Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS

Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	van Belzen, Ianthe A. E. M. [verfasserIn] Cai, Casey van Tuil, Marc Badloe, Shashi Strengman, Eric Janse, Alex Verwiel, Eugène T. P. van der Leest, Douwe F. M. Kester, Lennart Molenaar, Jan J. Meijerink, Jules Drost, Jarno Peng, Weng Chuan Kerstens, Hindrik H. D. Tops, Bastiaan B. J. Holstege, Frank C. P. Kemmeren, Patrick Hehir-Kwa, Jayne Y.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Gene fusions Chimeric transcripts Pediatric cancer Structural variants Whole genome sequencing RNA sequencing

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: BMC cancer - London : BioMed Central, 2001, 23(2023), 1 vom: 03. Juli
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:1 ; day:03 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s12885-023-11054-3

Katalog-ID:	SPR052136035

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100	1		\|a van Belzen, Ianthe A. E. M. \|e verfasserin \|4 aut
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520			\|a Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making.
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allfields	10.1186/s12885-023-11054-3 doi (DE-627)SPR052136035 (SPR)s12885-023-11054-3-e DE-627 ger DE-627 rakwb eng van Belzen, Ianthe A. E. M. verfasserin aut Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. Gene fusions (dpeaa)DE-He213 Chimeric transcripts (dpeaa)DE-He213 Pediatric cancer (dpeaa)DE-He213 Structural variants (dpeaa)DE-He213 Whole genome sequencing (dpeaa)DE-He213 RNA sequencing (dpeaa)DE-He213 Cai, Casey aut van Tuil, Marc aut Badloe, Shashi aut Strengman, Eric aut Janse, Alex aut Verwiel, Eugène T. P. aut van der Leest, Douwe F. M. aut Kester, Lennart aut Molenaar, Jan J. aut Meijerink, Jules aut Drost, Jarno aut Peng, Weng Chuan aut Kerstens, Hindrik H. D. aut Tops, Bastiaan B. J. aut Holstege, Frank C. P. aut Kemmeren, Patrick aut Hehir-Kwa, Jayne Y. (orcid)0000-0003-0837-304X aut Enthalten in BMC cancer London : BioMed Central, 2001 23(2023), 1 vom: 03. Juli (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:23 year:2023 number:1 day:03 month:07 https://dx.doi.org/10.1186/s12885-023-11054-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 23 2023 1 03 07
spelling	10.1186/s12885-023-11054-3 doi (DE-627)SPR052136035 (SPR)s12885-023-11054-3-e DE-627 ger DE-627 rakwb eng van Belzen, Ianthe A. E. M. verfasserin aut Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. Gene fusions (dpeaa)DE-He213 Chimeric transcripts (dpeaa)DE-He213 Pediatric cancer (dpeaa)DE-He213 Structural variants (dpeaa)DE-He213 Whole genome sequencing (dpeaa)DE-He213 RNA sequencing (dpeaa)DE-He213 Cai, Casey aut van Tuil, Marc aut Badloe, Shashi aut Strengman, Eric aut Janse, Alex aut Verwiel, Eugène T. P. aut van der Leest, Douwe F. M. aut Kester, Lennart aut Molenaar, Jan J. aut Meijerink, Jules aut Drost, Jarno aut Peng, Weng Chuan aut Kerstens, Hindrik H. D. aut Tops, Bastiaan B. J. aut Holstege, Frank C. P. aut Kemmeren, Patrick aut Hehir-Kwa, Jayne Y. (orcid)0000-0003-0837-304X aut Enthalten in BMC cancer London : BioMed Central, 2001 23(2023), 1 vom: 03. Juli (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:23 year:2023 number:1 day:03 month:07 https://dx.doi.org/10.1186/s12885-023-11054-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 23 2023 1 03 07
