A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 ( NF1 ) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do n...

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Published inBMC genomics Vol. 18; no. 1; p. 127
Main Authors Way, Gregory P., Allaway, Robert J., Bouley, Stephanie J., Fadul, Camilo E., Sanchez, Yolanda, Greene, Casey S.
Format Journal Article
LanguageEnglish
Published London BioMed Central 06.02.2017
BioMed Central Ltd
Springer Nature B.V
Subjects
Analysis
Animal Genetics and Genomics
Biomedical and Life Sciences
Brain cancer
Brain tumors
Cancer
Cell Line, Tumor
Classifiers
Computational Biology - methods
Deactivation
Deoxyribonucleic acid
DNA
DNA microarrays
Gene expression
Gene mutation
Gene Silencing
Genetic disorders
Genomes
Genomics
Glioblastoma
Glioblastoma - pathology
Glioma
Glioma cells
Health aspects
Human and rodent genomics
Humans
Inactivation
Kinases
Learning algorithms
Lethality
Life Sciences
Machine Learning
Microarrays
Microbial Genetics and Genomics
Mutation
Neurofibroma
Neurofibromin 1
Neurofibromin 1 - genetics
Neurological disorders
Phosphorylation
Plant Genetics and Genomics
Precision medicine
Proteins
Proteomics
Research Article
Ribonucleic acid
RNA
Transcription
Transcription factors
Transcriptome
Tumor suppressor genes
Tumors
Classifier
Machine Learning
NF1 Inactivation
Glioblastoma
Neurofibromatosis Type I
Cancer
Online AccessGet full text
ISSN1471-2164
1471-2164
DOI10.1186/s12864-017-3519-7

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Abstract Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 ( NF1 ) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. Results We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 – 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 – 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier’s NF1 score was associated with NF1 protein concentration in these samples. Conclusions We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.
AbstractList We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 - 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 - 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier's NF1 score was associated with NF1 protein concentration in these samples. We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.
Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. Results We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 – 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 – 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier’s NF1 score was associated with NF1 protein concentration in these samples. Conclusions We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.
We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 - 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 - 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier's NF1 score was associated with NF1 protein concentration in these samples. We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.
Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. Results We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 - 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 - 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier's NF1 score was associated with NF1 protein concentration in these samples. Conclusions We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine. Keywords: Neurofibromatosis Type I, Glioblastoma, Machine Learning, Cancer, NF1 Inactivation, Classifier
Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 ( NF1 ) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. Results We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 – 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 – 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier’s NF1 score was associated with NF1 protein concentration in these samples. Conclusions We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.
We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules.BACKGROUNDWe have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules.We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 - 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 - 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier's NF1 score was associated with NF1 protein concentration in these samples.RESULTSWe developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 - 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 - 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier's NF1 score was associated with NF1 protein concentration in these samples.We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.CONCLUSIONSWe demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.
