Identification of genetic markers with synergistic survival effect in cancer

Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-s...
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Autor*in:	Louhimo, Riku [verfasserIn] Laakso, Marko Heikkinen, Tuomas Laitinen, Susanna Manninen, Pekka Rogojin, Vladimir Miettinen, Minna Blomqvist, Carl Liu, Jianjun Nevanlinna, Heli Hautaniemi, Sampsa

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	False Positive Rate Survival Effect Message Passing Interface Single Nucleotide Polymorphism Marker Marker Pair

Anmerkung:	© Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC systems biology - London : BioMed Central, 2007, 7(2013), Suppl 1 vom: 12. Aug.
Übergeordnetes Werk:	volume:7 ; year:2013 ; number:Suppl 1 ; day:12 ; month:08

Links:	Volltext

DOI / URN:	10.1186/1752-0509-7-S1-S2

Katalog-ID:	SPR028416414

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520			\|a Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival.
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allfields	10.1186/1752-0509-7-S1-S2 doi (DE-627)SPR028416414 (SPR)1752-0509-7-S1-S2-e DE-627 ger DE-627 rakwb eng Louhimo, Riku verfasserin aut Identification of genetic markers with synergistic survival effect in cancer 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. False Positive Rate (dpeaa)DE-He213 Survival Effect (dpeaa)DE-He213 Message Passing Interface (dpeaa)DE-He213 Single Nucleotide Polymorphism Marker (dpeaa)DE-He213 Marker Pair (dpeaa)DE-He213 Laakso, Marko aut Heikkinen, Tuomas aut Laitinen, Susanna aut Manninen, Pekka aut Rogojin, Vladimir aut Miettinen, Minna aut Blomqvist, Carl aut Liu, Jianjun aut Nevanlinna, Heli aut Hautaniemi, Sampsa aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 1 vom: 12. Aug. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 1 day:12 month:08 https://dx.doi.org/10.1186/1752-0509-7-S1-S2 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_70 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 7 2013 Suppl 1 12 08
spelling	10.1186/1752-0509-7-S1-S2 doi (DE-627)SPR028416414 (SPR)1752-0509-7-S1-S2-e DE-627 ger DE-627 rakwb eng Louhimo, Riku verfasserin aut Identification of genetic markers with synergistic survival effect in cancer 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. False Positive Rate (dpeaa)DE-He213 Survival Effect (dpeaa)DE-He213 Message Passing Interface (dpeaa)DE-He213 Single Nucleotide Polymorphism Marker (dpeaa)DE-He213 Marker Pair (dpeaa)DE-He213 Laakso, Marko aut Heikkinen, Tuomas aut Laitinen, Susanna aut Manninen, Pekka aut Rogojin, Vladimir aut Miettinen, Minna aut Blomqvist, Carl aut Liu, Jianjun aut Nevanlinna, Heli aut Hautaniemi, Sampsa aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 1 vom: 12. Aug. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 1 day:12 month:08 https://dx.doi.org/10.1186/1752-0509-7-S1-S2 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_70 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 7 2013 Suppl 1 12 08
allfields_unstemmed	10.1186/1752-0509-7-S1-S2 doi (DE-627)SPR028416414 (SPR)1752-0509-7-S1-S2-e DE-627 ger DE-627 rakwb eng Louhimo, Riku verfasserin aut Identification of genetic markers with synergistic survival effect in cancer 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. False Positive Rate (dpeaa)DE-He213 Survival Effect (dpeaa)DE-He213 Message Passing Interface (dpeaa)DE-He213 Single Nucleotide Polymorphism Marker (dpeaa)DE-He213 Marker Pair (dpeaa)DE-He213 Laakso, Marko aut Heikkinen, Tuomas aut Laitinen, Susanna aut Manninen, Pekka aut Rogojin, Vladimir aut Miettinen, Minna aut Blomqvist, Carl aut Liu, Jianjun aut Nevanlinna, Heli aut Hautaniemi, Sampsa aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 1 vom: 12. Aug. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 1 day:12 month:08 https://dx.doi.org/10.1186/1752-0509-7-S1-S2 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_70 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 7 2013 Suppl 1 12 08
