Systems approach for congruence and selection of cancer models towards precision medicine.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often availa...
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Gespeichert in:

Autor*in:	Jian Zou [verfasserIn] Osama Shah [verfasserIn] Yu-Chiao Chiu [verfasserIn] Tianzhou Ma [verfasserIn] Jennifer M Atkinson [verfasserIn] Steffi Oesterreich [verfasserIn] Adrian V Lee [verfasserIn] George C Tseng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Übergeordnetes Werk:	In: PLoS Computational Biology - Public Library of Science (PLoS), 2005, 20(2024), 1, p e1011754
Übergeordnetes Werk:	volume:20 ; year:2024 ; number:1, p e1011754

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1371/journal.pcbi.1011754

Katalog-ID:	DOAJ092652026

Internformat


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allfields	10.1371/journal.pcbi.1011754 doi (DE-627)DOAJ092652026 (DE-599)DOAJd6056c710d1441368e8499b836e820b4 DE-627 ger DE-627 rakwb eng QH301-705.5 Jian Zou verfasserin aut Systems approach for congruence and selection of cancer models towards precision medicine. 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine. Biology (General) Osama Shah verfasserin aut Yu-Chiao Chiu verfasserin aut Tianzhou Ma verfasserin aut Jennifer M Atkinson verfasserin aut Steffi Oesterreich verfasserin aut Adrian V Lee verfasserin aut George C Tseng verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 20(2024), 1, p e1011754 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:20 year:2024 number:1, p e1011754 https://doi.org/10.1371/journal.pcbi.1011754 kostenfrei https://doaj.org/article/d6056c710d1441368e8499b836e820b4 kostenfrei https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011754&type=printable kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 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_2522 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2024 1, p e1011754
spelling	10.1371/journal.pcbi.1011754 doi (DE-627)DOAJ092652026 (DE-599)DOAJd6056c710d1441368e8499b836e820b4 DE-627 ger DE-627 rakwb eng QH301-705.5 Jian Zou verfasserin aut Systems approach for congruence and selection of cancer models towards precision medicine. 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine. Biology (General) Osama Shah verfasserin aut Yu-Chiao Chiu verfasserin aut Tianzhou Ma verfasserin aut Jennifer M Atkinson verfasserin aut Steffi Oesterreich verfasserin aut Adrian V Lee verfasserin aut George C Tseng verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 20(2024), 1, p e1011754 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:20 year:2024 number:1, p e1011754 https://doi.org/10.1371/journal.pcbi.1011754 kostenfrei https://doaj.org/article/d6056c710d1441368e8499b836e820b4 kostenfrei https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011754&type=printable kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 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_2522 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2024 1, p e1011754
allfields_unstemmed	10.1371/journal.pcbi.1011754 doi (DE-627)DOAJ092652026 (DE-599)DOAJd6056c710d1441368e8499b836e820b4 DE-627 ger DE-627 rakwb eng QH301-705.5 Jian Zou verfasserin aut Systems approach for congruence and selection of cancer models towards precision medicine. 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine. Biology (General) Osama Shah verfasserin aut Yu-Chiao Chiu verfasserin aut Tianzhou Ma verfasserin aut Jennifer M Atkinson verfasserin aut Steffi Oesterreich verfasserin aut Adrian V Lee verfasserin aut George C Tseng verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 20(2024), 1, p e1011754 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:20 year:2024 number:1, p e1011754 https://doi.org/10.1371/journal.pcbi.1011754 kostenfrei https://doaj.org/article/d6056c710d1441368e8499b836e820b4 kostenfrei https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011754&type=printable kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 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_2522 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2024 1, p e1011754
allfieldsGer	10.1371/journal.pcbi.1011754 doi (DE-627)DOAJ092652026 (DE-599)DOAJd6056c710d1441368e8499b836e820b4 DE-627 ger DE-627 rakwb eng QH301-705.5 Jian Zou verfasserin aut Systems approach for congruence and selection of cancer models towards precision medicine. 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine. Biology (General) Osama Shah verfasserin aut Yu-Chiao Chiu verfasserin aut Tianzhou Ma verfasserin aut Jennifer M Atkinson verfasserin aut Steffi Oesterreich verfasserin aut Adrian V Lee verfasserin aut George C Tseng verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 20(2024), 1, p e1011754 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:20 year:2024 number:1, p e1011754 https://doi.org/10.1371/journal.pcbi.1011754 kostenfrei https://doaj.org/article/d6056c710d1441368e8499b836e820b4 kostenfrei https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011754&type=printable kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 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_2522 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2024 1, p e1011754
allfieldsSound	10.1371/journal.pcbi.1011754 doi (DE-627)DOAJ092652026 (DE-599)DOAJd6056c710d1441368e8499b836e820b4 DE-627 ger DE-627 rakwb eng QH301-705.5 Jian Zou verfasserin aut Systems approach for congruence and selection of cancer models towards precision medicine. 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine. Biology (General) Osama Shah verfasserin aut Yu-Chiao Chiu verfasserin aut Tianzhou Ma verfasserin aut Jennifer M Atkinson verfasserin aut Steffi Oesterreich verfasserin aut Adrian V Lee verfasserin aut George C Tseng verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 20(2024), 1, p e1011754 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:20 year:2024 number:1, p e1011754 https://doi.org/10.1371/journal.pcbi.1011754 kostenfrei https://doaj.org/article/d6056c710d1441368e8499b836e820b4 kostenfrei https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011754&type=printable kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 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_2522 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2024 1, p e1011754
language	English
source	In PLoS Computational Biology 20(2024), 1, p e1011754 volume:20 year:2024 number:1, p e1011754
sourceStr	In PLoS Computational Biology 20(2024), 1, p e1011754 volume:20 year:2024 number:1, p e1011754
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Biology (General)
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container_title	PLoS Computational Biology
authorswithroles_txt_mv	Jian Zou @@aut@@ Osama Shah @@aut@@ Yu-Chiao Chiu @@aut@@ Tianzhou Ma @@aut@@ Jennifer M Atkinson @@aut@@ Steffi Oesterreich @@aut@@ Adrian V Lee @@aut@@ George C Tseng @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Jian Zou
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topic_title	QH301-705.5 Systems approach for congruence and selection of cancer models towards precision medicine
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title	Systems approach for congruence and selection of cancer models towards precision medicine.
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title_sort	systems approach for congruence and selection of cancer models towards precision medicine
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title_auth	Systems approach for congruence and selection of cancer models towards precision medicine.
abstract	Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.
abstractGer	Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.
abstract_unstemmed	Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.
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title_short	Systems approach for congruence and selection of cancer models towards precision medicine.
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Systems approach for congruence and selection of cancer models towards precision medicine.

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