Unknown,Transcriptomics,Genomics,Proteomics

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Prostate Cancer

ABSTRACT: Prostate cancer, a leading cause of cancer death, displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. To explore potential molecular variation underlying this clinical heterogeneity, we profiled gene expression in 62 primary prostate tumors, as well as 41 normal prostate specimens and nine lymph node metastases, using cDNA microarrays containing approximately 26,000 genes. Unsupervised hierarchical clustering readily distinguished tumors from normal samples, and further identified three subclasses of prostate tumors based on distinct patterns of gene expression. High-grade and advanced stage tumors, as well as tumors associated with recurrence, were disproportionately represented among two of the three subtypes, one of which also included most lymph node metastases. To further characterize the clinical relevance of tumor subtypes, we evaluated as surrogate markers two genes differentially expressed among tumor subgroups by using immunohistochemistry on tissue microarrays representing an independent set of 225 prostate tumors. Positive staining for MUC1, a gene highly expressed in the subgroups with "aggressive" clinicopathological features, was associated with an elevated risk of recurrence (P = 0.003), whereas strong staining for AZGP1, a gene highly expressed in the other subgroup, was associated with a decreased risk of recurrence (P = 0.0008). In multivariate analysis, MUC1 and AZGP1 staining were strong predictors of tumor recurrence independent of tumor grade, stage, and preoperative prostate-specific antigen levels. Our results suggest that prostate tumors can be usefully classified according to their gene expression patterns, and these tumor subtypes may provide a basis for improved prognostication and treatment stratification. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Keywords: disease_state_design Using regression correlation

ORGANISM(S): Homo sapiens

SUBMITTER: Jacques Lapointe

PROVIDER: E-GEOD-3933 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Gene expression profiling identifies clinically relevant subtypes of prostate cancer.

Lapointe Jacques J Li Chunde C Higgins John P JP van de Rijn Matt M Bair Eric E Montgomery Kelli K Ferrari Michelle M Egevad Lars L Rayford Walter W Bergerheim Ulf U Ekman Peter P DeMarzo Angelo M AM Tibshirani Robert R Botstein David D Brown Patrick O PO Brooks James D JD Pollack Jonathan R JR

Proceedings of the National Academy of Sciences of the United States of America 20040107 3

Prostate cancer, a leading cause of cancer death, displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. To explore potential molecular variation underlying this clinical heterogeneity, we profiled gene expression in 62 primary prostate tumors, as well as 41 normal prostate specimens and nine lymph node metastases, using cDNA microarrays containing approximately 26,000 genes. Unsupervised hierarchical clustering readily distinguished tumors from no ...[more]

PMID: 14711987

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Project description:Summary: Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes (luminal A, luminal B, ERBB2-associated, basal-like and normal-like) with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes. Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression. Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 presumptive homozygous deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes. Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 50 human breast epithelial cell lines, in comparison to a universal RNA reference. Expression data were analyzed by hierarchical clustering to identify subgroups, and gene set enrichment analysis to identify subgroup-specific gene pathways.

Project description:The first goal of the study was to find the genes that explain the difference in progression of age-related retinal degeneration (ageRD) between the inbred strains C57BL/6J-c2J (B6a) and BALB/cByJ (C). The specific goal was to find the candidate genes that explain the chromosome 6 and chromosome 10 quantitative trait loci (QTL) found in a previous study. Genes differentially expressed in retina between both strains and that map to the QTL are candidate genes. By integrating different approaches: microarray study, knowledge-based candidate gene search and screening of genetic variants, we found 35 candidate genes for chromosome 6 QTL and 14 candidate genes for chromosome 10 QTL. Five genes for each QTL were selected for sequencing and qPCR analysis. One gene, Tnfaip3 was selected for phenotypic testing and was excluded as the quantitative trait gene for chr10 QTL. The second goal of the study was to find biological processes and pathways associated with ageRD by Gene Ontology analysis of the microarray data. We concentrated on GO terms overrepresented in either strain at 8 months; the age when there is a difference in the phenotype (outer nuclear layer thickness). The GO analysis revealed that transcripts associated with inflammation, mitochondrial envelope, DNA repairing and oxidative stress were increased in the ageRD sensitive C strain with age (8 months). In the ageRD resistant B6a strain transcripts associated with antioxidant response, regulation of blood vessel size and growth factor activity were increased with age. Transcripts associated with cell remodeling and lipid metabolism were increased in either strain with age. This study aims to identify gene expression differences in posterior eyecups between the strains C and B6a at 4 months and at 8 months. Twelve animals were used per strain and time point for a total of 48 mice. Total RNA for 3 animals (6 eyes) were pooled giving a total of 4 biological replicates per strain and time point. The 4-month C samples labeled with Cy5 were hybridized with the 4-month B6a samples labeled with Cy3 (4 biological replicates); the 8-month C samples labeled with Cy5 were hybridized with the 8-month B6a samples labeled with Cy3, for 8 arrays (4 biological replicates). A second set of eight arrays were used to analyzed the same RNA samples but with the dye orientation reversed, yielding a total of 16 arrays in the experiment. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc.

Dataset Information

Prostate Cancer

Publications

Gene expression profiling identifies clinically relevant subtypes of prostate cancer.

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