allfieldsGer	10.1186/1752-0509-7-S1-S2 doi (DE-627)SPR028416414 (SPR)1752-0509-7-S1-S2-e DE-627 ger DE-627 rakwb eng Louhimo, Riku verfasserin aut Identification of genetic markers with synergistic survival effect in cancer 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. False Positive Rate (dpeaa)DE-He213 Survival Effect (dpeaa)DE-He213 Message Passing Interface (dpeaa)DE-He213 Single Nucleotide Polymorphism Marker (dpeaa)DE-He213 Marker Pair (dpeaa)DE-He213 Laakso, Marko aut Heikkinen, Tuomas aut Laitinen, Susanna aut Manninen, Pekka aut Rogojin, Vladimir aut Miettinen, Minna aut Blomqvist, Carl aut Liu, Jianjun aut Nevanlinna, Heli aut Hautaniemi, Sampsa aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 1 vom: 12. Aug. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 1 day:12 month:08 https://dx.doi.org/10.1186/1752-0509-7-S1-S2 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_70 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 7 2013 Suppl 1 12 08
allfieldsSound	10.1186/1752-0509-7-S1-S2 doi (DE-627)SPR028416414 (SPR)1752-0509-7-S1-S2-e DE-627 ger DE-627 rakwb eng Louhimo, Riku verfasserin aut Identification of genetic markers with synergistic survival effect in cancer 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. False Positive Rate (dpeaa)DE-He213 Survival Effect (dpeaa)DE-He213 Message Passing Interface (dpeaa)DE-He213 Single Nucleotide Polymorphism Marker (dpeaa)DE-He213 Marker Pair (dpeaa)DE-He213 Laakso, Marko aut Heikkinen, Tuomas aut Laitinen, Susanna aut Manninen, Pekka aut Rogojin, Vladimir aut Miettinen, Minna aut Blomqvist, Carl aut Liu, Jianjun aut Nevanlinna, Heli aut Hautaniemi, Sampsa aut Enthalten in BMC systems biology London : BioMed Central, 2007 7(2013), Suppl 1 vom: 12. Aug. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:7 year:2013 number:Suppl 1 day:12 month:08 https://dx.doi.org/10.1186/1752-0509-7-S1-S2 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_70 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 7 2013 Suppl 1 12 08
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. 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author	Louhimo, Riku
spellingShingle	Louhimo, Riku misc False Positive Rate misc Survival Effect misc Message Passing Interface misc Single Nucleotide Polymorphism Marker misc Marker Pair Identification of genetic markers with synergistic survival effect in cancer
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topic_title	Identification of genetic markers with synergistic survival effect in cancer False Positive Rate (dpeaa)DE-He213 Survival Effect (dpeaa)DE-He213 Message Passing Interface (dpeaa)DE-He213 Single Nucleotide Polymorphism Marker (dpeaa)DE-He213 Marker Pair (dpeaa)DE-He213
topic	misc False Positive Rate misc Survival Effect misc Message Passing Interface misc Single Nucleotide Polymorphism Marker misc Marker Pair
topic_unstemmed	misc False Positive Rate misc Survival Effect misc Message Passing Interface misc Single Nucleotide Polymorphism Marker misc Marker Pair
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title	Identification of genetic markers with synergistic survival effect in cancer
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title_full	Identification of genetic markers with synergistic survival effect in cancer
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author_browse	Louhimo, Riku Laakso, Marko Heikkinen, Tuomas Laitinen, Susanna Manninen, Pekka Rogojin, Vladimir Miettinen, Minna Blomqvist, Carl Liu, Jianjun Nevanlinna, Heli Hautaniemi, Sampsa
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title_sort	identification of genetic markers with synergistic survival effect in cancer
title_auth	Identification of genetic markers with synergistic survival effect in cancer
abstract	Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. Conclusions Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival. © Louhimo et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Identification of genetic markers with synergistic survival effect in cancer
url	https://dx.doi.org/10.1186/1752-0509-7-S1-S2
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author2	Laakso, Marko Heikkinen, Tuomas Laitinen, Susanna Manninen, Pekka Rogojin, Vladimir Miettinen, Minna Blomqvist, Carl Liu, Jianjun Nevanlinna, Heli Hautaniemi, Sampsa
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up_date	2024-07-03T19:21:28.595Z
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. Results The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. 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Identification of genetic markers with synergistic survival effect in cancer

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