Showing papers by "Rainer Spang published in 2007"

Inferring cellular networks – a review

Abstract: In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.

Gene Expression Signatures for Tumor Progression, Tumor Subtype, and Tumor Thickness in Laser-Microdissected Melanoma Tissues

Abstract: Purpose: To better understand the molecular mechanisms of malignant melanoma progression and metastasis, gene expression profiling was done of primary melanomas and melanoma metastases. Experimental Design: Tumor cell–specific gene expression in 19 primary melanomas and 22 melanoma metastases was analyzed using oligonucleotide microarrays after laser-capture microdissection of melanoma cells. Statistical analysis was done by random permutation analysis and support vector machines. Microarray data were further validated by immunohistochemistry and immunoblotting. Results: Overall, 308 genes were identified that showed significant differential expression between primary melanomas and melanoma metastases (false discovery rate ≤ 0.05). Significantly overrepresented gene ontology categories in the list of 308 genes were cell cycle regulation, mitosis, cell communication, and cell adhesion. Overall, 47 genes showed up-regulation in metastases. These included Cdc6, Cdk1, septin 6, mitosin, kinesin family member 2C, osteopontin , and fibronectin . Down-regulated genes included E-cadherin, fibroblast growth factor binding protein , and desmocollin 1 and desmocollin 3, stratifin/14-3-3σ , and the chemokine CCL27 . Using support vector machine analysis of gene expression data, a performance of >85% correct classifications for primary melanomas and metastases was reached. Further analysis showed that subtypes of primary melanomas displayed characteristic gene expression patterns, as do thin tumors (≤1.0 mm Breslow thickness) compared with intermediate and thick tumors (>2.0 mm Breslow thickness). Conclusions: Taken together, this large-scale gene expression study of malignant melanoma identified molecular signatures related to metastasis, melanoma subtypes, and tumor thickness. These findings not only provide deeper insights into the pathogenesis of melanoma progression but may also guide future research on innovative treatments.

Nested effects models for high-dimensional phenotyping screens

Abstract: Motivation: In high-dimensional phenotyping screens, a large number of cellular features is observed after perturbing genes by knockouts or RNA interference. Comprehensive analysis of perturbation effects is one of the most powerful techniques for attributing functions to genes, but not much work has been done so far to adapt statistical and computational methodology to the specific needs of large-scale and high-dimensional phenotyping screens. Results: We introduce and compare probabilistic methods to efficiently infer a genetic hierarchy from the nested structure of observed perturbation effects. These hierarchies elucidate the structures of signaling pathways and regulatory networks. Our methods achieve two goals: (1) they reveal clusters of genes with highly similar phenotypic profiles, and (2) they order (clusters of) genes according to subset relationships between phenotypes. We evaluate our algorithms in the controlled setting of simulation studies and show their practical use in two experimental scenarios: (1) a data set investigating the response to microbial challenge in Drosophila melanogaster, and (2) a compendium of expression profiles of Saccharomyces cerevisiae knockout strains. We show that our methods identify biologically justified genetic hierarchies of perturbation effects. Availability: The software used in our analysis is freely available in the R package ‘nem’ from www.bioconductor.org Contact: ogt@cs.princeton.edu

Gene expression shift towards normal B cells, decreased proliferative capacity and distinct surface receptors characterize leukemic blasts persisting during induction therapy in childhood acute lymphoblastic leukemia.

Abstract: In childhood acute lymphoblastic leukemia (ALL), persistence of leukemic blasts during therapy is of crucial prognostic significance. In the present study, we address molecular and cell biologic features of blasts persisting after 1 week of induction glucocorticoid therapy. Genome-wide gene expression analysis of leukemic samples from precursor B-cell ALL patients (n=18) identified a set of genes differentially expressed in blasts at diagnosis day 0 (d0) and persisting on day 8 (d8). Expression changes indicate a shift towards mature B cells, inhibition of cell cycling and increased expression of adhesion (CD11b/ITGAM) and cytokine (CD119/IFNGR1) receptors. A direct comparison with normal B cells, which are largely therapy resistant, confirmed the differentiation shift at the mRNA (n=10) and protein (n=109) levels. Flow cytometric analysis in independent cohorts of patients confirmed both a decreased proliferative activity (n=13) and the upregulation of CD11b and CD119 (n=29) in d8 blasts. The differentiation shift and low proliferative activity in d8 blasts may account for the persistence of blasts during therapy and affect their sensitivity to further therapeutic treatment. CD11b and CD119 are potential specific markers for d8 blast persistence and detection of minimal residual disease, which warrant further investigation.

