Unknown,Transcriptomics,Genomics,Proteomics

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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 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.

ORGANISM(S): Homo sapiens

SUBMITTER: Oscar D'Urso

PROVIDER: E-GEOD-11822 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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:OBJECTIVES: High activity of Indoleamine 2,3-dioxygenase1 (IDO1) in lung cancer patients converts tryptophan (Trp), which is the essential amino acid for T cell metabolism, to kynurenine (Kyn) and consequently suppresses anti-tumor immune responses. We aimed to track the dynamics of IDO1 activity in stage III non-small cell lung cancer (NSCLC) patients who received first-line radiotherapy (RT) and explore its association with survival outcomes.MATERIALS AND METHODS: Systemic IDO1 activity was calculated by Kyn:Trp ratio. Plasma levels of Kyn and Trp in 113 thoracic RT-received stage III NSCLC patients were measured by high-performance liquid chromatography before the initiation of RT. Dynamic change of IDO1 activity was followed in 24 patients by measuring Kyn:Trp ratio before, during and after RT administration.RESULTS: In 24 patients with dynamic tracking of plasma IDO1 activity, there was no significant alterations observed among the 3 time points (Friedman test, p=0.13). The changing pattern of Kyn:Trp ratio was divided into 4 groups: decreased consistently during RT, first increased then decreased, increased consistently, first decreased then increased. Patients whose Kyn:Trp ratio kept decreasing or first increased then decreased were defined as good-change group. The good-change status was identified as an independent positive factor for overall survival (OS) and progression-free survival (PFS) (p=0.04; p=0.01) in multivariate analysis among evaluated parameters. Patients with good change showed significantly superior local control than bad-change group (p=0.01, HR=0.22). In 113 stage III NSCLC patients with pre-radiation Kyn:Trp ratio, a trend that high baseline IDO1 activity was associated with short OS was observed (p=0.079).CONCLUSION: Favorable change of IDO1 activity during RT was associated with superior OS, PFS and local control. IDO1 activity is a promising biomarker for prognosis in stage III NSCLC patients.

Project description:Background. Genome-wide expression changes are associated with development of chemoresistance in patients with ovarian cancer (OVCA); the BCL2 antagonist of cell death (BAD) apoptosis pathway may play a role in clinical outcome. Methods. We analyzed specimens and/or genomic data from 1,406 patients and 116 cancer cell lines. Genome-wide expression changes and cisplatin-resistance were evaluated in OVCA cell lines subjected to a total of 144 (cisplatin)-treatment/recovery cycles. Pathway analysis was performed on genes associated with increasing cisplatin-resistance. BAD protein phosphorylation was studied in patient samples and cell lines, and small interfering RNAs (siRNA) used to explore the pathway as a therapeutic target. We evaluated the influence of BAD-pathway expression on chemosensitivity and/or clinical outcome using genomic data from 60 human cancer cell lines and ovarian, breast, colon, and brain cancers from 1,258 patients. Results. The BAD pathway was associated with evolution of OVCA cell line cisplatin-resistance (P<0.001) and resistance of 7 human cancer cell types to 8 cytotoxic agents (P<0.05). OVCA chemoresistance was associated with BAD protein phosphorylation, and targeted siRNA modulation produced corresponding changes in chemosensitivity. Expression of a 47-gene BAD-pathway signature was associated with survival of 1,258 patients with ovarian, breast, colon, and brain cancer. The OVCA BAD-pathway signature survival advantage was independent of surgical cytoreductive status. Conclusions. The BAD apoptosis pathway influences the sensitivity of human cancers to a variety of chemotherapies, likely via modulation of BAD-phosphorylation. The pathway has clinical relevance as a potential biomarker of therapeutic response, patient survival, and as a promising therapeutic target. Twenty-eight (28) advanced-stage serous epithelial ovarian cancers were resected at the time of primary surgery from patients who would receive platinum-based therapy. The tumors were arrayed on Affymetrix HG-U133A GeneChips. The samples were analyzed with respect to the BAD pathway for correlation to overall survival and cisplatin response.

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Gliomatosis Cerebri I

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