Dataset Information

SWAN pathway-network identification of common aneuploidy-based oncogenic drivers.

ABSTRACT: Haploinsufficiency drives Darwinian evolution. Siblings, while alike in many aspects, differ due to monoallelic differences inherited from each parent. In cancer, solid tumors exhibit aneuploid genetics resulting in hundreds to thousands of monoallelic gene-level copy-number alterations (CNAs) in each tumor. Aneuploidy patterns are heterogeneous, posing a challenge to identify drivers in this high-noise genetic environment. Here, we developed Shifted Weighted Annotation Network (SWAN) analysis to assess biology impacted by cumulative monoallelic changes. SWAN enables an integrated pathway-network analysis of CNAs, RNA expression, and mutations via a simple web platform. SWAN is optimized to best prioritize known and novel tumor suppressors and oncogenes, thereby identifying drivers and potential druggable vulnerabilities within cancer CNAs. Protein homeostasis, phospholipid dephosphorylation, and ion transport pathways are commonly suppressed. An atlas of CNA pathways altered in each cancer type is released. These CNA network shifts highlight new, attractive targets to exploit in solid tumors.

SUBMITTER: Bowers RR

PROVIDER: S-EPMC9023287 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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SWAN pathway-network identification of common aneuploidy-based oncogenic drivers.

Bowers Robert R RR Jones Christian M CM Paz Edwin A EA Barrows John K JK Armeson Kent E KE Long David T DT Delaney Joe R JR

Nucleic acids research 20220401 7

Haploinsufficiency drives Darwinian evolution. Siblings, while alike in many aspects, differ due to monoallelic differences inherited from each parent. In cancer, solid tumors exhibit aneuploid genetics resulting in hundreds to thousands of monoallelic gene-level copy-number alterations (CNAs) in each tumor. Aneuploidy patterns are heterogeneous, posing a challenge to identify drivers in this high-noise genetic environment. Here, we developed Shifted Weighted Annotation Network (SWAN) analysis t ...[more]

PMID: 35380699

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Project description:"A part of current research has intensively been focused on the proliferation and metabolic processes governing biological systems. Since the advent of high throughput methodologies like microarrays, the load of genomic data has increased geometrically and along with that the need for computational methods which will interpret these data. In the present work we study in vitro the common proliferation and metabolic processes, which are combined to the common oncogenic pathways, as far as gene expression is concerned, between the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. We present a computational approach, using cDNA microarrays, in order to identify commonalities between diverse biological systems. Our analysis predicted that JAK1, STAT1, PIAS2 and CDK4 are the driving forces in the two cell lines. This type of analysis can lead to the understanding of the common mechanisms that transform physiological cells to malignant, as well as it reveals a new holistic way to understandthe the dynamics of tumor onset as well as the mechanistics of oncogenic drivers. The present work is concerned with the common expressional profile of two cell lines: the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. Our investigation was focused on the identification of genes that share a common expression profile between the two cell lines. Both cell lines are characterized by the fact that their differentiation has stopped at an early stage, before they mature to their final cell type. Normally, these cells would have matured and progressed into differentiated cells, constituting blood and muscle cells, respectively. At some unknown point, normal differentiation ceased for these cells and they became malignant. From that point on, to the first manifestation of symptoms of malignancy, there is a lack of knowledge regarding the mechanisms underlying oncogenesis. From these observations, the question whether two distinct cell types destined to fulfill different functions, manifest similar mechanisms of growth and progression due to their malignant character, arises. The present study was focused on the identification of the differential expression profiles underlying the two cell lines. A previous report studied the expression profile of seven ARMS cell lines possessing the PAX3-FKHR fusion gene, along with other cell lines of different tumor types (22). To our knowledge, this is the first time that a comparison between two totally different types of neoplasia, such as the CCRF-CEM and TE-671 cell lines, is attempted. These mechanisms are examined with purpose to identify common drivers that lead to the progression of tumor cells. We hereby propose a new computational approach for the investigation of common oncogenic drivers."