Ataxia Telangiectasia-Mutated Gene Is a Possible Biomarker for Discrimination of Infiltrative Deep Penetrating Nevi and Metastatic Vertical Growth Phase Melanoma

Abstract: The deep penetrating nevus (DPN) is a variant of benign melanocytic nevus with clinical and histologic features mimicking vertical growth phase, nodular malignant melanoma (NMM). Because fatal misdiagnosis such as NMM occurs in 29% to 40% of the DPN, molecular differentiation markers are highly desirable. Beyond the clinical demand for precise diagnosis and diagnosis-adapted, preventive therapeutic strategies, the DPN represents a valuable natural model for melanocytic invasion without metastatic potential that per se deserves further investigations. In the present study, at first, we used a genome-wide, microarray-based approach to systematically prescreen for possible molecular markers differentially expressed between selected cases of typical DPN (n=4) and metastatic NMM controls (n=4). Gene expression profiling was done on Affymetrix Human X3P microarrays. Of the 47,000 genes spotted, we identified a list of 227 transcripts, which remained significantly regulated at a false discovery rate of 5%. Subsequently, we verified the expression of a subset of the most interesting transcripts in a larger immunohistochemical series (DPN, n=17; NMM, n=16). Of these transcripts, three were selected for immunohistochemical confirmation: tissue inhibitor of metalloproteinase-2, tumor protein D52, and ataxia telangiectasia-mutated gene (ATM). Additional criteria for selection from the list of 227 significantly regulated transcripts were grouping into functional Ingenuity networks and a known melanoma- or cancer-relevant function. Following these criteria, we detected a highly significant up-regulation of ATM transcription in NMM, which was also mirrored by ATM protein up-regulation. In contrast to the other markers, ATM particularly might serve as a suitable diagnostic and reliable discriminator of DPN/NMM because ATM immunoreactivity also showed a reliable staining consistency within all samples of both entities.

Annotation-based distance measures for patient subgroup discovery in clinical microarray studies

Abstract: Motivation: Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering, however, can also stratify patients by similarity of their gene expression profiles, thereby defining novel disease entities based on molecular characteristics. Several distance-based cluster algorithms have been suggested, but little attention has been given to the distance measure between patients. Even with the Euclidean metric, including and excluding genes from the analysis leads to different distances between the same objects, and consequently different clustering results. Results: We describe a new clustering algorithm, in which gene selection is used to derive biologically meaningful clusterings of samples by combining expression profiles and functional annotation data. According to gene annotations, candidate gene sets with specific functional characterizations are generated. Each set defines a different distance measure between patients, leading to different clusterings. These clusterings are filtered using a resampling-based significance measure. Significant clusterings are reported together with the underlying gene sets and their functional definition. Conclusions: Our method reports clusterings defined by biologically focused sets of genes. In annotation-driven clusterings, we have recovered clinically relevant patient subgroups through biologically plausible sets of genes as well as new subgroupings. We conjecture that our method has the potential to reveal so far unknown, clinically relevant classes of patients in an unsupervised manner. Availability: We provide the R package adSplit as part of Bioconductor release 1.9 and on http://compdiag.molgen.mpg.de/software Contact: claudio.lottaz@molgen.mpg.de

Compensating for unknown confounders in microarray data analysis using filtered permutations.

Abstract: Permutation of class labels is a common approach in microarray analysis. It is assumed to produce random score distributions, which are not affected by biological differences between samples. However, hidden confounding variables like the genetic background of patients or undetected experimental artifacts leave traces in the expression data contaminating the score distributions obtained from random permutations. While the effects of known confounders can be compensated using established methodology, little is known on how to deal with unknown confounders. We discuss a computational method called permutation filtering, which aims to borrow information across genes to detect and compensate the effects of unknown confounders.