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Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes
by
Niu, Mingming
, Diaz, Alexander K.
, Bai, Bing
, Baker, Suzanne J.
, Paugh, Barbara S.
, Li, Yuxin
, Shirinifard, Abbas
, Thiagarajan, Suresh
, Cho, Ji-Hoon
, Shaw, Timothy I.
, Pagala, Vishwajeeth
, Peng, Junmin
, Sifford, Jeffrey
, Wu, Zhiping
, Tan, Haiyan
, High, Anthony A.
, Zhang, Yang
, Wang, Xusheng
, Tillman, Heather S.
, Wang, Hong
, Zhou, Suiping
, Li, Chunliang
, Hover, Laura D.
, Sablauer, Andras
in
631/114/2391
/ 631/1647/2067
/ 631/67/395
/ 82/16
/ 82/58
/ AKT protein
/ AMP-Activated Protein Kinases - metabolism
/ Animal models
/ Animals
/ Biology
/ Brain
/ Brain cancer
/ Brain Neoplasms - genetics
/ Brain Neoplasms - metabolism
/ Brain tumors
/ Cancer
/ CRISPR
/ Depth profiling
/ Disease Models, Animal
/ Feedback loops
/ Feedback, Physiological
/ Gene expression
/ Gene Expression Profiling
/ Glioma
/ Glioma - genetics
/ Glioma - metabolism
/ gRNA
/ Humanities and Social Sciences
/ Kinases
/ Mass spectrometry
/ Mass spectroscopy
/ Mice
/ Molecular modelling
/ Mouse devices
/ multidisciplinary
/ Mutation
/ Myc protein
/ Oncogene Protein p65(gag-jun) - metabolism
/ Phosphopeptides - metabolism
/ Phosphoproteins - metabolism
/ Positive feedback
/ Proteomes
/ Proteomics
/ Proto-Oncogene Proteins c-akt - metabolism
/ Proto-Oncogene Proteins c-myc - metabolism
/ Receptor, Platelet-Derived Growth Factor alpha - genetics
/ Receptor, trkA - genetics
/ Regulators
/ Science
/ Science (multidisciplinary)
/ Signal Transduction
/ Systems Biology
/ Transcription factors
/ Tumors
/ Up-Regulation
/ Viability
2019
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Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes
by
Niu, Mingming
, Diaz, Alexander K.
, Bai, Bing
, Baker, Suzanne J.
, Paugh, Barbara S.
, Li, Yuxin
, Shirinifard, Abbas
, Thiagarajan, Suresh
, Cho, Ji-Hoon
, Shaw, Timothy I.
, Pagala, Vishwajeeth
, Peng, Junmin
, Sifford, Jeffrey
, Wu, Zhiping
, Tan, Haiyan
, High, Anthony A.
, Zhang, Yang
, Wang, Xusheng
, Tillman, Heather S.
, Wang, Hong
, Zhou, Suiping
, Li, Chunliang
, Hover, Laura D.
, Sablauer, Andras
in
631/114/2391
/ 631/1647/2067
/ 631/67/395
/ 82/16
/ 82/58
/ AKT protein
/ AMP-Activated Protein Kinases - metabolism
/ Animal models
/ Animals
/ Biology
/ Brain
/ Brain cancer
/ Brain Neoplasms - genetics
/ Brain Neoplasms - metabolism
/ Brain tumors
/ Cancer
/ CRISPR
/ Depth profiling
/ Disease Models, Animal
/ Feedback loops
/ Feedback, Physiological
/ Gene expression
/ Gene Expression Profiling
/ Glioma
/ Glioma - genetics
/ Glioma - metabolism
/ gRNA
/ Humanities and Social Sciences
/ Kinases
/ Mass spectrometry
/ Mass spectroscopy
/ Mice
/ Molecular modelling
/ Mouse devices
/ multidisciplinary
/ Mutation
/ Myc protein
/ Oncogene Protein p65(gag-jun) - metabolism
/ Phosphopeptides - metabolism
/ Phosphoproteins - metabolism
/ Positive feedback
/ Proteomes
/ Proteomics
/ Proto-Oncogene Proteins c-akt - metabolism
/ Proto-Oncogene Proteins c-myc - metabolism
/ Receptor, Platelet-Derived Growth Factor alpha - genetics
/ Receptor, trkA - genetics
/ Regulators
/ Science
/ Science (multidisciplinary)
/ Signal Transduction
/ Systems Biology
/ Transcription factors
/ Tumors
/ Up-Regulation
/ Viability
2019
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Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes
by
Niu, Mingming
, Diaz, Alexander K.
