Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition.

in Biology by Nazareno Gonzalez, Melanie Perez Küper, Matias Garcia Fallit, Alejandro J Nicola Candia, Jorge A Peña Agudelo, Maicol Suarez Velandia, Ana Clara Romero, Guillermo Agustin Videla-Richardson, Marianela Candolfi

TLDR

  • Researchers developed a risk score model to predict glioblastoma patient outcomes based on gene expression and identify potential alternative chemotherapy options.
  • The model shows strong predictive power and may enable personalized and cost-effective treatments.
  • Future studies will focus on validating the model and exploring its application in clinical practice.

Abstract

Glioblastoma (GBM) presents significant therapeutic challenges due to its invasive nature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This study aimed to identify gene signatures that predict poor TMZ response and high PD-L1/PD-1 tumor expression, and explore potential sensitivity to alternative drugs. We analyzed The Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes (DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantly correlated with overall survival. A risk score model was built using these 5 DEGs, classifying patients into low-, medium-, and high-risk groups. We assessed immune cell infiltration, 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-risk patients exhibiting poorer survival and increased immune infiltration. GSEA revealed upregulation 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 could show sensitivity to etoposide and paclitaxel. This risk score model provides a valuable tool for guiding therapeutic decisions and identifying alternative chemotherapy options to enable the development of personalized and cost-effective treatments for GBM patients.

Overview

  • The study aimed to identify gene signatures that predict poor response to temozolomide (TMZ) and high progesterone receptor (PD-L1/PD-1) tumor expression.
  • The researchers analyzed The Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes (DEGs) linked to these characteristics.
  • The study built a risk score model using five DEGs that correlated with overall survival, classifying patients into low-, medium-, and high-risk groups.

Comparative Analysis & Findings

  • The risk score model demonstrated strong predictive power, with high-risk patients exhibiting poorer survival and increased immune infiltration.
  • Gene Set Enrichment Analysis (GSEA) revealed upregulation of immune and epithelial-mesenchymal transition (EMT)-related pathways in high-risk patients.
  • The study suggests that high-risk patients may exhibit limited response to PD-1 inhibitors, but could show sensitivity to etoposide and paclitaxel.

Implications and Future Directions

  • The risk score model provides a valuable tool for guiding therapeutic decisions and identifying alternative chemotherapy options.
  • Future studies could investigate the mechanisms underlying the proposed therapeutic strategies and identify additional biomarkers for personalized treatment.
  • The model's predictive power and ability to identify high-risk patients may enable the development of cost-effective and targeted treatments for glioblastoma patients.
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