Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Gene expression analysis of pre-defined sets of neoplastic tissues

ABSTRACT: To identify a group of genes, characteristic of a single neoplasia and determinant for the risk to relapse, we have performed gene expression analysis of ductal breast carcinoma samples using Affymetrix oligonucletide microarray (HU133A). Biopses from 60 patients, 30 good prognosis (desease free from six to ten years after surgical treatment) and 30 poor prognosis, have been analyzed. From each group we have created six homogeneous pools to show, during the analysis, only genes expressed in a greatly different way. A reduced number of cases has been used to avoid dilution of genes expressed only in few samples. Analysis have identified 77 candidate genes with a specific ontological distribution that contained some genes that were linked to breast cancer by previous studies. Microarray results validation has been performed by real-time PCR analysis on 127 single cases of breast cancer. Gene function study and literature data allow to define our candidate genes as potential markers of prognosis. Keywords: Breast cancer gene expression analysis From a group of 130 frozen tumor samples, 30 cases of ductal invasive carcinoma with recurrence within 72 months from surgery (high-risk) and 30 without recurrence (low-risk) were selected for microarray analysis. From each group, 6 RNA pools of 5 samples each were prepared and analyzed on GeneChip Human Genome U133A oligonucleotide microarrays. Hybridizations were performed in technical duplicates, in two experimental sessions.

ORGANISM(S): Homo sapiens

SUBMITTER: Daniela Cimino

PROVIDER: E-GEOD-9662 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Introduction: MicroRNAs (miRNAs) are small, non-coding RNA molecules involved in post-transcriptional gene regulation and have recently been shown to play a role in cancer metastasis. In solid tumors, especially breast cancer, alterations in miRNA expression contribute to cancer pathogenesis, including metastasis. Considering the emerging role of miRNAs in metastasis, the identification of predictive markers is necessary to further understanding of stage-specific breast cancer development. This is a retrospective analysis that aimed to identify molecular biomarkers related to distant breast cancer metastasis development. Methods: A retrospective case cohort study was performed in 64 breast cancer patients treated during the period from 1998-2001. The case group (n=29) consisted of patients with a poor prognosis who presented with breast cancer recurrence or metastasis during follow up. The control group (n=35) consisted of a random sample of patients with a good prognosis who did not develop breast cancer recurrence or metastasis. These patient groups were stratified according to TNM clinical stage (CS) I, II and III, and the main clinical features of the patients were homogeneous. miRNA profiling was performed using formalin-fixed, paraffin-embedded tumors. Biomarkers related to metastatic potential were identified independent of clinical stage, and a cutoff point was selected based on the optimal sensitivity and specificity (ROC curve). Finally, a hazard risk analysis of these biomarkers was performed to evaluate their relation to metastatic potential. Results: miRNA expression profiling identified several miRNAs that were either specific and shared across all clinical stages (p?0.05). Among these, we identified miRNAs previously associated with cell motility (let-7 family), cell proliferation and invasion (hsa-miR-16 and has-miR-205) and distant metastasis (hsa-miR-21). In addition, hsa-miR-494 and hsa-miR-21 were up-regulated in metastatic cases of CSI and II. Furthermore, the combination of the 3 miRNAs identified for CSII (hsa-miR-494, hsa-miR-183 and hsa-miR-21) was significant and were a more effective risk marker compared to the single miRNAs. Conclusions: Women with metastatic breast cancer, especially CSII, presented up-regulated levels of miR-183, miR-494 and miR-21, which were associated with a poor prognosis. These miRNAs therefore represent new risk biomarkers of breast cancer metastasis and may be useful for future targeted therapies.

Project description:When making treatment decisions, oncologists often stratify breast cancers into a low-risk group (ER+, low grade); an intermediate-risk group (ER+, high grade); and a high-risk group that includes Her2+ and triple-negative (ER-/PR-/Her2-) tumors. None of the currently available gene signatures correlates to this clinical classification. We aimed to develop a test that is practical for the oncologists, that offers both molecular characterization of BCs, and improved prediction of prognosis and treatment response. We investigated the molecular basis of such clinical practice by grouping Her2+ and triple-negative breast cancers together during clustering analyses on the genome-wide gene expression profiles of our training cohort, mostly derived from fine needle aspiration biopsies (FNABs) of 149 consecutive evaluable Breast cancers. The analyses consistently divided these tumors into a three-cluster pattern, similar to clinical risk-stratification groups, that was reproducible in published microarray databases (n=2487) annotated with clinical outcomes. The clinicopathologic parameters of each of these three molecular groups were also similar to clinical classification. The low-risk group had good outcomes and benefited from endocrine therapy. Both intermediate- and high-risk groups had poor outcomes and were resistant to endocrine therapy. The latter demonstrated the highest rate of complete pathological response to neoadjuvant chemotherapy; the highest activities in MYC, E2F1, Ras, β-Catenin and IFN-γ pathways; and poor prognosis predicted by 14 independent prognostic signatures. Based on a multivariate analysis, this new gene signature, termed ClinicoMolecular Triad Classification, predicted recurrence and treatment response better than all pathologic parameters and other prognostic signatures. 149 invasive breast cancers from the 172 specimens contained 161 tumors were used in this study. Expression data of the 11 tumors with replicate was separately combined before analysis.

Project description:Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities. Training set comprises 59 samples and validation set 50 samples

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Gene expression analysis of pre-defined sets of neoplastic tissues

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