Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition
- Autores
- González, Nazareno; Perez Kuper, Melanie; Garcia Fallit, Matías; Nicola Candia, Alejandro Javier; Peña Agudelo, Jorge Armando; Suarez Velandia, Maicol Mauricio; Romero, Ana Clara; Videla Richardson, Guillermo Agustin; Candolfi, Marianela
- Año de publicación
- 2025
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Glioblastoma (GBM) presents significant therapeutic challenges due to its invasivenature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This studyaimed to identify gene signatures that predict poor TMZ response and high PD−L1/PD−1tumor expression, and explore potential sensitivity to alternative drugs. We analyzedThe Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes(DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantlycorrelated with overall survival. A risk score model was built using these 5 DEGs, classifyingpatients into low-, medium-, and high-risk groups. We assessed immune cellinfiltration, immunosuppressive mediators, and epithelial–mesenchymal transition (EMT)markers in each group using correlation analysis, Gene Set Enrichment Analysis (GSEA),and machine learning. The model demonstrated strong predictive power, with high-riskpatients exhibiting poorer survival and increased immune infiltration. GSEA revealedupregulation of immune and EMT-related pathways in high-risk patients. Our analyses suggest that high-risk patients may exhibit limited response to PD−1 inhibitors, but couldshow sensitivity to etoposide and paclitaxel. This risk score model provides a valuabletool for guiding therapeutic decisions and identifying alternative chemotherapy options toenable the development of personalized and cost-effective treatments for GBM patients.
Fil: González, Nazareno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina
Fil: Perez Kuper, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina
Fil: Garcia Fallit, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina
Fil: Nicola Candia, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina
Fil: Peña Agudelo, Jorge Armando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina
Fil: Suarez Velandia, Maicol Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina
Fil: Romero, Ana Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina
Fil: Videla Richardson, Guillermo Agustin. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Candolfi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina - Materia
-
GLIOBLASTOMA
IMMUNE MICROENVIRONMENT
DIFFERENTIALLY EXPRESSED GENES
RISK SCORE MODEL
TEMOZOLOMIDE RESISTANCE - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/278348
Ver los metadatos del registro completo
| id |
CONICETDig_9787ac79a57a69be5636b9095faa52b4 |
|---|---|
| oai_identifier_str |
oai:ri.conicet.gov.ar:11336/278348 |
| network_acronym_str |
CONICETDig |
| repository_id_str |
3498 |
| network_name_str |
CONICET Digital (CONICET) |
| spelling |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint InhibitionGonzález, NazarenoPerez Kuper, MelanieGarcia Fallit, MatíasNicola Candia, Alejandro JavierPeña Agudelo, Jorge ArmandoSuarez Velandia, Maicol MauricioRomero, Ana ClaraVidela Richardson, Guillermo AgustinCandolfi, MarianelaGLIOBLASTOMAIMMUNE MICROENVIRONMENTDIFFERENTIALLY EXPRESSED GENESRISK SCORE MODELTEMOZOLOMIDE RESISTANCEhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Glioblastoma (GBM) presents significant therapeutic challenges due to its invasivenature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This studyaimed to identify gene signatures that predict poor TMZ response and high PD−L1/PD−1tumor expression, and explore potential sensitivity to alternative drugs. We analyzedThe Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes(DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantlycorrelated with overall survival. A risk score model was built using these 5 DEGs, classifyingpatients into low-, medium-, and high-risk groups. We assessed immune cellinfiltration, immunosuppressive mediators, and epithelial–mesenchymal transition (EMT)markers in each group using correlation analysis, Gene Set Enrichment Analysis (GSEA),and machine learning. The model demonstrated strong predictive power, with high-riskpatients exhibiting poorer survival and increased immune infiltration. GSEA revealedupregulation of immune and EMT-related pathways in high-risk patients. Our analyses suggest that high-risk patients may exhibit limited response to PD−1 inhibitors, but couldshow sensitivity to etoposide and paclitaxel. This risk score model provides a valuabletool for guiding therapeutic decisions and identifying alternative chemotherapy options toenable the development of personalized and cost-effective treatments for GBM patients.Fil: González, Nazareno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Perez Kuper, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Garcia Fallit, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Nicola Candia, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Peña Agudelo, Jorge Armando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Suarez Velandia, Maicol Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Romero, Ana Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Videla Richardson, Guillermo Agustin. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Candolfi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaMDPI2025-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/278348González, Nazareno; Perez Kuper, Melanie; Garcia Fallit, Matías; Nicola Candia, Alejandro Javier; Peña Agudelo, Jorge Armando; et al.; Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition; MDPI; Biology; 14; 5; 5-2025; 1-242079-7737CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2079-7737/14/5/572info:eu-repo/semantics/altIdentifier/doi/10.3390/biology14050572info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-01-08T12:57:56Zoai:ri.conicet.gov.ar:11336/278348instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-01-08 12:57:56.921CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition |
| title |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition |
| spellingShingle |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition González, Nazareno GLIOBLASTOMA IMMUNE MICROENVIRONMENT DIFFERENTIALLY EXPRESSED GENES RISK SCORE MODEL TEMOZOLOMIDE RESISTANCE |
| title_short |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition |
