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Gliomatosis Cerebri I

ABSTRACT: In modern clinical neuro-oncology the pathological diagnoses are very challenging creating significant clinical confusion and affecting therapeutic decisions and prognostic estimation. We investigated whether gene expression profiling, coupled with class prediction methodology, could be used to predict prognosis of gliomatosis cerebri in a more consistent manner then standard pathology. We report the results of a molecular study in fifty-nine cases of gliomatosis cerebri, correlating these results with prognosis. TP53 and PTEN gene sequences were analyzed and microarray expression profiling were also performed. At the 24 month follow-up, 17 patients were alive, while 42 patients were died. We identified a 23 gene signature able to predict patient prognosis with microarray gene expression profiling. With the aim to produce a prognostication tool useful in clinical investigation we studied the expression of this 23 gene signature by RT-qPCR. Real time expression values relative to these 23 gene features were used to built a prediction method able to distinguish patients with good prognosis (more likely responsive to therapy) from patients with a bad prognosis (more likely irresponsive to therapy). These results demonstrated not only strong association of gene expression patterns with the survival of the patients but also a robust reproducibility of these gene expression– based predictors.

ORGANISM(S): Homo sapiens

PROVIDER: GSE11822 | GEO | 2009/06/17

SECONDARY ACCESSION(S): PRJNA105919

REPOSITORIES: GEO

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Project description:In modern clinical neuro-oncology the pathological diagnoses are very challenging creating significant clinical confusion and affecting therapeutic decisions and prognostic estimation. We investigated whether gene expression profiling, coupled with class prediction methodology, could be used to predict prognosis of gliomatosis cerebri in a more consistent manner then standard pathology. We report the results of a molecular study in fifty-nine cases of gliomatosis cerebri, correlating these results with prognosis. TP53 and PTEN gene sequences were analyzed and microarray expression profiling were also performed. At the 24 month follow-up, 17 patients were alive, while 42 patients were died. We identified a 23 gene signature able to predict patient prognosis with microarray gene expression profiling. With the aim to produce a prognostication tool useful in clinical investigation we studied the expression of this 23 gene signature by RT-qPCR. Real time expression values relative to these 23 gene features were used to built a prediction method able to distinguish patients with good prognosis (more likely responsive to therapy) from patients with a bad prognosis (more likely irresponsive to therapy). These results demonstrated not only strong association of gene expression patterns with the survival of the patients but also a robust reproducibility of these gene expressionâ?? based predictors. We have analyzed 59 gliomatosis cerebri bioptic tissue comparing them with normal brain tissue derived by non-tumor surrounding tissue or commercial brain tissue. We alternatively labeled test sample and reference with Cy3 and Cy5 so to avoid a systematic bias due to label efficiency. Since the aim of the work was to find a correlation between prognosis and expression profile we considered each sample of a one prognosis group like a replicate of that group. Median survival data allowed us to distinguish 59 gliomatosis cerebri patients in two group: bad prognosis group, counting 42 patients, with a median survival of 14.58 months and good prognosis group with a median survival of 41.20 months. Moreover TP53 and PTEN mutation analysis was performed on this patients. Kaplan-Meier survival curve with log-rank test showed like each prognosis group can be furthermore divided in 2 groups where bad prognosis group patients bringing a mutation in one of the two gene analized or both genes showed the wrong survival. Microarray expression profile follwed by average linkage clustering analysis based on Pearson correlation coefficients was applied to all tissues on the basis of similarity in the expression pattern over all genes. As expected, it yielded two major clusters, one representing the good prognosis group and the other the bad prognosis one. Interesting these cluster are identical to cluster based on survival. Tumour classification based upon WHO 2007.

Project description:Background: Validated markers to predict outcome in rectal cancer patients treated with multimodal therapy remain elusive. Identifying molecular profiles for disease prognosis would be required for the design of clinical trials aimed at optimizing risk-adapted therapies. We have therefore used whole genome expression profiling of tumors of a large cohort of patients enrolled in the multicenter trials of the German Rectal Cancer Study Group (GRCSG) to identify molecular profiles for individualized therapy. Methods: We prospectively collected pretherapeutic biopsies from patients (n=300) treated according to the GRCSG trial guidelines from seven different German surgical departments using rigid quality controls. These samples were profiled by global gene expression analysis and a classifier developed to predict postoperative lymph node status and Disease Free Survival (DFS). The performance of the classifier was validated with an independent, prospectively collected set of samples. Findings: The final training and test set included 198 patients. Analyzes for postoperative nodal status and DFS revealed 69 and 674 differentially regulated genes, respectively. Depending on the classifier the accuracy to predict lymph node status ranged from 64% to 69% with negative predictive values between 72% and 74%. Stratification according to DFS resulted in a good (n=99), bad (n=96) and a small very bad prognosis group (n=3). Based on linear discriminant analysis the classifier for positive lymph node status was validated in 47 independent patients and revealed an accuracy of 72% and a positive predictive value of 100%. Thereby, prediction based on molecular profiling is superior to prediction based on conventional clinical markers. Interpretation: Whole genome expression analysis of pretherapeutical biopsies resulted in a molecular classifier of disease prognosis. This classifier was successfully validated. The high positive predictive value for post therapeutic lymph node status allows identification of patients requiring alternative or intensified treatment protocols. These data are currently validated for their clinical applicability and should be taken as basis for future molecular driven clinical trials to develop risk-adapted treatments.

Project description:Breast carcinoma (BC) is the leading cause of death in women worldwide, making up 23% of all cancers in women, with 1.38 million new cases worldwide annually and responsible for 460,000 deaths. Despite the significant advances in the identification of molecular markers and different modalities of treatment in primary BC, the ability to predict the metastatic behavior in breast cancer is still limited. The purpose of this study was to help identify novel molecular markers associated with clinical outcome in a cohort of Brazilian BC patients. We generated global gene expression profiles from 24 patients with invasive ductal BC followed for â¥ 5-years, including 15 samples from patients classified as presenting good prognosis based on traditional markers and clinical criteria and 9 patients that developed metastasis. We identified a set of 58 differentially expressed genes (p â¤0.01) between groups of patients with good and poor prognosis. Up-regulation of B3GNT7, PPM1D, TNKS2, PHB and GTSE1 in patients with poor prognosis was confirmed by quantitative RT-PCR in an independent sample set from patients with BC (47 with good prognosis and 8 that presented metastasis). Expression of BAD protein was investigated by immunohistochemistry in 1276 BC samples and confirmed the reduced expression levels in metastatic cases observed in the oligoarray data. These findings point to novel prognostic markers that can distinguish breast carcinoma samples according to clinical course and progression of the disease. Global expression profiles from 38 ductal breast tumor patient samples were used to search for molecular signatures correlated with current prognostic markers. A subset of 24 cases comprising 15 patients that remained free of disease after surgery and 9 patients that developed metastasis was used to identify candidate biomarkers associated with metastatic progression. Candidates were subsequently validated in additional independent samples by RT-qPCR or immunohistochemistry.

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Gliomatosis Cerebri I

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