Cancer genomics

BACKGROUND: Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy. RESULTS: We develop a method for identification of copy number alterations in a tumor genome compared to its matched control, based on application of Smith-Waterman algorithm to single-end sequencing data. In a performance test with simulated data, our algorithm shows >90% sensitivity and >90% precision in detecting a single copy number change that contains approximately 500 reads for the normal sample. With 100-bp reads, this corresponds to a ~50 kb region for 1X genome coverage of the human genome. We further refine the algorithm to develop rSW-seq, (recursive Smith-Waterman-seq) to identify alterations in a complex configuration, which are commonly observed in the human cancer genome. To validate our approach, we compare our algorithm with an existing algorithm using simulated and publicly available datasets. We also compare the sequencing-based profiles to microarray-based results. CONCLUSION: We propose rSW-seq as an efficient method for detecting copy number changes in the tumor genome.

PURPOSE: Sunitinib (SU) is a multitargeted receptor tyrosine kinase inhibitor of the vascular endothelial growth factor and platelet-derived growth factor receptors. The present study examined SU and radiotherapy (RT) in a genetically engineered mouse model of soft tissue sarcoma (STS). METHODS AND MATERIALS: Primary extremity STSs were generated in genetically engineered mice. The mice were randomized to treatment with SU, RT (10 Gy x 2), or both (SU+RT). Changes in the tumor vasculature before and after treatment were assessed in vivo using fluorescence-mediated tomography. The control and treated tumors were harvested and extensively analyzed. RESULTS: The mean fluorescence in the tumors was not decreased by RT but decreased 38-44% in tumors treated with SU or SU+RT. The control tumors grew to a mean of 1378 mm(3) after 12 days. SU alone or RT alone delayed tumor growth by 56% and 41%, respectively, but maximal growth inhibition (71%) was observed with the combination therapy. SU target effects were confirmed by loss of target receptor phosphorylation and alterations in SU-related gene expression. Cancer cell proliferation was decreased and apoptosis increased in the SU and RT groups, with a synergistic effect on apoptosis observed in the SU+RT group. RT had a minimal effect on the tumor microvessel density and endothelial cell-specific apoptosis, but SU alone or SU+RT decreased the microvessel density by >66% and induced significant endothelial cell apoptosis. CONCLUSION: SU inhibited STS growth by effects on both cancer cells and tumor vasculature. SU also augmented the efficacy of RT, suggesting that this combination strategy could improve local control of STS.

BACKGROUND: Broader understanding of diverse angiogenic pathways in a particular cancer can lead to better utilization of anti-angiogenic therapies. The aim of this study was to develop profiles of angiogenesis-related gene and protein expression for various histologic subtypes of soft tissue sarcomas (STS) growing in different sites. MATERIALS AND METHODS: Plasma levels of vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF), angiopoietin 2 (Ang2), and leptin were determined in 108 patients with primary STS. Gene expression patterns were analyzed in 38 STS samples and 13 normal tissues using oligonucleotide microarrays. RESULTS: VEGF and bFGF plasma levels were elevated 10-13 fold in STS patients compared to controls. VEGF levels were broadly elevated while bFGF levels were higher in patients with fibrosarcomas and leiomyosarcomas. Ang2 levels correlated with tumor size and were most elevated for tumors located in the trunk, while leptin levels were highest in patients with liposarcomas. Hierarchical clustering of microarray data based on angiogenesis-related gene expression demonstrated that histologic subtypes of STS often shared similar expression patterns, and these patterns were distinctly different from those of normal tissues. Matrix metalloproteinase 2, platelet-derived growth factor receptor, alpha and Notch 4 were among several genes that were up-regulated at least 7-fold in STS. CONCLUSIONS: STS demonstrate significant heterogeneity in their angiogenic profiles based on size, histologic subtype, and location of tumor growth, which may have implications for anti-angiogenic strategies. Comparison of STS to normal tissues reveals a panel of upregulated genes that may be targets for future therapies.

A novel genome-wide screen that combines patient outcome analysis with array comparative genomic hybridization and mRNA expression profiling was developed to identify genes with copy number alterations, aberrant mRNA expression, and relevance to survival in glioblastoma. The method led to the discovery of physical gene clusters within the cancer genome with boundaries defined by physical proximity, correlated mRNA expression patterns, and survival relatedness. These boundaries delineate a novel genomic interval called the functional common region (FCR). Many FCRs contained genes of high biological relevance to cancer and were used to pinpoint functionally significant DNA alterations that were too small or infrequent to be reliably identified using standard algorithms. One such FCR contained the EphA2 receptor tyrosine kinase. Validation experiments showed that EphA2 mRNA overexpression correlated inversely with patient survival in a panel of 21 glioblastomas, and ligand-mediated EphA2 receptor activation increased glioblastoma proliferation and tumor growth via a mitogen-activated protein kinase-dependent pathway. This novel genome-wide approach greatly expanded the list of target genes in glioblastoma and represents a powerful new strategy to identify the upstream determinants of tumor phenotype in a range of human cancers.

There is an increasing need to link the large amount of genotypic data, gathered using microarrays for example, with various phenotypic data from patients. The classification problem in which gene expression data serve as predictors and a class label phenotype as the binary outcome variable has been examined extensively, but there has been less emphasis in dealing with other types of phenotypic data. In particular, patient survival times with censoring are often not used directly as a response variable due to the complications that arise from censoring. We show that the issues involving censored data can be circumvented by reformulating the problem as a standard Poisson regression problem. The procedure for solving the transformed problem is a combination of two approaches: partial least squares, a regression technique that is especially effective when there is severe collinearity due to a large number of predictors, and generalized linear regression, which extends standard linear regression to deal with various types of response variables. The linear combinations of the original variables identified by the method are highly correlated with the patient survival times and at the same time account for the variability in the covariates. The algorithm is fast, as it does not involve any matrix decompositions in the iterations. We apply our method to data sets from lung carcinoma and diffuse large B-cell lymphoma studies to verify its effectiveness.