, Bai, Bing
, Baker, Suzanne J.
, Paugh, Barbara S.
, Li, Yuxin
, Shirinifard, Abbas
, Thiagarajan, Suresh
, Cho, Ji-Hoon
, Shaw, Timothy I.
, Pagala, Vishwajeeth
, Peng, Junmin
, Sifford, Jeffrey
, Wu, Zhiping
, Tan, Haiyan
, High, Anthony A.
, Zhang, Yang
, Wang, Xusheng
, Tillman, Heather S.
, Wang, Hong
, Zhou, Suiping
, Li, Chunliang
, Hover, Laura D.
, Sablauer, Andras
in
631/114/2391
/ 631/1647/2067
/ 631/67/395
/ 82/16
/ 82/58
/ AKT protein
/ AMP-Activated Protein Kinases - metabolism
/ Animal models
/ Animals
/ Biology
/ Brain
/ Brain cancer
/ Brain Neoplasms - genetics
/ Brain Neoplasms - metabolism
/ Brain tumors
/ Cancer
/ CRISPR
/ Depth profiling
/ Disease Models, Animal
/ Feedback loops
/ Feedback, Physiological
/ Gene expression
/ Gene Expression Profiling
/ Glioma
/ Glioma - genetics
/ Glioma - metabolism
/ gRNA
/ Humanities and Social Sciences
/ Kinases
/ Mass spectrometry
/ Mass spectroscopy
/ Mice
/ Molecular modelling
/ Mouse devices
/ multidisciplinary
/ Mutation
/ Myc protein
/ Oncogene Protein p65(gag-jun) - metabolism
/ Phosphopeptides - metabolism
/ Phosphoproteins - metabolism
/ Positive feedback
/ Proteomes
/ Proteomics
/ Proto-Oncogene Proteins c-akt - metabolism
/ Proto-Oncogene Proteins c-myc - metabolism
/ Receptor, Platelet-Derived Growth Factor alpha - genetics
/ Receptor, trkA - genetics
/ Regulators
/ Science
/ Science (multidisciplinary)
/ Signal Transduction
/ Systems Biology
/ Transcription factors
/ Tumors
/ Up-Regulation
/ Viability
2019
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Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes
Journal Article
Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes
2019
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Overview
High throughput omics approaches provide an unprecedented opportunity for dissecting molecular mechanisms in cancer biology. Here we present deep profiling of whole proteome, phosphoproteome and transcriptome in two high-grade glioma (HGG) mouse models driven by mutated RTK oncogenes,
PDGFRA
and
NTRK1
, analyzing 13,860 proteins and 30,431 phosphosites by mass spectrometry. Systems biology approaches identify numerous master regulators, including 41 kinases and 23 transcription factors. Pathway activity computation and mouse survival indicate the
NTRK1
mutation induces a higher activation of AKT downstream targets including MYC and JUN, drives a positive feedback loop to up-regulate multiple other RTKs, and confers higher oncogenic potency than the
PDGFRA
mutation. A mini-gRNA library CRISPR-Cas9 validation screening shows 56% of tested master regulators are important for the viability of
NTRK
-driven HGG cells, including TFs (Myc and Jun) and metabolic kinases (AMPK
a
1 and AMPK
a
2), confirming the validity of the multiomics integrative approaches, and providing novel tumor vulnerabilities.
Multi-omic profiling is a powerful approach to dissecting molecular mechanisms in disease. Here the authors generate whole proteome, phosphoproteome and transcriptome profiles from two mouse models of high-grade glioma driven by different oncogenes, and validate identified master regulators with a CRISPR screen.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 82/16
/ 82/58
/ AMP-Activated Protein Kinases - metabolism
/ Animals
/ Biology
/ Brain
/ Brain Neoplasms - metabolism
/ Cancer
/ CRISPR
/ Glioma
/ gRNA
/ Humanities and Social Sciences
/ Kinases
/ Mice
/ Mutation
/ Oncogene Protein p65(gag-jun) - metabolism
/ Phosphopeptides - metabolism
/ Phosphoproteins - metabolism
/ Proto-Oncogene Proteins c-akt - metabolism
/ Proto-Oncogene Proteins c-myc - metabolism
/ Receptor, Platelet-Derived Growth Factor alpha - genetics
/ Science
/ Tumors
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