Novel cancer drivers: mining the kinome

Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer gen...
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Gespeichert in:

Autor*in:	Biankin, Andrew V [verfasserIn] Grimmond, Sean M

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Cancer Genome Driver Gene Attractive Therapeutic Target Cancer Driver International Cancer Genome Consortium

Anmerkung:	© BioMed Central Ltd 2013

Übergeordnetes Werk:	Enthalten in: Genome medicine - London : BioMed Central, 2009, 5(2013), 2 vom: 28. Feb.
Übergeordnetes Werk:	volume:5 ; year:2013 ; number:2 ; day:28 ; month:02

Links:	Volltext

DOI / URN:	10.1186/gm423

Katalog-ID:	SPR030598680

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spelling	10.1186/gm423 doi (DE-627)SPR030598680 (SPR)gm423-e DE-627 ger DE-627 rakwb eng Biankin, Andrew V verfasserin aut Novel cancer drivers: mining the kinome 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2013 Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads. Cancer Genome (dpeaa)DE-He213 Driver Gene (dpeaa)DE-He213 Attractive Therapeutic Target (dpeaa)DE-He213 Cancer Driver (dpeaa)DE-He213 International Cancer Genome Consortium (dpeaa)DE-He213 Grimmond, Sean M aut Enthalten in Genome medicine London : BioMed Central, 2009 5(2013), 2 vom: 28. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:5 year:2013 number:2 day:28 month:02 https://dx.doi.org/10.1186/gm423 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2013 2 28 02
allfields_unstemmed	10.1186/gm423 doi (DE-627)SPR030598680 (SPR)gm423-e DE-627 ger DE-627 rakwb eng Biankin, Andrew V verfasserin aut Novel cancer drivers: mining the kinome 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2013 Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads. Cancer Genome (dpeaa)DE-He213 Driver Gene (dpeaa)DE-He213 Attractive Therapeutic Target (dpeaa)DE-He213 Cancer Driver (dpeaa)DE-He213 International Cancer Genome Consortium (dpeaa)DE-He213 Grimmond, Sean M aut Enthalten in Genome medicine London : BioMed Central, 2009 5(2013), 2 vom: 28. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:5 year:2013 number:2 day:28 month:02 https://dx.doi.org/10.1186/gm423 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2013 2 28 02
allfieldsGer	10.1186/gm423 doi (DE-627)SPR030598680 (SPR)gm423-e DE-627 ger DE-627 rakwb eng Biankin, Andrew V verfasserin aut Novel cancer drivers: mining the kinome 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2013 Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads. Cancer Genome (dpeaa)DE-He213 Driver Gene (dpeaa)DE-He213 Attractive Therapeutic Target (dpeaa)DE-He213 Cancer Driver (dpeaa)DE-He213 International Cancer Genome Consortium (dpeaa)DE-He213 Grimmond, Sean M aut Enthalten in Genome medicine London : BioMed Central, 2009 5(2013), 2 vom: 28. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:5 year:2013 number:2 day:28 month:02 https://dx.doi.org/10.1186/gm423 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2013 2 28 02
allfieldsSound	10.1186/gm423 doi (DE-627)SPR030598680 (SPR)gm423-e DE-627 ger DE-627 rakwb eng Biankin, Andrew V verfasserin aut Novel cancer drivers: mining the kinome 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2013 Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads. Cancer Genome (dpeaa)DE-He213 Driver Gene (dpeaa)DE-He213 Attractive Therapeutic Target (dpeaa)DE-He213 Cancer Driver (dpeaa)DE-He213 International Cancer Genome Consortium (dpeaa)DE-He213 Grimmond, Sean M aut Enthalten in Genome medicine London : BioMed Central, 2009 5(2013), 2 vom: 28. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:5 year:2013 number:2 day:28 month:02 https://dx.doi.org/10.1186/gm423 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2013 2 28 02
language	English
source	Enthalten in Genome medicine 5(2013), 2 vom: 28. Feb. volume:5 year:2013 number:2 day:28 month:02
sourceStr	Enthalten in Genome medicine 5(2013), 2 vom: 28. Feb. volume:5 year:2013 number:2 day:28 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cancer Genome Driver Gene Attractive Therapeutic Target Cancer Driver International Cancer Genome Consortium
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container_title	Genome medicine
authorswithroles_txt_mv	Biankin, Andrew V @@aut@@ Grimmond, Sean M @@aut@@
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author	Biankin, Andrew V
spellingShingle	Biankin, Andrew V misc Cancer Genome misc Driver Gene misc Attractive Therapeutic Target misc Cancer Driver misc International Cancer Genome Consortium Novel cancer drivers: mining the kinome
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topic_title	Novel cancer drivers: mining the kinome Cancer Genome (dpeaa)DE-He213 Driver Gene (dpeaa)DE-He213 Attractive Therapeutic Target (dpeaa)DE-He213 Cancer Driver (dpeaa)DE-He213 International Cancer Genome Consortium (dpeaa)DE-He213
topic	misc Cancer Genome misc Driver Gene misc Attractive Therapeutic Target misc Cancer Driver misc International Cancer Genome Consortium
topic_unstemmed	misc Cancer Genome misc Driver Gene misc Attractive Therapeutic Target misc Cancer Driver misc International Cancer Genome Consortium
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title	Novel cancer drivers: mining the kinome
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title_full	Novel cancer drivers: mining the kinome
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title_sort	novel cancer drivers: mining the kinome
title_auth	Novel cancer drivers: mining the kinome
abstract	Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads. © BioMed Central Ltd 2013
abstractGer	Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads. © BioMed Central Ltd 2013
abstract_unstemmed	Abstract Large-scale cancer genome studies are unveiling significant complexity and heterogeneity even in histopathologically indistinguishable cancers. Differentiating 'driver' mutations that are functionally relevant from 'passenger' mutations is a major challenge in cancer genomics. While recurrent mutations in a gene provides supporting evidence of 'driver' status, novel computational methods and model systems are greatly improving our ability to identify genes important in carcinogenesis. Reimand and Bader have recently shown that driver gene discovery in discrete gene classes (in this case the kinome) is possible across multiple cancer types and has the potential to yield new druggable targets and clinically relevant leads. © BioMed Central Ltd 2013
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title_short	Novel cancer drivers: mining the kinome
url	https://dx.doi.org/10.1186/gm423
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up_date	2024-07-03T19:01:41.268Z
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