An Integrated Approach to Uncover Drivers of Cancer

Akavia, Uri David; Litvin, Oren; Kim, Jessica; Sanchez-Garcia, Félix; Kotliar, Dylan; Causton, Helen C.; Pochanard, Panisa; Mozes, Eyal; Garraway, Levi A.; Pe’er, Dana

doi:10.1016/j.cell.2010.11.013

Cited by 464 publications

(500 citation statements)

References 121 publications

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“…These genes were selected using the CONEXIC algorithm, which combines CNAs and gene expression data and constructs regulatory networks, based on the driver genes that appear (19). Also, the algorithm uses a Bayesian function to detect modulator candidates (drivers) among the regions with amplifications and deletions.…”

Section: Integrative Analysismentioning

confidence: 99%

“…The study was based on the frequency of CNAs to further compare with GWE. This method has several limitations: the size of the region might contain a large number of genes, and this approach alone fails to determine the actual influence of CNA on changes in gene expression (19). Micci and colleagues selected 2 genes, based on recurrent losses on 3p and 9p by aCGH (FHIT and PTPRD), and 5 genes (MAL, KRT4, OLFM4, SPRR2G, and S100A7A), based on expression array alterations.…”

Section: Introductionmentioning

confidence: 99%

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An Integrative Approach Uncovers Biomarkers that Associate with Clinically Relevant Disease Outcomes in Vulvar Carcinoma

Lavorato-Rocha

Akagi

Maia

et al. 2016

Molecular Cancer Research

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Vulvar squamous cell carcinoma (VSCC) is a rare disease that has a high mortality rate ($40%). However, little is known about its molecular signature. Therefore, an integrated genomics approach, based on comparative genome hybridization (aCGH) and genome-wide expression (GWE) array, was performed to identify driver genes in VSCC. To achieve that, DNA and RNA were extracted from frozen VSCC clinical specimens and examined by aCGH and GWE array, respectively. On the basis of the integration of data using the CONEXIC algorithm, PLXDC2 and GNB3 were validated by RT-qPCR. The expression of these genes was then analyzed by IHC in a large set of formalin-fixed paraffin-embedded specimens. These analyses identified 47 putative drivers, 46 of which were characterized by copy number gains that were concomitant with overexpression and one with a copy number loss and downregulation. Two of these genes, PLXDC2 and GNB3, were selected for further validation: PLXDC2 was downregulated and GNB3 was overexpressed compared with non-neoplastic tissue. By IHC, both proteins were ubiquitously expressed throughout vulvar tissue. High expression of GNB3 and low PLXDC2 immunostaining in the same sample was significantly associated with less lymph node metastasis and greater disease-free survival. On the basis of a robust methodology never used before for VSCC evaluation, two novel prognostic markers in vulvar cancer are identified: one with favorable prognosis (GNB3) and the other with unfavorable prognosis (PLXDC2).Implications: This genomics study reveals markers that associate with prognosis and may provide guidance for better treatment in vulvar cancer.

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Section: Integrative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Integrative Approach Uncovers Biomarkers that Associate with Clinically Relevant Disease Outcomes in Vulvar Carcinoma

Lavorato-Rocha

Akagi

Maia

et al. 2016

Molecular Cancer Research

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“…An example is the IL28B gene implicated in liver cancer [15,16]. More importantly, the driver mutations of genes have been identified in a genome-wide association study [17] using next-generation sequencing technology [18]. Genes inducing pathogenic physical changes are usually drivers rather than passengers of disease consequents [19].…”

Section: Node Biomarkers For Classification and Prediction Without Nementioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

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“…Indeed, reconstruction and analysis of gene regulatory networks has proven to be an excellent tool for causal inference, allowing researchers to uncover gene-drivers of carcinogenesis of different tumors. [46][47][48] Moreover, these networks can be built from different types of variables using a variety of available tools 49 as long as all parameters are all measured in the same sample.…”

Section: Microbiota Characterizationmentioning

confidence: 99%

Investigating a holobiont: Microbiota perturbations and transkingdom networks

et al. 2016

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The scientific community has recently come to appreciate that, rather than existing as independent organisms, multicellular hosts and their microbiota comprise a complex evolving superorganism or metaorganism, termed a holobiont. This point of view leads to a re-evaluation of our understanding of different physiological processes and diseases. In this paper we focus on experimental and computational approaches which, when combined in one study, allowed us to dissect mechanisms (traditionally named host-microbiota interactions) regulating holobiont physiology. Specifically, we discuss several approaches for microbiota perturbation, such as use of antibiotics and germ-free animals, including advantages and potential caveats of their usage. We briefly review computational approaches to characterize the microbiota and, more importantly, methods to infer specific components of microbiota (such as microbes or their genes) affecting host functions. One such approach called transkingdom network analysis has been recently developed and applied in our study. 1 Finally, we also discuss common methods used to validate the computational predictions of host-microbiota interactions using in vitro and in vivo experimental systems.

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An Integrated Approach to Uncover Drivers of Cancer

Cited by 464 publications

References 121 publications

An Integrative Approach Uncovers Biomarkers that Associate with Clinically Relevant Disease Outcomes in Vulvar Carcinoma

An Integrative Approach Uncovers Biomarkers that Associate with Clinically Relevant Disease Outcomes in Vulvar Carcinoma

Edge biomarkers for classification and prediction of phenotypes

Investigating a holobiont: Microbiota perturbations and transkingdom networks

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