| 1. |
- Morgan, Daniel, et al.
(författare)
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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
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| 2. |
- Weishaupt, Holger, 1983-, et al.
(författare)
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Graph Centrality Based Prediction of Cancer Genes
- 2016
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Ingår i: Engineering Mathematics II. - Cham : Springer. - 9783319421049 - 9783319421056 ; , s. 275-311
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Bokkapitel (refereegranskat)abstract
- Current cancer therapies including surgery, radiotherapy and chemotherapy are often plagued by high failure rates. Designing more targeted and personalized treatment strategies requires a detailed understanding of druggable tumor drivergenes. As a consequence, the detection of cancer driver genes has evolved to a critical scientific field integrating both high-through put experimental screens as well as computational and statistical strategies. Among such approaches, network based prediction tools have recently been accentuated and received major focus due to their potential to model various aspects of the role of cancer genes in a biological system. In this chapter, we focus on how graph centralities obtained from biological networks have been used to predict cancer genes. Specifically, we start by discussing the current problems in cancer therapy and the reasoning behind using network based cancer gene prediction, followed by an outline of biological networks, their generation and properties. Finally, we review major concepts, recent results as well as future challenges regarding the use of graph centralities in cancer gene prediction.
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| 3. |
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| 4. |
- Morgan, Daniel, et al.
(författare)
-
Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Motivation: Cancer is known to stem from multiple, independent mutations, the effects of which aggregate to drive the cell into a cancerous state. To understand the complex interplay between affected genes, their gene regulatory network (GRN) needs to be uncovered, to revealing detailed insights of regulatory mechanisms. We therefore decided to infer a reliable GRN from perturbation responses of 40 genes known or suspected to have a role in human cancers yet whose regulatory interactions are poorly known.Results: siRNA knockdown experiments of each gene were done in a human squamous carcinoma cell line, after which the transcriptomic response was measured. From these data GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. The best GRN was shown to be significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes but subjected to double perturbations. It agrees with many known links in addition to predicting a large number of novel interactions, a subset of which were experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.
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| 5. |
- Weishaupt, Holger, et al.
(författare)
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Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 35:18, s. 3357-3364
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential.Results: We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses.
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| 6. |
- Weishaupt, Holger, et al.
(författare)
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Comparing the landcapes of common retroviral insertion sites across tumor models
- 2017
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Ingår i: AIP Conference Proceedings, Volume 1798. - : American Institute of Physics (AIP). - 0094-243X. - 9780735414648 ; , s. 020173-1-020173-9
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Konferensbidrag (refereegranskat)abstract
- Retroviral tagging represents an important technique, which allows researchers to screen for candidate cancer genes. The technique is based on the integration of retroviral sequences into the genome of a host organism, which might then lead to the artificial inhibition or expression of proximal genetic elements. The identification of potential cancer genes in this framework involves the detection of genomic regions (common insertion sites; CIS) which contain a number of such viral integration sites that is greater than expected by chance. During the last two decades, a number of different methods have been discussed for the identification of such loci and the respective techniques have been applied to a variety of different retroviruses and/or tumor models. We have previously established a retrovirus driven brain tumor model and reported the CISs which were found based on a Monte Carlo statistics derived detection paradigm. In this study, we consider a recently proposed alternative graph theory based method for identifying CISs and compare the resulting CIS landscape in our brain tumor dataset to those obtained when using the Monte Carlo approach. Finally, we also employ the graph-based method to compare the CIS landscape in our brain tumor model with those of other published retroviral tumor models.
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| 7. |
- Weishaupt, Holger, et al.
(författare)
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Loss of conservation of graph centralities in reverse-engineered transcriptional regulatory networks
- 2015
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Ingår i: ASMDA 2015 Proceedings. - : ISAST: International Society for the Advancement of Science and Technology. - 9786185180058 ; , s. 1077-1091
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Konferensbidrag (refereegranskat)abstract
- Graph centralities are often used to prioritize disease genes in transcrip-tional regulatory networks. Studies on small networks of experimentally validatedinteractions emphasize the general validity of this approach and extensions of suchndings have recently also been proposed for networks inferred from expression data.However, due to the noise inherent to expression data, it is largely unknown howwell centralities are preserved in such networks. Specically, while previous stud-ies have evaluated the performance of inference methods on synthetic expression, ithas yet to be established how the choice of method can aect individual centralitiesin the network. Here we compare two centralities between reference networks andnetworks inferred from corresponding simulated expression data using a number ofrelated methods. The results indicate that there exists only a modest conservationof centrality measures for the used inference methods. In conclusion, caution shouldbe exercised when inspecting centralities in reverse-engineered networks and furtherwork will be required to establish the use of such networks for prioritizing genes.
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| 8. |
- Weishaupt, Holger, et al.
(författare)
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Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks
- 2017
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Ingår i: Methodology and Computing in Applied Probability. - : Springer. - 1387-5841 .- 1573-7713. ; 19:4, s. 1095-1105
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Tidskriftsartikel (refereegranskat)abstract
- Graph centralities are commonly used to identify and prioritize disease genes in transcriptional regulatory networks. Studies on small networks of experimentally validated protein-protein interactions underpin the general validity of this approach and extensions of such findings have recently been proposed for networks inferred from gene expression data. However, it is largely unknown how well gene centralities are preserved between the underlying biological interactions and the networks inferred from gene expression data. Specifically, while previous studies have evaluated the performance of inference methods on synthetic gene expression, it has not been established how the choice of inference method affects individual centralities in the network. Here, we compare two gene centrality measures between reference networks and networks inferred from corresponding simulated gene expression data, using a number of commonly used network inference methods. The results indicate that the centrality of genes is only moderately conserved for all of the inference methods used. In conclusion, caution should be exercised when inspecting centralities in reverse-engineered networks and further work will be required to establish the use of such networks for prioritizing disease genes.
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