allfields_unstemmed	10.1186/s12885-023-11054-3 doi (DE-627)SPR052136035 (SPR)s12885-023-11054-3-e DE-627 ger DE-627 rakwb eng van Belzen, Ianthe A. E. M. verfasserin aut Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. Gene fusions (dpeaa)DE-He213 Chimeric transcripts (dpeaa)DE-He213 Pediatric cancer (dpeaa)DE-He213 Structural variants (dpeaa)DE-He213 Whole genome sequencing (dpeaa)DE-He213 RNA sequencing (dpeaa)DE-He213 Cai, Casey aut van Tuil, Marc aut Badloe, Shashi aut Strengman, Eric aut Janse, Alex aut Verwiel, Eugène T. P. aut van der Leest, Douwe F. M. aut Kester, Lennart aut Molenaar, Jan J. aut Meijerink, Jules aut Drost, Jarno aut Peng, Weng Chuan aut Kerstens, Hindrik H. D. aut Tops, Bastiaan B. J. aut Holstege, Frank C. P. aut Kemmeren, Patrick aut Hehir-Kwa, Jayne Y. (orcid)0000-0003-0837-304X aut Enthalten in BMC cancer London : BioMed Central, 2001 23(2023), 1 vom: 03. Juli (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:23 year:2023 number:1 day:03 month:07 https://dx.doi.org/10.1186/s12885-023-11054-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 23 2023 1 03 07
allfieldsGer	10.1186/s12885-023-11054-3 doi (DE-627)SPR052136035 (SPR)s12885-023-11054-3-e DE-627 ger DE-627 rakwb eng van Belzen, Ianthe A. E. M. verfasserin aut Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. Gene fusions (dpeaa)DE-He213 Chimeric transcripts (dpeaa)DE-He213 Pediatric cancer (dpeaa)DE-He213 Structural variants (dpeaa)DE-He213 Whole genome sequencing (dpeaa)DE-He213 RNA sequencing (dpeaa)DE-He213 Cai, Casey aut van Tuil, Marc aut Badloe, Shashi aut Strengman, Eric aut Janse, Alex aut Verwiel, Eugène T. P. aut van der Leest, Douwe F. M. aut Kester, Lennart aut Molenaar, Jan J. aut Meijerink, Jules aut Drost, Jarno aut Peng, Weng Chuan aut Kerstens, Hindrik H. D. aut Tops, Bastiaan B. J. aut Holstege, Frank C. P. aut Kemmeren, Patrick aut Hehir-Kwa, Jayne Y. (orcid)0000-0003-0837-304X aut Enthalten in BMC cancer London : BioMed Central, 2001 23(2023), 1 vom: 03. Juli (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:23 year:2023 number:1 day:03 month:07 https://dx.doi.org/10.1186/s12885-023-11054-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 23 2023 1 03 07
allfieldsSound	10.1186/s12885-023-11054-3 doi (DE-627)SPR052136035 (SPR)s12885-023-11054-3-e DE-627 ger DE-627 rakwb eng van Belzen, Ianthe A. E. M. verfasserin aut Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. Gene fusions (dpeaa)DE-He213 Chimeric transcripts (dpeaa)DE-He213 Pediatric cancer (dpeaa)DE-He213 Structural variants (dpeaa)DE-He213 Whole genome sequencing (dpeaa)DE-He213 RNA sequencing (dpeaa)DE-He213 Cai, Casey aut van Tuil, Marc aut Badloe, Shashi aut Strengman, Eric aut Janse, Alex aut Verwiel, Eugène T. P. aut van der Leest, Douwe F. M. aut Kester, Lennart aut Molenaar, Jan J. aut Meijerink, Jules aut Drost, Jarno aut Peng, Weng Chuan aut Kerstens, Hindrik H. D. aut Tops, Bastiaan B. J. aut Holstege, Frank C. P. aut Kemmeren, Patrick aut Hehir-Kwa, Jayne Y. (orcid)0000-0003-0837-304X aut Enthalten in BMC cancer London : BioMed Central, 2001 23(2023), 1 vom: 03. Juli (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:23 year:2023 number:1 day:03 month:07 https://dx.doi.org/10.1186/s12885-023-11054-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 23 2023 1 03 07
language	English
source	Enthalten in BMC cancer 23(2023), 1 vom: 03. Juli volume:23 year:2023 number:1 day:03 month:07
sourceStr	Enthalten in BMC cancer 23(2023), 1 vom: 03. Juli volume:23 year:2023 number:1 day:03 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Gene fusions Chimeric transcripts Pediatric cancer Structural variants Whole genome sequencing RNA sequencing
isfreeaccess_bool	true
container_title	BMC cancer
