Mechanisms of DiseaseMolecular characterisation of soft tissue tumours: a gene expression study
Introduction
Soft-tissue tumours are neoplasms that show morphological and immunophenotypical characteristics of mesenchymal cells such as fibroblasts, adipocytes, muscle cells, or peripheral nerve-sheath cells. These rare tumours have a wide range of histological appearances, within which over 100 different entities have been defined.1 Although many soft-tissue tumours can be distinguished from each other, the boundaries between several diagnostic groups are vague and can overlap. The classification is further complicated by the fact that there are few reliable immunohistochemical markers to aid in tumour subclassification or to help predict a patient's outcome. The specialty of soft-tissue tumour pathology is therefore hampered—perhaps more than in any other group of tumours—by uncertainty surrounding diagnosis.
Over 30 000 expressed genes in man have (at least partly) been sequenced, and we expect that the sequence of all expressed genes will be available soon. Level of expression of such large numbers of genes, impossible by old methods, can now be studied with cDNA expression microarrays.2 Furthermore, hierarchical clustering analysis recognises groups of genes that are co-expressed, providing a new level of insight into their possible functions. Microarray hybridisation technologies have begun to identify new molecular subclassifications in breast carcinomas,3, 4 lymphomas,5 leukaemias,6 melanomas,7 and prostate cancer.8
We did cDNA gene microarray analysis on a set of 41 soft-tissue tumours to identify gene clusters that define tumour families on a molecular level, to relate these families to histological diagnoses and known molecular markers, and to highlight new markers of potential diagnostic value.
Section snippets
Specimens and RNA isolation
Frozen tissue samples were obtained from soft-tissue tumour specimens resected at the Vancouver Hospital and Health Sciences Centre, the Stanford University Medical Centre, and the Hospital of the University of Pennsylvania between 1993 and 2000. 41 specimens were used for this study: these included eight gastrointestinal stromal tumours; eight monophasic synovial sarcomas; four liposarcomas (one dedifferentiated, one myxoid, two pleomorphic); 11 leiomyosarcomas (including one primary and
Results
46 specimens from 41 soft-tissue tumours were analysed for geneexpression profiles by a combination of 22K and 42K cDNA microarrays. Histological sections of representative tumours are shown in figure 1; sections of all specimens used can be viewed on the accompanying website.9 The relation between tumour type and gene-expression profile was analysed for 5520 well defined genes that showed variation in expression across the 46 arrayed specimens (figure 2) by hierarchical cluster analysis13 and
Discussion
We have reported gene-expression profiles of 41 soft-tissue tumours with cDNA microarrays; the complete dataset is available in a searchable format on the website accompanying this report.9 We have shown that singular value decomposition analysis can be used to overcome bias introduced by use of different batches of arrays. The two methods used for removal of array bias showed strikingly similar results: 5520 genes survived reselection after removal of array bias by singular value
GLOSSARY
- eigengene
- A trend in gene expression. If an eigengene correlates with a suspected source of artifact, than it can be deduced from the dataset.
- eigenarray
- Represents a similar trend in array types to an eigengene.
- hierarchical clustering
- Clustering in data mining is a statistical discovery process that groups a set of data in such a way that the intracluster similarity is kept to a maximum and the intercluster similarity is kept to a minimum. In the clustering process, two clusters are merged only if
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