Zielgene der RAS-Onkoprotein-abhängigen Signaltransduktion

Die Entstehung und Progression maligner Tumoren ist ein mehrstufiger Prozeß, der auf einer Vielzahl genetischer Alterationen beruht. Essentielle Schritte sind die Aktivierung von Proto-Onkogenen und die Inaktivierung von Tumor-Suppressorgenen. Infolge dessen können die Zellen unabhängig von externen Wachstumssignalen ungebremst proliferieren, die Apoptose wird gehemmt, die Angiogenese wird aktiviert, und es kommt schließlich zur Metastasierung. Zu den bekanntesten Proto-Onkogenen, die in humanen Tumoren aktiviert werden, gehören die RAS Gene. Sie sind in einer Vielzahl von Tumoren mutiert und führen zu einer Stimulation der Proliferation. Um den Einfluß aktivierter RAS Onkogene auf die Regulation der Genexpression zu untersuchen wurden Genexpressionsprofile in Zellkultur-Modellen und humanen Tumoren erstellt. In einem Fibroblasten- und einem Epithelzell-basierten System konnten mehrere hundert, RAS-abhängig differenziell exprimierte Genen identifiziert werden. Aufgrund der bekannten Funktionen ihrer Genprodukte spielen sie eine wichtige Rolle im Verlust der Zellzyklus-Kontrolle, der Kontrolle der Signalübertragung, in der Angiogenese-Induktion sowie in der Invasion und damit Metastasierung. Die Zusammenhänge zwischen der Aktivierung bestimmter Signalkaskaden wie z.B. Raf-Mek-Erk oder PI-3K und der Expression von definierten Genmustern wurden hergestellt. Weiterhin konnte mit Hilfe von Microarray Analysen eine Vielzahl potentieller Tumormarker und Zielgene für therapeutische Intervention im Ovarialkarzinom identifiziert werden. Die Rolle der KlasseII Tumorsuppressor Gene Caveolin-1 und H-REV107-1 in humanen Ovarialkarzinomen wurde detailliert untersucht und ihre Rolle in der Regulation des Zellüberlebens nachgewiesen. Caveolin-1, ein negativer Regulator der RAS-abhängigen Signalübertragung, wird in über 80% der untersuchten humanen Ovarialkarzinome gehemmt. Hierbei spielen epigenetische Mechanismen eine Rolle, die jedoch nicht Caveolin-1 selbst, sondern einen unbekannten Regulator des Caveolin-1 Gens betreffen. Das H-REV107-1 Gen, ein Wachstumsregulator mit unbekannter Funktion wird in ca. 50% der untersuchten Ovarialkarzinome nicht mehr exprimiert. Ähnlich wie bei Caveolin-1, führt eine gezielte Expression des Gens in Tumorzellen zur Apoptose. Die Suche nach Interaktionspartnern des H-REV107-1 Gens führte zur Identifizierung der ubiquitär exprimierten Phosphatase2A (PP2A). Die Bindung zwischen H-REV107-1 und PP2A wurde weiter charakterisiert und ihre Rolle in der H-REV107-1 vermittelten Apoptose analysiert.Development and progression of human tumours is a multistep process depending on numerous genetic alterations. Essentiell steps herein are the mutational activation of oncogenes and the inactivation of tumour suppressor genes. As a result of these alterations, the cells acquire the potential of unlimited growth independent of external growth factor signals, apoptosis is diminished, angiogenesis is stimulated and finally metastasis can occur. Among the best known proto-oncogenes, mutated in a number of human tumours, are the RAS genes. To investigate the role of RAS oncogenes in transformation-related transcriptional alterations, expressionsprofiling was performed from cell culture models and human tumours. Several hundred genes were identified to be de-regulated in a RAS-dependent manner in a fibroblast and an epithelial cell-based model. The protein products encoded by these genes play important roles in the loss of cell cycle control, control of signal transduction, angiogenesis induction as well as invasion and metastasis. Groups of de-regulated genes could be assigned to distinct signaling pathways such as the Raf-Mek-Erk or the PI-3 kinase dependent pathways. In addition, a number of potential tumour markers and potential target structures for therapeutic intervention were identified in ovarian carcinomas with the help of microarray studies. The role of the class II tumor suppressor genes Caveolin-1 and H-REV107-1 in human ovarian carcinomas was further investigated and their role in the regulation of cell survival was demonstrated. Caveolin-1, a negative regulator of RAS-dependent signal transduction, is supressed in more than 80% of the ovarian carcinomas analysed. This suppression is mediated by epigenetic mechanisms which due not target Caveolin-1 itself but an unknown regulator of the Caveolin-1 gene. The H-Rev107-1 gene, a growth regulator with unknown function, is no longer expressed in nearly 50% of the ovarian carcinomas analysed. Similar to Caveolin-1, also re-expression of H-REV107-1 results in apoptosis in the tumour cells. The search for proteins interacting with H-REV107-1 led to the identification of the ubiquitously expressed phosphatase 2A (PP2A). The interaction between H-REV107-1 and PP2A was further characterised and its role in the H-REV107-1 mediated apoptosis investigated