| title_full |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition |
| title_fullStr |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition |
| title_full_unstemmed |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition |
| title_sort |
Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition |
| dc.creator.none.fl_str_mv |
González, Nazareno Perez Kuper, Melanie Garcia Fallit, Matías Nicola Candia, Alejandro Javier Peña Agudelo, Jorge Armando Suarez Velandia, Maicol Mauricio Romero, Ana Clara Videla Richardson, Guillermo Agustin Candolfi, Marianela |
| author |
González, Nazareno |
| author_facet |
González, Nazareno Perez Kuper, Melanie Garcia Fallit, Matías Nicola Candia, Alejandro Javier Peña Agudelo, Jorge Armando Suarez Velandia, Maicol Mauricio Romero, Ana Clara Videla Richardson, Guillermo Agustin Candolfi, Marianela |
| author_role |
author |
| author2 |
Perez Kuper, Melanie Garcia Fallit, Matías Nicola Candia, Alejandro Javier Peña Agudelo, Jorge Armando Suarez Velandia, Maicol Mauricio Romero, Ana Clara Videla Richardson, Guillermo Agustin Candolfi, Marianela |
| author2_role |
author author author author author author author author |
| dc.subject.none.fl_str_mv |
GLIOBLASTOMA IMMUNE MICROENVIRONMENT DIFFERENTIALLY EXPRESSED GENES RISK SCORE MODEL TEMOZOLOMIDE RESISTANCE |
| topic |
GLIOBLASTOMA IMMUNE MICROENVIRONMENT DIFFERENTIALLY EXPRESSED GENES RISK SCORE MODEL TEMOZOLOMIDE RESISTANCE |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3 |
| dc.description.none.fl_txt_mv |
Glioblastoma (GBM) presents significant therapeutic challenges due to its invasivenature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This studyaimed to identify gene signatures that predict poor TMZ response and high PD−L1/PD−1tumor expression, and explore potential sensitivity to alternative drugs. We analyzedThe Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes(DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantlycorrelated with overall survival. A risk score model was built using these 5 DEGs, classifyingpatients into low-, medium-, and high-risk groups. We assessed immune cellinfiltration, immunosuppressive mediators, and epithelial–mesenchymal transition (EMT)markers in each group using correlation analysis, Gene Set Enrichment Analysis (GSEA),and machine learning. The model demonstrated strong predictive power, with high-riskpatients exhibiting poorer survival and increased immune infiltration. GSEA revealedupregulation of immune and EMT-related pathways in high-risk patients. Our analyses suggest that high-risk patients may exhibit limited response to PD−1 inhibitors, but couldshow sensitivity to etoposide and paclitaxel. This risk score model provides a valuabletool for guiding therapeutic decisions and identifying alternative chemotherapy options toenable the development of personalized and cost-effective treatments for GBM patients. Fil: González, Nazareno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina Fil: Perez Kuper, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina Fil: Garcia Fallit, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina Fil: Nicola Candia, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina Fil: Peña Agudelo, Jorge Armando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina Fil: Suarez Velandia, Maicol Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina Fil: Romero, Ana Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina Fil: Videla Richardson, Guillermo Agustin. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina Fil: Candolfi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina |
| description |
Glioblastoma (GBM) presents significant therapeutic challenges due to its invasivenature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This studyaimed to identify gene signatures that predict poor TMZ response and high PD−L1/PD−1tumor expression, and explore potential sensitivity to alternative drugs. We analyzedThe Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes(DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantlycorrelated with overall survival. A risk score model was built using these 5 DEGs, classifyingpatients into low-, medium-, and high-risk groups. We assessed immune cellinfiltration, immunosuppressive mediators, and epithelial–mesenchymal transition (EMT)markers in each group using correlation analysis, Gene Set Enrichment Analysis (GSEA),and machine learning. The model demonstrated strong predictive power, with high-riskpatients exhibiting poorer survival and increased immune infiltration. GSEA revealedupregulation of immune and EMT-related pathways in high-risk patients. Our analyses suggest that high-risk patients may exhibit limited response to PD−1 inhibitors, but couldshow sensitivity to etoposide and paclitaxel. This risk score model provides a valuabletool for guiding therapeutic decisions and identifying alternative chemotherapy options toenable the development of personalized and cost-effective treatments for GBM patients. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025-05 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/278348 González, Nazareno; Perez Kuper, Melanie; Garcia Fallit, Matías; Nicola Candia, Alejandro Javier; Peña Agudelo, Jorge Armando; et al.; Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition; MDPI; Biology; 14; 5; 5-2025; 1-24 2079-7737 CONICET Digital CONICET |
| url |
http://hdl.handle.net/11336/278348 |
| identifier_str_mv |
González, Nazareno; Perez Kuper, Melanie; Garcia Fallit, Matías; Nicola Candia, Alejandro Javier; Peña Agudelo, Jorge Armando; et al.; Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition; MDPI; Biology; 14; 5; 5-2025; 1-24 2079-7737 CONICET Digital CONICET |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2079-7737/14/5/572 info:eu-repo/semantics/altIdentifier/doi/10.3390/biology14050572 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
| dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf |
| dc.publisher.none.fl_str_mv |
MDPI |
| publisher.none.fl_str_mv |
MDPI |
| dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
| reponame_str |
CONICET Digital (CONICET) |
| collection |
CONICET Digital (CONICET) |
| instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
| repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
| repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
| _version_ |
1853775473677434880 |
| score |
13.25844 |