Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma
<p><i>Nf2</i> loss results in Ras<sup>G12D</sup>-induced oncogenesis and cooperates with <i>Trp53</i> loss to accelerate ICC formation. <b>A,</b> Kaplan–Meier curve demonstrating the relative survival proportions of mice with KRAS<sup>G12D&...
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2025
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| _version_ | 1849927640199200768 |
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| author | Nicholas T. Younger (14956251) |
| author2 | Mollie L. Wilson (14956254) Anabel Martinez Lyons (14956257) Edward J. Jarman (9773166) Alison M. Meynert (14956260) Graeme R. Grimes (14160170) Konstantinos Gournopanos (14956263) Scott H. Waddell (14956266) Peter A. Tennant (14956269) David H. Wilson (14956272) Rachel V. Guest (14956275) Stephen J. Wigmore (14915943) Juan Carlos Acosta (14956278) Timothy J. Kendall (14956281) Martin S. Taylor (14956284) Duncan Sproul (13971883) Pleasantine Mill (256953) Luke Boulter (14956287) |
| author2_role | author author author author author author author author author author author author author author author author author |
| author_facet | Nicholas T. Younger (14956251) Mollie L. Wilson (14956254) Anabel Martinez Lyons (14956257) Edward J. Jarman (9773166) Alison M. Meynert (14956260) Graeme R. Grimes (14160170) Konstantinos Gournopanos (14956263) Scott H. Waddell (14956266) Peter A. Tennant (14956269) David H. Wilson (14956272) Rachel V. Guest (14956275) Stephen J. Wigmore (14915943) Juan Carlos Acosta (14956278) Timothy J. Kendall (14956281) Martin S. Taylor (14956284) Duncan Sproul (13971883) Pleasantine Mill (256953) Luke Boulter (14956287) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nicholas T. Younger (14956251) Mollie L. Wilson (14956254) Anabel Martinez Lyons (14956257) Edward J. Jarman (9773166) Alison M. Meynert (14956260) Graeme R. Grimes (14160170) Konstantinos Gournopanos (14956263) Scott H. Waddell (14956266) Peter A. Tennant (14956269) David H. Wilson (14956272) Rachel V. Guest (14956275) Stephen J. Wigmore (14915943) Juan Carlos Acosta (14956278) Timothy J. Kendall (14956281) Martin S. Taylor (14956284) Duncan Sproul (13971883) Pleasantine Mill (256953) Luke Boulter (14956287) |
| dc.date.none.fl_str_mv | 2025-11-24T22:22:13Z |
| dc.identifier.none.fl_str_mv | 10.1158/0008-5472.30698868 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Figure_3_from_i_In_Vivo_i_Modeling_of_Patient_Genetic_Heterogeneity_Identifies_New_Ways_to_Target_Cholangiocarcinoma/30698868 |
| dc.rights.none.fl_str_mv | CC BY info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cancer Cancer Biology Molecular and Cellular Biology Therapeutic Research and Development Methods and Technology Cell Signaling Computational Methods Sequence analysis Drug Targets Gastrointestinal Cancers Liver cancer Gene Technologies Comparative genomics Oncogenes & Tumor Suppressors Kras Preclinical Models Animal models of cancer |
| dc.title.none.fl_str_mv | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><i>Nf2</i> loss results in Ras<sup>G12D</sup>-induced oncogenesis and cooperates with <i>Trp53</i> loss to accelerate ICC formation. <b>A,</b> Kaplan–Meier curve demonstrating the relative survival proportions of mice with KRAS<sup>G12D</sup> and gRNAs targeting <i>Trp53</i> (<i>N</i> = 12), <i>Nf2</i> (<i>N</i> = 5), <i>Nf2</i>;<i>Trp53</i> (<i>N</i> = 13), or nontargeting control (scrm, <i>N</i> = 5). <b>B</b> and <b>C,</b> Proportion of liver occupied by tumor (<b>B</b>) and number of tumors per mouse (<b>C</b>). <b>D,</b> Hematoxylin and eosin (H&E) staining of KRAS<sup>G12D</sup> tumors with <i>Trp53</i>, <i>Nf2</i>, or <i>Trp53</i>;<i>Nf2</i> loss. Scale bar, 100 μm. Dotted line, tumor-stroma boundary. <b>E,</b> Comparison of RNA-seq analysis when the transcriptomes from <i>Nf2</i>;<i>Trp53</i> versus <i>Trp53</i> alone tumors (blue) are compared with transcripts from <i>Nf2</i>;<i>Trp53</i> versus <i>Nf2</i> alone (yellow) tumors. Each group contains <i>N</i> = 4 regionally distinct tumors. <b>F,</b> Analysis of RPPA data demonstrating the changes in the proportion of phosphorylated GSK3α/β, β-catenin, and pAKT relative to total protein levels in KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup> (gray points), KRAS<sup>G12D</sup>;<i>Nf2</i><sup>KO</sup> (yellow points), KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>;<i>Nf2</i><sup>KO</sup> (blue points). <b>G,</b> IHC of active, dephosphorylated β-catenin (top) and phosphorylated AKT<sup>Ser647</sup> (bottom) in KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>, KRAS<sup>G12D</sup>;<i>Nf2</i><sup>KO</sup>, KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>/<i>Nf2</i><sup>KO</sup> tumors. Scale bar, 50 μm. <b>H.</b> Immunoblot for dephosphorylated (active) β-catenin (β-catenin<sup>Ser33/37/Thr41</sup>) in tumors isolated from mice baring Kras<sup>G12D</sup> -driven ICC with <i>Trp53</i>, <i>Nf2</i>, or <i>Trp53</i>;<i>Nf2</i> co-loss. GAPDH was used as a loading control. <b>I,</b> Schematic representing our dosing approach to determine whether Wnt inhibition, PI3K inhibition, or a combination of the two is effective in improving the survival of mice with KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>;<i>Nf2</i><sup>KO</sup> ICC. <b>J,</b> Kaplan–Meier curve demonstrating the survival changes when KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>;<i>Nf2</i><sup>KO</sup> animals are treated with vehicle (yellow line), LGK974 (Wnt-inhibitor; blue line), pictilisib (PI3K inhibitor; orange line), or a combination (green line; <i>N</i> = 5 per group).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_c63815199bb68866bb56b7d86e2a7fd7 |
| identifier_str_mv | 10.1158/0008-5472.30698868 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30698868 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY |
| spelling | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target CholangiocarcinomaNicholas T. Younger (14956251)Mollie L. Wilson (14956254)Anabel Martinez Lyons (14956257)Edward J. Jarman (9773166)Alison M. Meynert (14956260)Graeme R. Grimes (14160170)Konstantinos Gournopanos (14956263)Scott H. Waddell (14956266)Peter A. Tennant (14956269)David H. Wilson (14956272)Rachel V. Guest (14956275)Stephen J. Wigmore (14915943)Juan Carlos Acosta (14956278)Timothy J. Kendall (14956281)Martin S. Taylor (14956284)Duncan Sproul (13971883)Pleasantine Mill (256953)Luke Boulter (14956287)CancerCancer BiologyMolecular and Cellular BiologyTherapeutic Research and DevelopmentMethods and TechnologyCell SignalingComputational MethodsSequence analysisDrug TargetsGastrointestinal CancersLiver cancerGene TechnologiesComparative genomicsOncogenes & Tumor SuppressorsKrasPreclinical ModelsAnimal models of cancer<p><i>Nf2</i> loss results in Ras<sup>G12D</sup>-induced oncogenesis and cooperates with <i>Trp53</i> loss to accelerate ICC formation. <b>A,</b> Kaplan–Meier curve demonstrating the relative survival proportions of mice with KRAS<sup>G12D</sup> and gRNAs targeting <i>Trp53</i> (<i>N</i> = 12), <i>Nf2</i> (<i>N</i> = 5), <i>Nf2</i>;<i>Trp53</i> (<i>N</i> = 13), or nontargeting control (scrm, <i>N</i> = 5). <b>B</b> and <b>C,</b> Proportion of liver occupied by tumor (<b>B</b>) and number of tumors per mouse (<b>C</b>). <b>D,</b> Hematoxylin and eosin (H&E) staining