Gene expression profiling in rheumatology.

In the last decade, the analysis of gene expression in tissues and cells has evolved from the analysis of a selected set of genes to an efficient high throughput whole-genome screening approach of potentially all genes expressed. Development of sophisticated methodologies such as microarray technology allows an open-ended survey to identify comprehensively the fraction of genes that are differentially expressed between samples and that define the samples' unique biology. By a global analysis of the genes that are expressed in cells and tissues of an individual under different conditions and during disease, we can build up "gene expression profiles (signatures)" which characterize the dynamic functioning of the genome under pathophysiological conditions. This strategy also provides the means to subdivide patients that suffer from a complex heterogeneous disease into more homogeneous subgroups. Such discovery-based research identifies biological processes that may include new genes with unknown function or genes not previously known to be involved in this process. The latter category may hold surprises that sometimes urge us to redirect our thinking. We have used microarrays to disclose the heterogeneity of rheumatoid arthritis (RA) patients at the level of gene expression of the affected synovial tissues. Analysis of the expression profiles of synovial tissues from different patients with RA revealed considerable variability, resulting in the identification of at least two molecularly distinct forms of RA tissues. One is characterized by genes that indicate an active inflammatory infiltrate with high immunoglobulin production, whereas the other type shows little immune activation and instead shows a higher stromal cell activity. These results confirm the heterogeneous nature of RA and suggest the existence of distinct pathogenic mechanisms that contribute to RA. The differences in expression profiles provide opportunities to stratify patients for intervention therapies based on molecular criteria.

Fibroblast-like synoviocytes derived from patients with rheumatoid arthritis show the imprint of synovial tissue heterogeneity: evidence of a link between an increased myofibroblast-like phenotype and high-inflammation synovitis.

OBJECTIVE: Given the heterogeneity of gene expression patterns and cellular distribution between rheumatoid arthritis (RA) synovial tissues, we sought to determine whether this variability was also reflected at the level of the fibroblast-like synoviocyte (FLS) cultured from RA synovial tissues. METHODS: Gene expression profiles in FLS cultured from synovial tissues obtained from 19 RA patients were analyzed using complementary DNA microarrays and hierarchical cluster analysis. To validate the subclassification, we performed prediction analysis and principal components analysis. Genes that differed significantly in their expression between FLS cultures were selected using Statistical Analysis of Microarrays software. Real-time quantitative polymerase chain reaction was performed to validate the microarray data. Immunocytochemistry was applied to study the expression of the genes of interest in FLS and synovial tissues. RESULTS: Hierarchical clustering identified 2 main groups of FLS characterized by distinctive gene expression profiles. FLS from high-inflammation synovial tissues revealed increased expression of a transforming growth factor beta/activin A-inducible gene profile that is characteristic of myofibroblasts, a cell type considered to be involved in wound healing, whereas increased production of growth factor (insulin-like growth factor 2/insulin-like growth factor binding protein 5) appeared to constitute a characteristic feature of FLS derived from low-inflammation synovial tissues. The molecular feature that defines the myofibroblast-like phenotype was reflected as an increased proportion of myofibroblast-like cells in the heterogeneous FLS population. Myofibroblast-like cells were also found upon immunohistochemical analysis of synovial tissue. CONCLUSION: Our findings support the notion that heterogeneity between synovial tissues is reflected in FLS as a stable trait, and provide evidence of a possible link between the behavior of FLS and the inflammation status of RA synovium.

Rheumatoid arthritis is a heterogeneous disease: evidence for differences in the activation of the STAT-1 pathway between rheumatoid tissues.

OBJECTIVE: To generate a molecular description of synovial tissue from rheumatoid arthritis (RA) patients that would allow us to unravel novel aspects of pathogenesis and to identify different forms of disease. METHODS: We applied complementary DNA microarray analysis to profile gene expression, with a focus on immune-related genes, in affected joint tissues from RA patients and in tissues from osteoarthritis (OA) patients as a control. To validate microarray data, real-time polymerase chain reaction was performed on genes of interest. RESULTS: The gene expression signatures of synovial tissues from RA patients showed considerable variability, resulting in the identification of at least two molecularly distinct forms of RA tissues. One class of tissues revealed abundant expression of clusters of genes indicative of an involvement of the adaptive immune response. Detailed analysis of the expression profile provided evidence for a prominent role of an activated signal transducer and activator of transcription 1 pathway in these tissues. The expression profiles of another group of RA tissues revealed an increased tissue remodeling activity and a low inflammatory gene expression signature. The gene expression pattern in the latter tissues was reminiscent of that observed in the majority of OA tissues. CONCLUSION: The differences in the gene expression profiles provide a unique perspective for distinguishing different pathogenetic RA subsets based on molecular criteria. These data reflect important aspects of molecular variation that are relevant for understanding the biologic dysregulation underlying these subsets of RA. This approach may also help to define homogeneous groups for clinical studies and evaluation of targeted therapies.

Transcriptional programs activated by exposure of human prostate cancer cells to androgen.

BACKGROUND: Androgens are required for both normal prostate development and prostate carcinogenesis. We used DNA microarrays, representing approximately 18,000 genes, to examine the temporal program of gene expression following treatment of the human prostate cancer cell line LNCaP with a synthetic androgen. RESULTS: We observed statistically significant changes in levels of transcripts of more than 500 genes. Many of these genes were previously reported androgen targets, but most were not previously known to be regulated by androgens. The androgen-induced expression programs in three additional androgen-responsive human prostate cancer cell lines, and in four androgen-independent subclones derived from LNCaP, shared many features with those observed in LNCaP, but some differences were observed. A remarkable fraction of the genes induced by androgen appeared to be related to production of seminal fluid and these genes included many with roles in protein folding, trafficking, and secretion. CONCLUSIONS: Prostate cancer cell lines retain features of androgen responsiveness that reflect normal prostatic physiology. These results provide a broad view of the effect of androgen signaling on the transcriptional program in these cancer cells, and a foundation for further studies of androgen action.

Molecular characterisation of soft tissue tumours: a gene expression study.

BACKGROUND: Soft-tissue tumours are derived from mesenchymal cells such as fibroblasts, muscle cells, or adipocytes, but for many such tumours the histogenesis is controversial. We aimed to start molecular characterisation of these rare neoplasms and to do a genome-wide search for new diagnostic markers. METHODS: We analysed gene-expression patterns of 41 soft-tissue tumours with spotted cDNA microarrays. After removal of errors introduced by use of different microarray batches, the expression patterns of 5520 genes that were well defined were used to separate tumours into discrete groups by hierarchical clustering and singular value decomposition. FINDINGS: Synovial sarcomas, gastrointestinal stromal tumours, neural tumours, and a subset of the leiomyosarcomas, showed strikingly distinct gene-expression patterns. Other tumour categories--malignant fibrous histiocytoma, liposarcoma, and the remaining leiomyosarcomas--shared molecular profiles that were not predicted by histological features or immunohistochemistry. Strong expression of known genes, such as KIT in gastrointestinal stromal tumours, was noted within gene sets that distinguished the different sarcomas. However, many uncharacterised genes also contributed to the distinction between tumour types. INTERPRETATION: These results suggest a new method for classification of soft-tissue tumours, which could improve on the method based on histological findings. Large numbers of uncharacterised genes contributed to distinctions between the tumours, and some of these could be useful markers for diagnosis, have prognostic significance, or prove possible targets for treatment.