ArticleNumber 127
Audience Academic
Author Greene, Casey S.
Sanchez, Yolanda
Fadul, Camilo E.
Bouley, Stephanie J.
Allaway, Robert J.
Way, Gregory P.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/28166733$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1158/1078-0432.CCR-13-1943
10.1016/j.ccr.2008.01.003
10.1038/srep24968
10.4103/2229-5178.115515
10.1093/nar/24.3.478
10.1093/nar/gks1055
10.1136/jmg.39.5.311
10.7717/peerj.1621
10.1158/2159-8290.CD-13-0146
10.1093/nar/gkv177
10.18632/oncotarget.7718
10.1093/nar/gku1179
10.1158/0008-5472.CAN-12-3848
10.1371/journal.pcbi.1004890
10.1186/1471-2105-12-322
10.1093/nar/gng015
10.1038/nrc3911
10.1111/j.1471-4159.2008.05749.x
10.1145/2723872.2723882
10.1016/j.ccr.2009.05.009
10.1038/353864a0
10.1128/mSystems.00025-15
10.1371/journal.pgen.1001093
10.1016/j.celrep.2015.07.012
10.1038/75556
10.1093/bioinformatics/btt480
10.1016/S0092-8674(01)00245-8
10.1016/j.semcdb.2016.02.007
10.3150/13-BEJSP14
10.1523/JNEUROSCI.22-21-09194.2002
10.1016/j.jaad.2008.12.051
10.1158/1535-7163.MCT-11-0309
10.1016/0092-8674(90)90150-D
10.1016/j.ccr.2009.12.020
10.1371/journal.pone.0082144
10.1186/1471-2164-9-S1-S13
10.1002/path.2633
10.1016/0092-8674(90)90024-9
10.3892/or.2014.3469
10.1038/cdd.2014.12
10.1371/journal.pcbi.1004790
10.1371/journal.pone.0049570
10.1126/scitranslmed.aaf6853
10.1093/hmg/ddm059
10.1210/jc.2007-1253
10.1016/j.cell.2013.09.034
10.3389/fgene.2014.00115
10.1093/nar/gkn698
10.1038/nm.2158
10.1101/gad.862101
10.1093/bioinformatics/btq431
10.18632/oncotarget.2900
10.1158/0008-5472.CAN-08-2590
10.1186/1471-2105-14-198
10.1158/0008-5472.CAN-05-1553
10.1186/gb-2006-7-s1-s12
10.1038/ng.2252
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Issue 1
Keywords Classifier
Machine Learning
NF1 Inactivation
Glioblastoma
Neurofibromatosis Type I
Cancer
Language English
License Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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References PE Hollstein (3519_CR25) 2013; 3
J Cohen (3519_CR22) 1969
C-M Li (3519_CR54) 2006; 66
A Sokolov (3519_CR20) 2016; 12
GR Skuse (3519_CR13) 1996; 24
Y Liang (3519_CR19) 2013; 14
S Akamatsu (3519_CR55) 2015; 12
CW Brennan (3519_CR16) 2013; 155
E Thouënnon (3519_CR50) 2007; 92
J Guinney (3519_CR18) 2014; 20
3519_CR15
J Bageritz (3519_CR52) 2014; 21
G Xu (3519_CR2) 1990; 62
C Boettiger (3519_CR32) 2015; 49
SE Reese (3519_CR29) 2013; 29
The Gene Ontology Consortium (3519_CR36) 2015; 43
M Allen (3519_CR23) 2016; 8
J Tan (3519_CR41) 2016; 1
M Molla (3519_CR37) 2004; 25
Y Zhu (3519_CR58) 2001; 15
B Yu (3519_CR43) 2013; 19
M Wood (3519_CR8) 2011; 10
BS Carvalho (3519_CR27) 2010; 26
JL-Y Chen (3519_CR44) 2010; 6
M de Boeck (3519_CR61) 2016; 6
A Kamburov (3519_CR33) 2009; 37
LT McGillicuddy (3519_CR10) 2009; 16
NM Joseph (3519_CR59) 2008; 13
S Subramanian (3519_CR11) 2010; 220
N Almog (3519_CR47) 2009; 69
3519_CR48
RGW Verhaak (3519_CR63) 2010; 17
Y Xu (3519_CR49) 2002; 22
K Cichowski (3519_CR14) 2001; 104
RA Irizarry (3519_CR28) 2003; 31
DGR Evans (3519_CR5) 2002; 39
M Bastani (3519_CR38) 2013; 8
MR Wallace (3519_CR12) 1991; 353
3519_CR31
X Tan (3519_CR26) 2012; 7
JA Thompson (3519_CR17) 2016; 4
Z Sheng (3519_CR56) 2010; 16
A Morishita (3519_CR60) 2013; 73
RJ Allaway (3519_CR9) 2016; 7
N Ratner (3519_CR7) 2015; 15
M Ashburner (3519_CR35) 2000; 25
JA Miller (3519_CR30) 2011; 12
X Deng (3519_CR53) 2014; 32
T Willer (3519_CR45) 2012; 44
DP Noren (3519_CR42) 2016; 12
M Pirooznia (3519_CR39) 2008; 9
GC Daginakatte (3519_CR24) 2007; 16
W-C Chou (3519_CR40) 2015; 43
3519_CR62
KP Boyd (3519_CR3) 2009; 61
3519_CR21
LA Greene (3519_CR57) 2009; 108
E Rad (3519_CR6) 2016; 52
A Kamburov (3519_CR34) 2013; 41
GA Martin (3519_CR1) 1990; 63
Q Cheng (3519_CR51) 2015; 6
D Thierry-Mieg (3519_CR46) 2006; 7
B Dogra (3519_CR4) 2013; 4
References_xml – volume: 20
  start-page: 265
  year: 2014
  ident: 3519_CR18
  publication-title: Clin Cancer Res
  doi: 10.1158/1078-0432.CCR-13-1943
– volume: 13
  start-page: 129
  year: 2008