authorswithroles_txt_mv	van Belzen, Ianthe A. E. M. @@aut@@ Cai, Casey @@aut@@ van Tuil, Marc @@aut@@ Badloe, Shashi @@aut@@ Strengman, Eric @@aut@@ Janse, Alex @@aut@@ Verwiel, Eugène T. P. @@aut@@ van der Leest, Douwe F. M. @@aut@@ Kester, Lennart @@aut@@ Molenaar, Jan J. @@aut@@ Meijerink, Jules @@aut@@ Drost, Jarno @@aut@@ Peng, Weng Chuan @@aut@@ Kerstens, Hindrik H. D. @@aut@@ Tops, Bastiaan B. J. @@aut@@ Holstege, Frank C. P. @@aut@@ Kemmeren, Patrick @@aut@@ Hehir-Kwa, Jayne Y. @@aut@@
publishDateDaySort_date	2023-07-03T00:00:00Z
hierarchy_top_id	326643710
id	SPR052136035
language_de	englisch
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M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. 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author	van Belzen, Ianthe A. E. M.
spellingShingle	van Belzen, Ianthe A. E. M. misc Gene fusions misc Chimeric transcripts misc Pediatric cancer misc Structural variants misc Whole genome sequencing misc RNA sequencing Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS
authorStr	van Belzen, Ianthe A. E. M.
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topic_title	Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS Gene fusions (dpeaa)DE-He213 Chimeric transcripts (dpeaa)DE-He213 Pediatric cancer (dpeaa)DE-He213 Structural variants (dpeaa)DE-He213 Whole genome sequencing (dpeaa)DE-He213 RNA sequencing (dpeaa)DE-He213
topic	misc Gene fusions misc Chimeric transcripts misc Pediatric cancer misc Structural variants misc Whole genome sequencing misc RNA sequencing
topic_unstemmed	misc Gene fusions misc Chimeric transcripts misc Pediatric cancer misc Structural variants misc Whole genome sequencing misc RNA sequencing
topic_browse	misc Gene fusions misc Chimeric transcripts misc Pediatric cancer misc Structural variants misc Whole genome sequencing misc RNA sequencing
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title	Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS
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title_full	Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS
author_sort	van Belzen, Ianthe A. E. M.
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author_browse	van Belzen, Ianthe A. E. M. Cai, Casey van Tuil, Marc Badloe, Shashi Strengman, Eric Janse, Alex Verwiel, Eugène T. P. van der Leest, Douwe F. M. Kester, Lennart Molenaar, Jan J. Meijerink, Jules Drost, Jarno Peng, Weng Chuan Kerstens, Hindrik H. D. Tops, Bastiaan B. J. Holstege, Frank C. P. Kemmeren, Patrick Hehir-Kwa, Jayne Y.
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author-letter	van Belzen, Ianthe A. E. M.
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title_sort	systematic discovery of gene fusions in pediatric cancer by integrating rna-seq and wgs
title_auth	Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS
abstract	Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. © The Author(s) 2023
abstractGer	Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. © The Author(s) 2023
abstract_unstemmed	Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making. © The Author(s) 2023
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title_short	Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS
url	https://dx.doi.org/10.1186/s12885-023-11054-3
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author2	Cai, Casey van Tuil, Marc Badloe, Shashi Strengman, Eric Janse, Alex Verwiel, Eugène T. P. van der Leest, Douwe F. M. Kester, Lennart Molenaar, Jan J. Meijerink, Jules Drost, Jarno Peng, Weng Chuan Kerstens, Hindrik H. D. Tops, Bastiaan B. J. Holstege, Frank C. P. Kemmeren, Patrick Hehir-Kwa, Jayne Y.
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Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. 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Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS

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