Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas.

BACKGROUND: High-grade soft tissue sarcomas are a heterogeneous, complex group of aggressive malignant tumors showing mesenchymal differentiation. Recently, soft tissue sarcomas have increasingly been classified on the basis of underlying genetic alterations; however, the role of aberrant DNA methylation in these tumors is not well understood and, consequently, the usefulness of methylation-based classification is unclear. RESULTS: We used the Infinium HumanMethylation27 platform to profile DNA methylation in 80 primary, untreated high-grade soft tissue sarcomas, representing eight relevant subtypes, two non-neoplastic fat samples and 14 representative sarcoma cell lines. The primary samples were partitioned into seven stable clusters. A classification algorithm identified 216 CpG sites, mapping to 246 genes, showing different degrees of DNA methylation between these seven groups. The differences between the clusters were best represented by a set of eight CpG sites located in the genes SPEG, NNAT, FBLN2, PYROXD2, ZNF217, COL14A1, DMRT2 and CDKN2A. By integrating DNA methylation and mRNA expression data, we identified 27 genes showing negative and three genes showing positive correlation. Compared with non-neoplastic fat, NNAT showed DNA hypomethylation and inverse gene expression in myxoid liposarcomas, and DNA hypermethylation and inverse gene expression in dedifferentiated and pleomorphic liposarcomas. Recovery of NNAT in a hypermethylated myxoid liposarcoma cell line decreased cell migration and viability. CONCLUSIONS: Our analysis represents the first comprehensive integration of DNA methylation and transcriptional data in primary high-grade soft tissue sarcomas. We propose novel biomarkers and genes relevant for pathogenesis, including NNAT as a potential tumor suppressor in myxoid liposarcomas.

MicroRNA profiling of primary high-grade soft tissue sarcomas.

High-grade soft tissue sarcomas are a heterogeneous and complex group of tumors. MicroRNAs (miRNAs) are considered as attractive candidates that may improve diagnostic, prognostic, and predictive characterization of this group of malignancies. We performed a comprehensive miRNA expression analysis in a series of 76 untreated, primary high-grade soft tissue sarcomas representing eight subtypes, and in a panel of 15 representative sarcoma cell lines using microarray technology. This screening revealed unique miRNA expression patterns for synovial sarcomas, myxoid liposarcomas, and leiomyosarcomas, and defined unique sets of miRNAs discriminating the different liposarcoma subtypes from non-neoplastic adipose tissue. The over-represented miRNAs included members of the miR-200 family in synovial sarcomas, and the tumor-associated miR-9 and miR-9* in myxoid liposarcomas compared to adipose tissue. Moreover, we found coexpression of 63 miRNAs clustering in a genetically imprinted chromosomal region 14q32.2 separating primary sarcoma samples and sarcoma cell lines into two molecular subgroups. Taken together, our comprehensive miRNA profiling identified a novel set of miRNAs that might contribute to sarcomagenesis and provide a starting point for experimental modulation of relevant targets for new therapeutic strategies in high-grade sarcomas.

Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets.

BACKGROUND: Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis. RESULTS: To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer. CONCLUSIONS: Current methods of theme-driven survival studies assume uniformity of p-values for random genesets, which can lead to false conclusions. Our approach provides a method to correct for this pitfall, and provides a novel route to identifying higher-level biological themes and pathways with prognostic power in clinical microarray datasets.