  ident: 3519_CR59
  publication-title: Cancer Cell
  doi: 10.1016/j.ccr.2008.01.003
– volume: 6
  start-page: 24968
  year: 2016
  ident: 3519_CR61
  publication-title: Sci Rep
  doi: 10.1038/srep24968
– volume: 4
  start-page: 195
  year: 2013
  ident: 3519_CR4
  publication-title: Indian Dermatol Online J
  doi: 10.4103/2229-5178.115515
– volume: 24
  start-page: 478
  year: 1996
  ident: 3519_CR13
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/24.3.478
– volume: 41
  start-page: D793
  year: 2013
  ident: 3519_CR34
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gks1055
– volume: 39
  start-page: 311
  year: 2002
  ident: 3519_CR5
  publication-title: J Med Genet
  doi: 10.1136/jmg.39.5.311
– volume: 4
  start-page: e1621
  year: 2016
  ident: 3519_CR17
  publication-title: Peer J
  doi: 10.7717/peerj.1621
– volume: 3
  start-page: 880
  year: 2013
  ident: 3519_CR25
  publication-title: Cancer Discov
  doi: 10.1158/2159-8290.CD-13-0146
– volume: 43
  start-page: e67
  year: 2015
  ident: 3519_CR40
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkv177
– volume: 7
  start-page: 17087
  year: 2016
  ident: 3519_CR9
  publication-title: Oncotarget
  doi: 10.18632/oncotarget.7718
– volume: 43
  start-page: D1049
  year: 2015
  ident: 3519_CR36
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gku1179
– volume: 25
  start-page: 23
  year: 2004
  ident: 3519_CR37
  publication-title: AI Mag
– ident: 3519_CR15
– volume: 73
  start-page: 4289
  year: 2013
  ident: 3519_CR60
  publication-title: Cancer Res
  doi: 10.1158/0008-5472.CAN-12-3848
– volume: 12
  start-page: e1004890
  year: 2016
  ident: 3519_CR42
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1004890
– volume: 12
  start-page: 322
  year: 2011
  ident: 3519_CR30
  publication-title: BMC Bioinformatics
  doi: 10.1186/1471-2105-12-322
– volume: 31
  start-page: e15
  year: 2003
  ident: 3519_CR28
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gng015
– volume: 15
  start-page: 290
  year: 2015
  ident: 3519_CR7
  publication-title: Nat Rev Cancer
  doi: 10.1038/nrc3911
– ident: 3519_CR31
– volume: 108
  start-page: 11
  year: 2009
  ident: 3519_CR57
  publication-title: J Neurochem
  doi: 10.1111/j.1471-4159.2008.05749.x
– volume: 49
  start-page: 71
  year: 2015
  ident: 3519_CR32
  publication-title: Oper Syst Rev
  doi: 10.1145/2723872.2723882
– volume: 16
  start-page: 44
  year: 2009
  ident: 3519_CR10
  publication-title: Cancer Cell
  doi: 10.1016/j.ccr.2009.05.009
– volume: 353
  start-page: 864
  year: 1991
  ident: 3519_CR12
  publication-title: Nature
  doi: 10.1038/353864a0
– ident: 3519_CR62
– volume: 1
  start-page: e00025
  year: 2016
  ident: 3519_CR41
  publication-title: mSystems
  doi: 10.1128/mSystems.00025-15
– volume: 6
  start-page: e1001093
  year: 2010
  ident: 3519_CR44
  publication-title: PLoS Genet
  doi: 10.1371/journal.pgen.1001093
– volume: 12
  start-page: 922
  year: 2015
  ident: 3519_CR55
  publication-title: Cell Rep
  doi: 10.1016/j.celrep.2015.07.012
– ident: 3519_CR21
– volume: 25
  start-page: 25
  year: 2000
  ident: 3519_CR35
  publication-title: Nat Genet
  doi: 10.1038/75556
– volume: 29
  start-page: 2877
  year: 2013
  ident: 3519_CR29
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btt480
– volume: 104
  start-page: 593
  year: 2001
  ident: 3519_CR14
  publication-title: Cell
  doi: 10.1016/S0092-8674(01)00245-8
– volume: 52
  start-page: 39
  year: 2016
  ident: 3519_CR6
  publication-title: Semin Cell Dev Biol
  doi: 10.1016/j.semcdb.2016.02.007
– volume: 19
  start-page: 1484
  year: 2013
  ident: 3519_CR43
  publication-title: Bernoulli
  doi: 10.3150/13-BEJSP14
– volume: 22
  start-page: 9194
  year: 2002
  ident: 3519_CR49
  publication-title: J Neurosci Off J Soc Neurosci
  doi: 10.1523/JNEUROSCI.22-21-09194.2002
– volume: 61
  start-page: 1
  year: 2009
  ident: 3519_CR3
  publication-title: J Am Acad Dermatol
  doi: 10.1016/j.jaad.2008.12.051
– volume: 10
  start-page: 1740
  year: 2011
  ident: 3519_CR8
  publication-title: Mol Cancer Ther
  doi: 10.1158/1535-7163.MCT-11-0309
– volume: 63
  start-page: 843
  year: 1990
  ident: 3519_CR1
  publication-title: Cell
  doi: 10.1016/0092-8674(90)90150-D
– volume: 17
  start-page: 98
  year: 2010
  ident: 3519_CR63
  publication-title: Cancer Cell
  doi: 10.1016/j.ccr.2009.12.020
