Prognostic impact of gene expression-based classification for neuroblastoma.

Purpose: To evaluate the impact of a predefined gene expression-based classifier for clinical risk estimation and cytotoxic treatment decision making in neuroblastoma patients.

Patients And Methods: Gene expression profiles of 440 internationally collected neuroblastoma specimens were investigated by microarray analysis, 125 of which were examined prospectively. Patients were classified as either favorable or unfavorable by a 144-gene prediction analysis for microarrays (PAM) classifier established previously on a separate set of 77 patients.

Cross-study analysis of gene expression data for intermediate neuroblastoma identifies two biological subtypes.

Background: Neuroblastoma patients show heterogeneous clinical courses ranging from life-threatening progression to spontaneous regression. Recently, gene expression profiles of neuroblastoma tumours were associated with clinically different phenotypes. However, such data is still rare for important patient subgroups, such as patients with MYCN non-amplified advanced stage disease.

Classification of neuroblastoma patients by published gene-expression markers reveals a low sensitivity for unfavorable courses of MYCN non-amplified disease.

Currently, Pubmed lists 385 marker genes for neuroblastoma outcome. Using a customized neuroblastoma-microarray, we evaluated the prognostic impact of the gene-expression pattern of 349 of these candidates (90.6%) in 127 neuroblastoma patients with divergent outcome.

Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.

Purpose: To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease.

Patients And Methods: Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77).

Differential expression of neuronal genes defines subtypes of disseminated neuroblastoma with favorable and unfavorable outcome.

Purpose: Identification of molecular characteristics of spontaneously regressing stage IVS and progressing stage IV neuroblastoma to improve discrimination of patients with metastatic disease following favorable and unfavorable clinical courses.

Experimental Design: Serial analysis of gene expression profiles were generated from five stage IVS and three stage IV neuroblastoma. Differential expression of candidate genes was evaluated by real-time quantitative reverse transcription-PCR in 76 pretreatment tumor samples (stage IVS n=27 and stage IV n=49).

Gene expression signature predicting pathologic complete response with gemcitabine, epirubicin, and docetaxel in primary breast cancer.

Purpose: Primary systemic therapy (PST) with gemcitabine (G), epirubicin (E), and docetaxel (Doc) has resulted in a pathologic complete response (pCR) in 26% of primary breast cancer patients. This study was aimed at the identification of a gene expression signature in diagnostic core biopsy tissue samples that predicts pCR.

Patients And Methods: Core biopsy samples from patients with operable primary breast cancer, T2-4N0-2M0, enrolled onto two phase I and II trials evaluating GEDoc (n = 48) and GE sequentially followed by Doc (GEsDoc; n = 52) as PST were snap frozen and subjected to RNA expression profiling.

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes.

Background: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods.

Results: In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis.

Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling.

Neuroblastoma is a common childhood tumor comprising cases with rapid disease progression as well as spontaneous regression. Although numerous prognostic factors have been identified, risk evaluation in individual patients remains difficult. To define a reliable prognostic predictor and gene signatures characteristic of biological subgroups, we performed mRNA expression profiling of 68 neuroblastomas of all stages.

Automatic identification of subcellular phenotypes on human cell arrays.

Light microscopic analysis of cell morphology provides a high-content readout of cell function and protein localization. Cell arrays and microwell transfection assays on cultured cells have made cell phenotype analysis accessible to high-throughput experiments. Both the localization of each protein in the proteome and the effect of RNAi knock-down of individual genes on cell morphology can be assayed by manual inspection of microscopic images.