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| 2. |
- Frigyesi, Attila, et al.
(författare)
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Independent component analysis reveals new and biologically significant structures in micro array data
- 2006
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Background: An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation ( BSS) problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes. Results: We applied independent component analysis (ICA) to three different microarray data sets; two tumor data sets and one time series experiment. To obtain reliable components we used iterated ICA to estimate component centrotypes. We found that many of the low ranking components indeed may show a strong biological coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology ( GO) categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes. Conclusion: Although the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA.
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| 3. |
- Frigyesi, Attila, et al.
(författare)
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Non-negative matrix factorization for the analysis of complex gene expression data : Identification of clinically relevant tumor subtypes
- 2008
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Ingår i: Cancer Informatics. - : SAGE Publications. - 1176-9351. ; 6, s. 275-292
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Tidskriftsartikel (refereegranskat)abstract
- Non-negative matrix factorization (NMF) is a relatively new approach to analyze gene expression data that models data by additive combinations of non-negative basis vectors (metagenes). The non-negativity constraint makes sense biologically as genes may either be expressed or not, but never show negative expression. We applied NMF to five different microarray data sets. We estimated the appropriate number metagens by comparing the residual error of NMF reconstruction of data to that of NMF reconstruction of permutated data, thus finding when a given solution contained more information than noise. This analysis also revealed that NMF could not factorize one of the data sets in a meaningful way. We used GO categories and pre defined gene sets to evaluate the biological significance of the obtained metagenes. By analyses of metagenes specific for the same GO-categories we could show that individual metagenes activated different aspects of the same biological processes. Several of the obtained metagenes correlated with tumor subtypes and tumors with characteristic chromosomal translocations, indicating that metagenes may correspond to specific disease entities. Hence, NMF extracts biological relevant structures of microarray expression data and may thus contribute to a deeper understanding of tumor behavior.
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| 5. |
- Höglund, Mattias, et al.
(författare)
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A gene fusion network in human neoplasia.
- 2006
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Ingår i: Oncogene. - : Springer Science and Business Media LLC. - 1476-5594 .- 0950-9232. ; 25:18, s. 2674-2678
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Tidskriftsartikel (refereegranskat)
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| 7. |
- Jin, Yuesheng, et al.
(författare)
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Distinct mitotic segregation errors mediate chromosomal instability in aggressive urothelial cancers.
- 2007
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 13:6, s. 1703-1712
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Chromosomal instability (CIN) is believed to have an important role in the pathogenesis of urothelial cancer (UC). The aim of this study was to evaluate whether disturbances of mitotic segregation contribute to CIN in UC, if these processes have any effect on the course of disease, and how deregulation of these mechanisms affects tumor cell growth. Experimental Design: We developed molecular cytogenetic methods to classify mitotic segregation abnormalities in a panel of UC cell lines. Mitotic instabilities were then scored in biopsies from 52 UC patients and compared with the outcome of tumor disease. Finally, UC cells were exposed in vitro to a telomerase inhibitor to assess how this affects mitotic stability and cell proliferation. Results: Three distinct chromosome segregation abnormalities were identified: (a) telomere dysfunction, which triggers structural rearrangements and loss of chromosomes through anaphase bridging; (b) sister chromatid nondisjunction, which generates discrete chromosomal copy number variations; and (c) supernumerary centrosomes, which cause dramatic shifts in chromosome copy number through multipolar cell division. Chromosome segregation errors were already present in preinvasive tumors and a high rate mitotic instability was an independent predictor of poor survival. However, induction of even higher levels of the same segregation abnormalities in UC cells by telomerase inhibition in vitro led to reduced tumor cell proliferation and clonogenic survival. Conclusion: Several distinct chromosome segregation errors contribute to CIN in UC, and the rate of such mitotic errors has a significant effect on the clinical course. Efficient tumor cell proliferation may depend on the tight endogenous control of these processes.
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| 8. |
- Lauss, Martin, et al.
(författare)
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Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier
- 2010
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407 .- 1471-2407. ; 10, s. 532-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Genome wide gene expression data is a rich source for the identification of gene signatures suitable for clinical purposes and a number of statistical algorithms have been described for both identification and evaluation of such signatures. Some employed algorithms are fairly complex and hence sensitive to over-fitting whereas others are more simple and straight forward. Here we present a new type of simple algorithm based on ROC analysis and the use of metagenes that we believe will be a good complement to existing algorithms.Results: The basis for the proposed approach is the use of metagenes, instead of collections of individual genes, and a feature selection using AUC values obtained by ROC analysis. Each gene in a data set is assigned an AUC value relative to the tumor class under investigation and the genes are ranked according to these values. Metagenes are then formed by calculating the mean expression level for an increasing number of ranked genes, and the metagene expression value that optimally discriminates tumor classes in the training set is used for classification of new samples. The performance of the metagene is then evaluated using LOOCV and balanced accuracies.Conclusions: We show that the simple uni-variate gene expression average algorithm performs as well as several alternative algorithms such as discriminant analysis and the more complex approaches such as SVM and neural networks. The R package rocc is freely available at http://cran.r-project.org/web/packages/rocc/index.html.
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| 9. |
- Lindgren, David, et al.
(författare)
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Combined Gene Expression and Genomic Profiling Define Two Intrinsic Molecular Subtypes of Urothelial Carcinoma and Gene Signatures for Molecular Grading and Outcome
- 2010
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Ingår i: Cancer Research. - 0008-5472 .- 1538-7445. ; 70:9, s. 3463-3472
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Tidskriftsartikel (refereegranskat)abstract
- In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data show that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathologic data, it was possible to distinguish two molecular subtypes of T(a) and T(1) tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low-grade (G(1)/G(2)) and high-grade (G(3)) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathologic grading. We also present a gene expression signature with independent prognostic effect on metastasis and disease-specific survival. We conclude that the combination of molecular and histopathologic classification systems might provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type.
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| 10. |
- Veerla, Srinivas, et al.
(författare)
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MiRNA expression in urothelial carcinomas: Important roles of miR-10a, miR-222, miR-125b, miR-7 and miR-452 for tumor stage and metastasis, and frequent homozygous losses of miR-31.
- 2009
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 124, s. 2236-2242
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Tidskriftsartikel (refereegranskat)abstract
- We analyzed 34 cases of urothelial carcinomas by miRNA, mRNA and genomic profiling. Unsupervised hierarchical clustering using expression information for 300 miRNAs produced 3 major clusters of tumors corresponding to Ta, T1 and T2-T3 tumors, respectively. A subsequent SAM analysis identified 51 miRNAs that discriminated the 3 pathological subtypes. A score based on the expression levels of the 51 miRNAs, identified muscle invasive tumors with high precision and sensitivity. MiRNAs showing high expression in muscle invasive tumors included miR-222 and miR-125b and in Ta tumors miR-10a. A miRNA signature for FGFR3 mutated cases was also identified with miR-7 as an important member. MiR-31, located in 9p21, was found to be homozygously deleted in 3 cases and miR-452 and miR-452* were shown to be over expressed in node positive tumors. In addition, these latter miRNAs were shown to be excellent prognostic markers for death by disease as outcome. The presented data shows that pathological subtypes of urothelial carcinoma show distinct miRNA gene expression signatures. (c) 2009 Wiley-Liss, Inc.
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