient samples, who were part of Phase I/II clinical trial to study efficacy of ATO and Temozolomide (TMZ) in combination with radiation in treatment of high grade gliomas (NCT00275067) and which exhibited varied survival with ATO treatment (91 days to >1000 days), and determined their molecular context classification using whole transcriptome sequencing (RNAseq). In summary, we demonstrate a subclassification of GBM into novel contexts and we also show that these contexts are differentially sensitive to clinically relevant drugs. Supported by NIH U01CA168397.
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Abstract 4671: Identification of novel drugs for glioblastoma using chemical biology fingerprinting
Clinical Research (Excluding Clinical Trials), 2015Co-Authors: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey J. Raizer, Michael E. Berens, Kristiina VuoriAbstract:Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Glioblastoma Multiforme (GBM) is an aggressive brain tumor with very poor prognosis and extremely limited therapeutic options. GBM is the most common malignant brain tumor and the search for novel targets and/ or the repurposing of already extant drugs to treat the disease is therefore of utmost importance. We describe here a comprehensive multidisciplinary approach to identifying said targets and ergo potential therapies. We have applied a novel analytical strategy to The Cancer Genome Atlas (TCGA) GBM expression data to stratify GBM into novel subtypes we call molecular contexts, or mCs. Subsequently, a panel of patient-derived GBM xenografts was ascribed to our novel mCs. Utilizing a technique we term Chemical Biology Fingerprinting, or CBF, short-term cultures derived from these clinically-relevant preclinical models were screened for chemosensitivity with a deeply annotated, yet clinically relevant, chemical library. Agents that were statistically more toxic to one context than another were then re-tested in true drug dose response experiments to confirm sensitivity. Preliminary data demonstrated that mC14, characterized by mutant p53 and transcriptionally similar to the GBM proneural subtype, showed distinct Vulnerability to Arsenic Trioxide (ATO) as compared to mC4, enriched for NF1 mutations and with transcriptional patterns similar to the GBM mesenchymal subtype. To validate the ATO Vulnerability Signature in GBM, we acquired 20 treatment naive archival patient samples, that were part of a Phase I/II clinical trial to study the efficacy of ATO and Temozolomide in combination with ionizing radiation ([NCT00275067][1]). Participants in the trial exhibited varied survival with ATO treatment (91 days to >1000 days) and the clinical samples were subtyped into our molecular contexts using RNAseq data. In summary, we demonstrate a subclassification of GBM into novel molecular contexts (mCs) and show that these contexts are differentially sensitive to clinically relevant drugs. Citation Format: Darren Finlay, Harshil Dhruv, Lisa Evers, Sen Peng, Jeff Kiefer, Seungchan Kim, Jeffrey Raizer, Michael Berens, Kristiina Vuori. Identification of novel drugs for glioblastoma using chemical biology fingerprinting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4671. doi:10.1158/1538-7445.AM2015-4671 [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00275067&atom=%2Fcanres%2F75%2F15_Supplement%2F4671.atom
