scholarly journals A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Erkhembayar Jadamba ◽  
Miyoung Shin
Keyword(s):  
Breast Cancer ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
A Priori ◽  
Gene Expression Profiles ◽  
Biological Data ◽  
Genomic Knowledge ◽  
High Level ◽  
Systematic Framework ◽  
Phenotype Expression

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

scholarly journals A Supervised Network Analysis on Gene Expression Profiles of Breast Tumors Predicts a 41-Gene Prognostic Signature of the Transcription FactorMYBacross Molecular Subtypes

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Li-Yu D. Liu ◽  
Li-Yun Chang ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
King-Jen Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Essential Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prognostic Signature ◽  
Prognostic Features ◽  
Bioinformatic Tools ◽  
Prognostic Feature ◽  
Relevant Gene

Background. MYBis predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features ofMYBvia a supervised network analysis.Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson’s correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included.Results. ARNT2is predicted to be the essential gene partner ofMYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature ofMYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members ofMYBare elevated to promote poor prognosis when both levels ofMYBandARNT2decline. BothMYBL1andMYBL2may partially decrease the tumor suppressive activities that are predicted to be up-regulated byMYBandARNT2.Conclusions. The major prognostic feature ofMYBis predicted to be determined by theMYBsubnetwork (41 probes) that is relevant across subtypes.

Epigenetic Reprogramming and Landscape of Transcriptomic Interactions: Impending Therapeutic Interference of Triple-Negative Breast Cancer in Molecular Medicine

2021 ◽  
Vol 21 ◽  
Author(s):  
Suman Kumar Ray ◽  
Sukhes Mukherjee
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Treatment Decision ◽  
Copy Number Variations ◽  
Molecular Medicine ◽  
Breast Cancers ◽  
Breast Carcinogenesis

: The mechanisms governing the development and progression of cancers are believed to be the consequence of hereditary deformities and epigenetic modifications. Accordingly, epigenetics has become an incredible and progressively explored field of research to discover better prevention and therapy for neoplasia, especially triple-negative breast cancer (TNBC). It represents 15–20% of all invasive breast cancers and will, in general, have bellicose histological highlights and poor clinical outcomes. In the early phases of triple-negative breast carcinogenesis, epigenetic deregulation modifies chromatin structure and influences the plasticity of cells. It up-keeps the oncogenic reprogramming of malignant progenitor cells with the acquisition of unrestrained selfrenewal capacities. Genomic impulsiveness in TNBC prompts mutations, copy number variations, as well as genetic rearrangements, while epigenetic remodeling includes an amendment by DNA methylation, histone modification, and noncoding RNAs of gene expression profiles. It is currently evident that epigenetic mechanisms assume a significant part in the pathogenesis, maintenance, and therapeutic resistance of TNBC. Although TNBC is a heterogeneous malaise that is perplexing to describe and treat, the ongoing explosion of genetic and epigenetic research will help to expand these endeavors. Latest developments in transcriptome analysis have reformed our understanding of human diseases, including TNBC at the molecular medicine level. It is appealing to envision transcriptomic biomarkers to comprehend tumor behavior more readily regarding its cellular microenvironment. Understanding these essential biomarkers and molecular changes will propel our capability to treat TNBC adequately. This review will depict the different aspects of epigenetics and the landscape of transcriptomics in triple-negative breast carcinogenesis and their impending application for diagnosis, prognosis, and treatment decision with the view of molecular medicine.

Gene expression profiles in premenopausal women with HR+ HER2− early breast cancer

2020 ◽  
Vol 138 ◽  
pp. S76
Author(s):  
H. Ni ◽  
A. Kurt ◽  
J. Kumbrink ◽  
A. Seiler ◽  
D. Mayr ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Early Breast Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Premenopausal Women

scholarly journals Deciphering the Correlation between Breast Tumor Samples and Cell Lines by Integrating Copy Number Changes and Gene Expression Profiles

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yi Sun ◽  
Qi Liu
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Breast Tumors ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Tumor Group

Breast cancer is one of the most common cancers with high incident rate and high mortality rate worldwide. Although different breast cancer cell lines were widely used in laboratory investigations, accumulated evidences have indicated that genomic differences exist between cancer cell lines and tissue samples in the past decades. The abundant molecular profiles of cancer cell lines and tumor samples deposited in the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas now allow a systematical comparison of the breast cancer cell lines with breast tumors. We depicted the genomic characteristics of breast primary tumors based on the copy number variation and gene expression profiles and the breast cancer cell lines were compared to different subgroups of breast tumors. We identified that some of the breast cancer cell lines show high correlation with the tumor group that agrees with previous knowledge, while a big part of them do not, including the most used MCF7, MDA-MB-231, and T-47D. We presented a computational framework to identify cell lines that mostly resemble a certain tumor group for the breast tumor study. Our investigation presents a useful guide to bridge the gap between cell lines and tumors and helps to select the most suitable cell line models for personalized cancer studies.

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