– volume: 8
  start-page: e82144
  year: 2013
  ident: 3519_CR38
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0082144
– volume: 9
  start-page: S13
  year: 2008
  ident: 3519_CR39
  publication-title: BMC Genomics
  doi: 10.1186/1471-2164-9-S1-S13
– volume: 220
  start-page: 58
  year: 2010
  ident: 3519_CR11
  publication-title: J Pathol
  doi: 10.1002/path.2633
– volume: 62
  start-page: 599
  year: 1990
  ident: 3519_CR2
  publication-title: Cell
  doi: 10.1016/0092-8674(90)90024-9
– volume: 32
  start-page: 2159
  year: 2014
  ident: 3519_CR53
  publication-title: Oncol Rep
  doi: 10.3892/or.2014.3469
– volume: 21
  start-page: 929
  year: 2014
  ident: 3519_CR52
  publication-title: Cell Death Differ
  doi: 10.1038/cdd.2014.12
– volume: 12
  start-page: e1004790
  year: 2016
  ident: 3519_CR20
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1004790
– volume: 7
  start-page: e49570
  year: 2012
  ident: 3519_CR26
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0049570
– volume-title: Statistical power analysis for the behavioral sciences
  year: 1969
  ident: 3519_CR22
– volume: 8
  start-page: 354re3
  year: 2016
  ident: 3519_CR23
  publication-title: Sci Transl Med
  doi: 10.1126/scitranslmed.aaf6853
– volume: 16
  start-page: 1098
  year: 2007
  ident: 3519_CR24
  publication-title: Hum Mol Genet
  doi: 10.1093/hmg/ddm059
– volume: 92
  start-page: 4865
  year: 2007
  ident: 3519_CR50
  publication-title: J Clin Endocrinol Metab
  doi: 10.1210/jc.2007-1253
– volume: 155
  start-page: 462
  year: 2013
  ident: 3519_CR16
  publication-title: Cell
  doi: 10.1016/j.cell.2013.09.034
– ident: 3519_CR48
  doi: 10.3389/fgene.2014.00115
– volume: 37
  start-page: D623
  year: 2009
  ident: 3519_CR33
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkn698
– volume: 16
  start-page: 671
  year: 2010
  ident: 3519_CR56
  publication-title: Nat Med
  doi: 10.1038/nm.2158
– volume: 15
  start-page: 859
  year: 2001
  ident: 3519_CR58
  publication-title: Genes Dev
  doi: 10.1101/gad.862101
– volume: 26
  start-page: 2363
  year: 2010
  ident: 3519_CR27
  publication-title: Bioinforma Oxf Engl
  doi: 10.1093/bioinformatics/btq431
– volume: 6
  start-page: 4733
  year: 2015
  ident: 3519_CR51
  publication-title: Oncotarget
  doi: 10.18632/oncotarget.2900
– volume: 69
  start-page: 836
  year: 2009
  ident: 3519_CR47
  publication-title: Cancer Res
  doi: 10.1158/0008-5472.CAN-08-2590
– volume: 14
  start-page: 198
  year: 2013
  ident: 3519_CR19
  publication-title: BMC Bioinformatics
  doi: 10.1186/1471-2105-14-198
– volume: 66
  start-page: 665
  year: 2006
  ident: 3519_CR54
  publication-title: Cancer Res
  doi: 10.1158/0008-5472.CAN-05-1553
– volume: 7
  start-page: S12.1
  issue: Suppl 1
  year: 2006
  ident: 3519_CR46
  publication-title: Genome Biol
  doi: 10.1186/gb-2006-7-s1-s12
– volume: 44
  start-page: 575
  year: 2012
  ident: 3519_CR45
  publication-title: Nat Genet
  doi: 10.1038/ng.2252
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Snippet Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 ( NF1 ) tumor suppressor gene. However,...
We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing...
Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However,...
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StartPage 127
SubjectTerms Analysis
Animal Genetics and Genomics
Biomedical and Life Sciences
Brain cancer
Brain tumors
Cancer
Cell Line, Tumor
Classifiers
Computational Biology - methods
Deactivation
Deoxyribonucleic acid
DNA
DNA microarrays
Gene expression
Gene mutation
Gene Silencing
Genetic disorders
Genomes
Genomics
Glioblastoma
Glioblastoma - pathology
Glioma
Glioma cells
Health aspects
Human and rodent genomics
Humans
Inactivation
Kinases
Learning algorithms
Lethality
Life Sciences
Machine Learning
Microarrays
Microbial Genetics and Genomics
Mutation
Neurofibroma
Neurofibromin 1
Neurofibromin 1 - genetics
Neurological disorders
Phosphorylation
Plant Genetics and Genomics
Precision medicine
Proteins
Proteomics
Research Article
Ribonucleic acid
RNA
Transcription
Transcription factors
Transcriptome
Tumor suppressor genes
Tumors
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Title A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma
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