scholarly journals Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

2018 ◽  
Vol 11 (531) ◽  
pp. eaaq1087 ◽  
Author(s):  
Mark Grimes ◽  
Benjamin Hall ◽  
Lauren Foltz ◽  
Tyler Levy ◽  
Klarisa Rikova ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Protein Phosphorylation ◽  
Signaling Networks ◽  
Data Sets ◽  
Methylation Data ◽  
Cancer Signaling

scholarly journals Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tien-Dzung Tran ◽  
Duc-Tinh Pham
Keyword(s):  
Anticancer Drug ◽  
Drug Target ◽  
Target Genes ◽  
Signaling Network ◽  
Competitive Dynamics ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Dynamics Model ◽  
Cancer Signaling ◽  
The Impact

AbstractEach cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

A consistent two-mutation model of lung cancer for different data sets of radon-exposed rats

2001 ◽  
Vol 40 (4) ◽  
pp. 269-277 ◽  
Author(s):  
H. Bijwaard ◽  
M. J. P. Brugmans ◽  
H. P. Leenhouts
Keyword(s):  
Lung Cancer ◽  
Data Sets ◽  
Mutation Model

scholarly journals Investigation of the international comparability of population-based routine hospital data set derived comorbidity scores for patients with lung cancer

Thorax ◽  
2017 ◽  
Vol 73 (4) ◽  
pp. 339-349 ◽  
Author(s):  
Margreet Lüchtenborg ◽  
Eva J A Morris ◽  
Daniela Tataru ◽  
Victoria H Coupland ◽  
Andrew Smith ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Survival ◽  
Population Based ◽  
Data Sets ◽  
Hospital Data ◽  
Data Set ◽  
Discharge Data ◽  
International Differences ◽  
Cancer Data ◽  
Comorbidity Scores

IntroductionThe International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome.MethodsLinked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons.ResultsIt was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable.ConclusionThe results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required.

scholarly journals Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders

2019 ◽  
Vol 20 (S18) ◽  
Author(s):  
Zhenxing Wang ◽  
Yadong Wang
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Deep Learning ◽  
Malignant Tumors ◽  
Methylation Data ◽  
Cancer Epigenetics ◽  
Latent Space ◽  
Unsupervised Deep Learning ◽  
Latent Representations ◽  
Model Training

Abstract Background Lung cancer is one of the most malignant tumors, causing over 1,000,000 deaths each year worldwide. Deep learning has brought success in many domains in recent years. DNA methylation, an epigenetic factor, is used for model training in many studies. There is an opportunity for deep learning methods to analyze the lung cancer epigenetic data to determine their subtypes for appropriate treatment. Results Here, we employ variational autoencoders (VAEs), an unsupervised deep learning framework, on 450K DNA methylation data of TCGA-LUAD and TCGA-LUSC to learn latent representations of the DNA methylation landscape. We extract a biologically relevant latent space of LUAD and LUSC samples. It is showed that the bivariate classifiers on the further compressed latent features could classify the subtypes accurately. Through clustering of methylation-based latent space features, we demonstrate that the VAEs can capture differential methylation patterns about subtypes of lung cancer. Conclusions VAEs can distinguish the original subtypes from manually mixed methylation data frame with the encoded features of latent space. Further applications about VAEs should focus on fine-grained subtypes identification for precision medicine.

Analysis of cancer signaling networks by systems biology to develop therapies

2011 ◽  
Vol 21 (3) ◽  
pp. 200-206 ◽  
Author(s):  
Rexxi D. Prasasya ◽  
Dan Tian ◽  
Pamela K. Kreeger
Keyword(s):  
Systems Biology ◽  
Signaling Networks ◽  
Cancer Signaling

scholarly journals Aldo-keto reductases are biomarkers of NRF2 activity and are co-ordinately overexpressed in non-small cell lung cancer

2016 ◽  
Vol 115 (12) ◽  
pp. 1530-1539 ◽  
Author(s):  
A Kenneth MacLeod ◽  
Lourdes Acosta-Jimenez ◽  
Philip J Coates ◽  
Michael McMahon ◽  
Frank A Carey ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Lines ◽  
Mutant Cell ◽  
Immunohistochemical Analysis ◽  
Lung Tumour ◽  
Data Sets ◽  
Related Factor ◽  
Nrf2 Pathway ◽  
Mutational Status ◽  
Somatic Alterations

Abstract Background: Although the nuclear factor-erythroid 2-related factor 2 (NRF2) pathway is one of the most frequently dysregulated in cancer, it is not clear whether mutational status is a good predictor of NRF2 activity. Here we utilise four members of the aldo-keto reductase (AKR) superfamily as biomarkers to address this question. Methods: Twenty-three cell lines of diverse origin and NRF2-pathway mutational status were used to determine the relationship between AKR expression and NRF2 activity. AKR expression was evaluated in lung cancer biopsies and Cancer Genome Atlas (TCGA) and Oncomine data sets. Results: AKRs were expressed at a high basal level in cell lines carrying mutations in the NRF2 pathway. In non-mutant cell lines, co-ordinate induction of AKRs was consistently observed following activation of NRF2. Immunohistochemical analysis of lung tumour biopsies and interrogation of TCGA data revealed that AKRs are enriched in both squamous cell carcinomas (SCCs) and adenocarcinomas that contain somatic alterations in the NRF2 pathway but, in the case of SCC, AKRs were also enriched in most other tumours. Conclusions: An AKR biomarker panel can be used to determine NRF2 status in tumours. Hyperactivation of the NRF2 pathway is far more prevalent in lung SCC than previously predicted by genomic analyses.

scholarly journals Are methylation beta-values simplex distributed?

2019 ◽  
Author(s):  
Lara Nonell ◽  
Juan R González
Keyword(s):  
Dna Methylation ◽  
Sample Size ◽  
Microarray Data ◽  
Regression Models ◽  
Real Data ◽  
Small Sample ◽  
Beta Regression ◽  
Data Sets ◽  
Methylation Data ◽  
Simplex Distribution

AbstractDNA methylation plays an important role in the development and progression of disease. Beta-values are the standard methylation measures. Different statistical methods have been proposed to assess differences in methylation between conditions. However, most of them do not completely account for the distribution of beta-values. The simplex distribution can accommodate beta-values data. We hypothesize that simplex is a quite flexible distribution which is able to model methylation data.To test our hypothesis, we conducted several analyses using four real data sets obtained from microarrays and sequencing technologies. Standard data distributions were studied and modelled in comparison to the simplex. Besides, some simulations were conducted in different scenarios encompassing several distribution assumptions, regression models and sample sizes. Finally, we compared DNA methylation between females and males in order to benchmark the assessed methodologies under different scenarios.According to the results obtained by the simulations and real data analyses, DNA methylation data are concordant with the simplex distribution in many situations. Simplex regression models work well in small sample size data sets. However, when sample size increases, other models such as the beta regression or even the linear regression can be employed to assess group comparisons and obtain unbiased results. Based on these results, we can provide some practical recommendations when analyzing methylation data: 1) use data sets of at least 10 samples per studied condition for microarray data sets or 30 in NGS data sets, 2) apply a simplex or beta regression model for microarray data, 3) apply a linear model in any other case.

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