Background: Inevitable recurrence after
radiochemotherapy is the major problem in the treatment of
glioblastoma, the most prevalent type of adult brain
malignancy.
Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding
glioblastoma recurrence is that cytotoxic
therapy fails to target effectively
glioma stem cells. Recent advances indicate that
therapy-driven molecular evolution is a fundamental trait associated with
glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular
biomarkers and specific impacts of
glioma stem cells need to be taken into consideration in order to increase the accuracy of molecular diagnostics still relying on readouts obtained from a single
tumor specimen. Methods: This study integrates a multisampling strategy, longitudinal approach and complementary transcriptomic investigations in order to identify transcriptomic traits of recurrent
glioblastoma in whole-tissue specimens of
glioblastoma or
glioblastoma stem cells. In this study, 128 tissue samples of 44
tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent
glioblastomas were analyzed along with 27 primary cultures of
glioblastoma stem cells by
RNA sequencing. A novel algorithm was used to quantify longitudinal changes in pathway activities and model efficacy of anti-
cancer drugs based on gene expression data. Results: Our study reveals that intratumor heterogeneity of gene expression patterns is a fundamental characteristic of not only newly diagnosed but also recurrent
glioblastomas. Evidence is provided that
glioblastoma stem cells recapitulate intratumor heterogeneity, longitudinal transcriptomic changes and drug sensitivity patterns associated with the state of recurrence. Conclusions: Our results provide a transcriptional rationale for the lack of significant therapeutic benefit from
temozolomide in patients with recurrent
glioblastoma. Our findings imply that the spectrum of potentially effective drugs is likely to differ between newly diagnosed and recurrent
glioblastomas and underscore the merits of
glioblastoma stem cells as prognostic models for identifying alternative drugs and predicting drug response in recurrent
glioblastoma. With the majority of recurrent
glioblastomas being inoperable,
glioblastoma stem cell models provide the means of compensating for the limited availability of recurrent
glioblastoma specimens.