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Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival. | LitMetric

AI Article Synopsis

  • Glioblastoma is a highly aggressive brain cancer with varied characteristics, making it difficult to develop effective personalized treatments; addressing intratumoral heterogeneity is key to better tumor classification.
  • Researchers used transcriptional profiles from glioblastoma samples to enhance patient stratification by focusing on the cellular tumor structure for more accurate analysis.
  • The study demonstrates that analyzing pure cellular tumor tissue can improve biomarker effectiveness, aiding in diagnostics, prognostics, and treatment strategies for glioblastoma patients.

Article Abstract

Background: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification.

Methods: We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model.

Results: We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature.

Conclusions: Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462280PMC
http://dx.doi.org/10.1093/noajnl/vdaa093DOI Listing

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