Abstract
BackgroundPrognostic markers for glioblastoma are lacking. Both intrinsic tumour characteristics and microenvironment could influence cancer prognostic. The aim of our study was to generate a pure glioblastoma cell lines and immune classification in order to decipher the respective role of glioblastoma cell and microenvironment on prognosis.MethodsWe worked on two large cohorts of patients suffering from glioblastoma (TCGA, n = 481 and Rembrandt, n = 180) for which clinical data, transcriptomic profiles and outcome were recorded. Transcriptomic profiles of 129 pure glioblastoma cell lines were clustered to generate a glioblastoma cell lines classification. Presence of subtypes of glioblastoma cell lines and immune cells was determined using deconvolution.ResultsGlioblastoma cell lines classification defined three new molecular groups called oncogenic, metabolic and neuronal communication enriched. Neuronal communication-enriched tumours were associated with poor prognosis in both cohorts. Immune cell infiltrate was more frequent in mesenchymal classical classification subgroup and metabolic-enriched tumours. A combination of age, glioblastoma cell lines classification and immune classification could be used to determine patient’s outcome in both cohorts.ConclusionsOur study shows that glioblastoma-bearing patients can be classified based on their age, glioblastoma cell lines classification and immune classification. The combination of these information improves the capacity to address prognosis.
Highlights
Using KEGG pathway analysis, genes associated with each of these three groups could be related to oncogenic pathways, metabolic pathways, and neuronal communication pathways (Figure S1)
Interactions between the new classification and the immune cells proportions were added to the model because we showed earlier that the prognostic role of these cells differs in the different cell lines groups
In Rembrandt cohort classical molecular classification is significantly associated with OS. In this cohort proneural tumours were not significantly associated with better OS (HR = 0.58, 95% CI (0.30–1.133), p = 0.11, in comparison with mesenchymal tumours), suggesting the difficulty to use the classical molecular classification to address patients’ prognosis
Summary
CONCLUSIONS: Our study shows that glioblastoma-bearing patients can be classified based on their age, glioblastoma cell lines classification and immune classification. The combination of these information improves the capacity to address prognosis. The current treatment relies on surgical resection of gross tumour followed by radio-chemotherapy and adjuvant therapy with temozolomide After such therapy, most patients experiment recurrence and only a few therapeutic options are available. Median survival only reaches around 15 months.[1] To better understand this pathology and define subgroups of patients with particular molecular biology and particular prognosis or response to therapy, the Cancer Genome Atlas Consortium (TCGA) performed high-dimensional profiling and molecular classification of large series of GBM tumours. Proneuronal tumours seem to be associated with a better outcome, whereas mesenchymal tumours are related to a poorer survival.[3,4,5]
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