Unraveling the regulation of mTORC2 using logical modeling

Background The mammalian target of rapamycin (mTOR) is a regulator of cell proliferation, cell growth and apoptosis working through two distinct complexes: mTORC1 and mTORC2. Although much is known about the activation and inactivation of mTORC1, the processes controlling mTORC2 remain poorly characterized. Experimental and modeling studies have attempted to explain the regulation of mTORC2 but have yielded several conflicting hypotheses. More specifically, the Phosphoinositide 3-kinase (PI3K) pathway was shown to be involved in this process, but the identity of the kinase interacting with and regulating mTORC2 remains to be determined (Cybulski and Hall, Trends Biochem Sci 34:620-7, 2009). Method We performed a literature search and identified 5 published hypotheses describing mTORC2 regulation. Based on these hypotheses, we built logical models, not only for each single hypothesis but also for all combinations and possible mechanisms among them. Based on data provided by the original studies, a systematic analysis of all models was performed. Results We were able to find models that account for experimental observations from every original study, but do not require all 5 hypotheses to be implemented. Surprisingly, all hypotheses were in agreement with all tested data gathered from the different studies and PI3K was identified as an essential regulator of mTORC2. Conclusion The results and additional data suggest that more than one regulator is necessary to explain the behavior of mTORC2. Finally, this study proposes a new experiment to validate mTORC1 as second essential regulator

Evaluating Uncertainty in Signaling Networks Using Logical Modeling

Systems biology studies the structure and dynamics of biological systems using mathematical approaches. Bottom-up approaches create models from prior knowledge but usually cannot cope with uncertainty, whereas top-down approaches infer models directly from data using statistical methods but mostly neglect valuable known information from former studies. Here, we want to present a workflow that includes prior knowledge while allowing for uncertainty in the modeling process. We build not one but all possible models that arise from the uncertainty using logical modeling and subsequently filter for those models in agreement with data in a top-down manner. This approach enables us to investigate new and more complex biological research questions, however, the encoding in such a framework is often not obvious and thus not easily accessible for researcher from life sciences. To mitigate this problem, we formulate a pipeline with specific templates to address some research questions common in signaling network analysis. To illustrate the potential of this approach, we applied the pipeline to growth factor signaling processes in two renal cancer cell lines. These two cell lines originate from similar tissue, but surprisingly showed a very different behavior toward the cancer drug Sorafenib. Thus our aim was to explore differences between these cell lines regarding three sources of uncertainty in one analysis: possible targets of Sorafenib, crosstalk between involved pathways, and the effect of a mutation in mammalian target of Rapamycin (mTOR) in one of the cell lines. We were able to show that the model pools from the cell lines are disjoint, thus the discrepancies in behavior originate from differences in the cellular wiring. Also the mutation in mTOR is not affecting its activity in the pathway. The results on Sorafenib, while not fully clarifying the mechanisms involved, illustrate the potential of this analysis for generating new hypotheses.Peer Reviewe

DNA copy number changes define spatial patterns of heterogeneity in colorectal cancer

Genetic heterogeneity between and within tumours is a major factor determining cancer progression and therapy response. Here we examined DNA sequence and DNA copy-number heterogeneity in colorectal cancer (CRC) by targeted high-depth sequencing of 100 most frequently altered genes. In 97 samples, with primary tumours and matched metastases from 27 patients, we observe inter-tumour concordance for coding mutations; in contrast, gene copy numbers are highly discordant between primary tumours and metastases as validated by fluorescent in situ hybridization. To further investigate intra-tumour heterogeneity, we dissected a single tumour into 68 spatially defined samples and sequenced them separately. We identify evenly distributed coding mutations in APC and TP53 in all tumour areas, yet highly variable gene copy numbers in numerous genes. 3D morpho-molecular reconstruction reveals two clusters with divergent copy number aberrations along the proximal–distal axis indicating that DNA copy number variations are a major source of tumour heterogeneity in CRC

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical–pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.Peer Reviewe

Evaluating Uncertainty in Signaling Networks Using Logical Modeling

Systems biology studies the structure and dynamics of biological systems using mathematical approaches. Bottom-up approaches create models from prior knowledge but usually cannot cope with uncertainty, whereas top-down approaches infer models directly from data using statistical methods but mostly neglect valuable known information from former studies. Here, we want to present a workflow that includes prior knowledge while allowing for uncertainty in the modeling process. We build not one but all possible models that arise from the uncertainty using logical modeling and subsequently filter for those models in agreement with data in a top-down manner. This approach enables us to investigate new and more complex biological research questions, however, the encoding in such a framework is often not obvious and thus not easily accessible for researcher from life sciences. To mitigate this problem, we formulate a pipeline with specific templates to address some research questions common in signaling network analysis. To illustrate the potential of this approach, we applied the pipeline to growth factor signaling processes in two renal cancer cell lines. These two cell lines originate from similar tissue, but surprisingly showed a very different behavior toward the cancer drug Sorafenib. Thus our aim was to explore differences between these cell lines regarding three sources of uncertainty in one analysis: possible targets of Sorafenib, crosstalk between involved pathways, and the effect of a mutation in mammalian target of Rapamycin (mTOR) in one of the cell lines. We were able to show that the model pools from the cell lines are disjoint, thus the discrepancies in behavior originate from differences in the cellular wiring. Also the mutation in mTOR is not affecting its activity in the pathway. The results on Sorafenib, while not fully clarifying the mechanisms involved, illustrate the potential of this analysis for generating new hypotheses

An Alu Element–Associated Hypermethylation Variant of the POMC Gene Is Associated with Childhood Obesity

The individual risk for common diseases not only depends on genetic but also on epigenetic polymorphisms. To assess the role of epigenetic variations in the individual risk for obesity, we have determined the methylation status of two CpG islands at the POMC locus in obese and normal-weight children. We found a hypermethylation variant targeting individual CpGs at the intron2–exon3 boundary of the POMC gene by bisulphite sequencing that was significantly associated with obesity. POMC exon3 hypermethylation interferes with binding of the transcription enhancer P300 and reduces expression of the POMC transcript. Since intron2 contains Alu elements that are known to influence methylation in their genomic vicinity, the exon3 methylation variant seems to result from an Alu element–triggered default state of methylation boundary definition. Exon3 hypermethylation in the POMC locus represents the first identified DNA methylation variant that is associated with the individual risk for obesity