Project description:Glioblastoma (GB) is the most aggressive form of glioma and is characterized by a poor prognosis and high recurrence, despite intensive clinical interventions. To retrieve the key factors underlying the high malignancy of GB, we performed differential methylation analysis between low and high-grade gliomas by using Infinium MethylationEPIC beadchips.
Project description:Glioblastoma (GB) is the most aggressive form of glioma and is characterized by a poor prognosis and high recurrence, despite intensive clinical interventions. To retrieve the key factors underlying the high malignancy of GB, we performed differential expression analysis between low and high-grade gliomas by using RNA-seq.
Project description:We carried out the analyses of chromosome variations between low-grade and high-grade gliomas in Chinese population. We found out the differences in chromosomes, cytobands, genes, pathways and GO functions. To identify the glioma tissue-specific genomic alterations and compare the genomic variations between low-grade and high-grade gliomas.
Project description:We carried out the analyses of chromosome variations between low-grade and high-grade gliomas in Chinese population. We found out the differences in chromosomes, cytobands, genes, pathways and GO functions.
Project description:Low grade gliomas (LGG; WHO grade 2 astrocytomas, oligodendrogliomas and oligoastrocytomas) account for about 25% of diffuse gliomas. Most occur in young adults between the ages of 30 and 45 years, and are usually only diagnosed after a seizure. In general, they can be characterised by a long period of continuous slow growth, followed by malignant transformation that will be the cause of death up to 25 years after onset. However, there is a significant number of patients for whom malignant progression is more rapid, with mortality observed within 5 years. This suggests that, as with other tumour types, there may be different subtypes of LGG with specific prognosis. It follows that being able to identify these subtypes may permit better patient stratification and aid targeted treatments. Until recently, our understanding of the variables involved in patient prognosis included the type of tumour oligodendroglial tumours indicate better prognosis than oligoastrocytic or astrocytic and presence of the 1p-19q co-deletion. In addition, the recent discovery of mutations in IDH1&2 in the majority of LGGs provided another means of stratifying patients, while offering an important insight into their biology. However, we still understand very little of the biology behind the genesis and progression of the 70-80% of LGG that bear IDH1&2 mutations, let alone the remaining IDH wild-type tumours.
Project description:Ribba2012 - Low-grade gliomas, tumour growth inhibition model
Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy.
This model is described in the article:
A tumour growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy
Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F.
Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080
Abstract:
PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy.
EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy.
RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data.
CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.
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