Bi-level and Bi-objective p-Median Type Problems for Integrative Clustering: Application to Analysis of Cancer Gene-Expression and Drug-Response Data

2018 ◽  
Vol 15 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Anton V. Ushakov ◽  
Xenia Klimentova ◽  
Igor Vasilyev
Keyword(s):  
Gene Expression ◽  
Drug Response ◽  
Cancer Gene ◽  
Response Data

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

2008 ◽  
Vol 26 (5) ◽  
pp. 531-539 ◽  
Author(s):  
Zoltán Kutalik ◽  
Jacques S Beckmann ◽  
Sven Bergmann
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Drug Response ◽  
Integrative Analysis ◽  
Modular Approach ◽  
Response Data

A genomic strategy to combinatorial therapeutics in solid tumors

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 2031-2031
Author(s):  
A. Potti ◽  
H. K. Dressman ◽  
A. Bild ◽  
R. Riedel ◽  
M. Kelley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines ◽  
Pi3 Kinase ◽  
Expression Data ◽  
Chemotherapy Sensitivity ◽  
Response Data ◽  
Pathway Deregulation

2031 Background: For most advanced solid tumors, the response rate to cytotoxic drugs is generally low, highlighting the importance of identifying those patients most likely to respond, either to single agents or combinations of cytotoxic or targeted therapies. Methods: We have made use of in vitro drug response data generated on the NCI-60 panel of cancer cell lines, coupled with Affymetrix U133 2.0 plus gene expression data, to develop genomic predictors of chemotherapy sensitivity. These models were then validated in independent cancer cell lines as well as response data from patient treatment studies. Results: Predictive models making use of gene expression data were developed for docetaxel, adriamycin, 5-flourouracil, cyclophosphamide, paclitaxel, and topotecan. These models were shown to accurately predict sensitivity to the drugs in an independent set (n = 30) of cancer cell lines. Importantly, three of the predictors (docetaxel, topotecan, paclitaxel) also accurately (> 80%) predicted response in patient studies. When evaluated in a large collection of human cancers (n = 381), these gene expression signatures of drug response identified patterns of predicted sensitivity suggesting potential opportunities for novel combinations. We also combined the predictions of chemotherapy sensitivity with predictions of pathway deregulation (Bild A, Nature 2005), to develop further opportunities for combination therapy. For instance, this analysis revealed a significant relationship between PI3 kinase pathway deregulation and docetaxel resistance (p = 0.001), and a correlation between docetaxel sensitivity and the activation of the Rb/E2F pathway (p = 0.009). Furthermore, cell lines showing an increased probability of PI3 kinase and Rb/E2F activation were also more likely to respond to a PI3 kinase (LY-294002) inhibitor (p = 0.01) or R-Roscovitine (p = 0.03), a cell cycle inhibitor, respectively. Conclusions: The development and validation of chemotherapeutic response predictors, together with oncogenic pathway signatures that can guide the use of targeted agents, provides an opportunity to develop effective combinatorial therapeutic strategies geared to the individual patient. No significant financial relationships to disclose.

scholarly journals MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
He-Gang Chen ◽  
Xiong-Hui Zhou
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Drug Response ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Recent Decade ◽  
Data Set ◽  
Module Network ◽  
The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Mammary Fat of Breast Cancer: Gene Expression Profiling and Functional Characterization

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109742 ◽  
Author(s):  
Fengliang Wang ◽  
Sheng Gao ◽  
Fei Chen ◽  
Ziyi Fu ◽  
Hong Yin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Functional Characterization ◽  
Cancer Gene ◽  
Breast Cancer Gene
Sign in / Sign up

Export Citation Format

Share Document