Project description:A part of current research has intensively been focused on the proliferation and metabolic processes governing biological systems. Since the advent of high throughput methodologies like microarrays, the load of genomic data has increased geometrically and along with that the need for computational methods which will interpret these data. In the present work we study in vitro the common proliferation and metabolic processes, which are combined to the common oncogenic pathways, as far as gene expression is concerned, between the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. We present a computational approach, using cDNA microarrays, in order to identify commonalities between diverse biological systems. Our analysis predicted that JAK1, STAT1, PIAS2 and CDK4 are the driving forces in the two cell lines. This type of analysis can lead to the understanding of the common mechanisms that transform physiological cells to malignant, as well as it reveals a new holistic way to understandthe the dynamics of tumor onset as well as the mechanistics of oncogenic drivers. The present work is concerned with the common expressional profile of two cell lines: the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. Our investigation was focused on the identification of genes that share a common expression profile between the two cell lines. Both cell lines are characterized by the fact that their differentiation has stopped at an early stage, before they mature to their final cell type. Normally, these cells would have matured and progressed into differentiated cells, constituting blood and muscle cells, respectively. At some unknown point, normal differentiation ceased for these cells and they became malignant. From that point on, to the first manifestation of symptoms of malignancy, there is a lack of knowledge regarding the mechanisms underlying oncogenesis. From these observations, the question whether two distinct cell types destined to fulfill different functions, manifest similar mechanisms of growth and progression due to their malignant character, arises. The present study was focused on the identification of the differential expression profiles underlying the two cell lines. A previous report studied the expression profile of seven ARMS cell lines possessing the PAX3-FKHR fusion gene, along with other cell lines of different tumor types (22). To our knowledge, this is the first time that a comparison between two totally different types of neoplasia, such as the CCRF-CEM and TE-671 cell lines, is attempted. These mechanisms are examined with purpose to identify common drivers that lead to the progression of tumor cells. We hereby propose a new computational approach for the investigation of common oncogenic drivers.

Project description:BackgroundCleft lip (CL) is one of the most common congenital birth defects with complex etiology. While genome-wide association studies (GWAS) have made significant advances in our understanding of mutations and their related genes with potential involvement in the etiology of CL, it remains unknown how these genes are functionally regulated and interact with each other in lip development. Currently, identifying the disease-causing genes in human CL is urgently needed. So far, the causative CL genes have been largely undiscovered, making it challenging to design experiments to validate the functional influence of the mutations identified from large genomic studies such as CL GWAS.ResultsTranscription factors (TFs) and microRNAs (miRNAs) are two important regulators in cellular system. In this study, we aimed to investigate the genetic interactions among TFs, miRNAs and the CL genes curated from the previous studies. We constructed miRNA-TF co-regulatory networks, from which the critical regulators as putative drivers in CL were examined. Based on the constructed networks, we identified ten critical hub genes with prior evidence in CL. Furthermore, the analysis of partitioned regulatory modules highlighted a number of biological processes involved in the pathology of CL, including a novel pathway "Signaling pathway regulating pluripotency of stem cells". Our subnetwork analysis pinpointed two candidate miRNAs, hsa-mir-27b and hsa-mir-497, activating the Wnt pathway that was associated with CL. Our results were supported by an independent gene expression dataset in CL.ConclusionsThis study represents the first regulatory network analysis of CL genes. Our work presents a global view of the CL regulatory network and a novel approach on investigating critical miRNAs, TFs and genes via combinatory regulatory networks in craniofacial development. The top genes and miRNAs will be important candidates for future experimental validation of their functions in CL.

Dataset Information

SWAN pathway-network identification of common aneuploidy-based oncogenic drivers.

Publications

SWAN pathway-network identification of common aneuploidy-based oncogenic drivers.

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