of KRAS<sup>G12D</sup> tumors with <i>Trp53</i>, <i>Nf2</i>, or <i>Trp53</i>;<i>Nf2</i> loss. Scale bar, 100 μm. Dotted line, tumor-stroma boundary. <b>E,</b> Comparison of RNA-seq analysis when the transcriptomes from <i>Nf2</i>;<i>Trp53</i> versus <i>Trp53</i> alone tumors (blue) are compared with transcripts from <i>Nf2</i>;<i>Trp53</i> versus <i>Nf2</i> alone (yellow) tumors. Each group contains <i>N</i> = 4 regionally distinct tumors. <b>F,</b> Analysis of RPPA data demonstrating the changes in the proportion of phosphorylated GSK3α/β, β-catenin, and pAKT relative to total protein levels in KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup> (gray points), KRAS<sup>G12D</sup>;<i>Nf2</i><sup>KO</sup> (yellow points), KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>;<i>Nf2</i><sup>KO</sup> (blue points). <b>G,</b> IHC of active, dephosphorylated β-catenin (top) and phosphorylated AKT<sup>Ser647</sup> (bottom) in KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>, KRAS<sup>G12D</sup>;<i>Nf2</i><sup>KO</sup>, KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>/<i>Nf2</i><sup>KO</sup> tumors. Scale bar, 50 μm. <b>H.</b> Immunoblot for dephosphorylated (active) β-catenin (β-catenin<sup>Ser33/37/Thr41</sup>) in tumors isolated from mice baring Kras<sup>G12D</sup> -driven ICC with <i>Trp53</i>, <i>Nf2</i>, or <i>Trp53</i>;<i>Nf2</i> co-loss. GAPDH was used as a loading control. <b>I,</b> Schematic representing our dosing approach to determine whether Wnt inhibition, PI3K inhibition, or a combination of the two is effective in improving the survival of mice with KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>;<i>Nf2</i><sup>KO</sup> ICC. <b>J,</b> Kaplan–Meier curve demonstrating the survival changes when KRAS<sup>G12D</sup>;<i>Trp53</i><sup>KO</sup>;<i>Nf2</i><sup>KO</sup> animals are treated with vehicle (yellow line), LGK974 (Wnt-inhibitor; blue line), pictilisib (PI3K inhibitor; orange line), or a combination (green line; <i>N</i> = 5 per group).</p>2025-11-24T22:22:13ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1158/0008-5472.30698868https://figshare.com/articles/figure/Figure_3_from_i_In_Vivo_i_Modeling_of_Patient_Genetic_Heterogeneity_Identifies_New_Ways_to_Target_Cholangiocarcinoma/30698868CC BYinfo:eu-repo/semantics/openAccessoai:figshare.com:article/306988682025-11-24T22:22:13Z |
| spellingShingle | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma Nicholas T. Younger (14956251) Cancer Cancer Biology Molecular and Cellular Biology Therapeutic Research and Development Methods and Technology Cell Signaling Computational Methods Sequence analysis Drug Targets Gastrointestinal Cancers Liver cancer Gene Technologies Comparative genomics Oncogenes & Tumor Suppressors Kras Preclinical Models Animal models of cancer |
| status_str | publishedVersion |
| title | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma |
| title_full | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma |
| title_fullStr | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma |
| title_full_unstemmed | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma |
| title_short | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma |
| title_sort | Figure 3 from <i>In Vivo</i> Modeling of Patient Genetic Heterogeneity Identifies New Ways to Target Cholangiocarcinoma |
| topic | Cancer Cancer Biology Molecular and Cellular Biology Therapeutic Research and Development Methods and Technology Cell Signaling Computational Methods Sequence analysis Drug Targets Gastrointestinal Cancers Liver cancer Gene Technologies Comparative genomics Oncogenes & Tumor Suppressors Kras Preclinical Models Animal models of cancer |