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Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data
1National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Motivation:
The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. The emerging large-scale pharmacogenomic data provide us valuable opportunities to improve existing therapies or to guide early-phase clinical trials of compounds under development. However, how to identify the underlying combinatorial patterns among pharmacogenomics data are still a challenging issue.
Results:
In this study, we adopted a sparse network-regularized partial least square (SNPLS) method to identify joint modular patterns using large-scale pairwise gene-expression and drug-response data. We incorporated a molecular network to the (sparse) partial least square model to improve the module accuracy via a network-based penalty. We first demonstrated the effectiveness of SNPLS using a set of simulation data and compared it with two typical methods. Further, we applied it to gene expression profiles for 13 321 genes and pharmacological profiles for 98 anticancer drugs across 641 cancer cell lines consisting of diverse types of human cancers. We identified 20 gene-drug co-modules, each of which consists of 30 cell lines, 137 genes and 2 drugs on average. The majority of identified co-modules have significantly functional implications and coordinated gene-drug associations. The modular analysis here provided us new insights into the molecular mechanisms of how drugs act and suggested new drug targets for therapy of certain types of cancers.
Availability And Implementation:
A matlab package of SNPLS is available at http://page.amss.ac.cn/shihua.zhang/
Contact:
: zsh@amss.ac.cn
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces a novel sparse network-regularized partial least square (SNPLS) method to uncover complex gene-drug relationships in cancer. The method identified 20 gene-drug co-modules, offering new insights into cancer drug mechanisms and potential therapeutic targets.
Area of Science:
- Genomics
- Pharmacology
- Computational Biology
Background:
- The relationship between genomic factors and cancer drug response is not well understood.
- Patient response to cancer treatments varies due to genetic alterations.
- Large-scale pharmacogenomic data offers opportunities for therapy improvement but presents analytical challenges.
Purpose of the Study:
- To develop and apply a novel method for identifying combinatorial patterns in pharmacogenomic data.
- To uncover gene-drug co-modules and their functional implications in diverse human cancers.
Main Methods:
- A sparse network-regularized partial least square (SNPLS) method was developed.
- A molecular network was integrated into the partial least square model using a network-based penalty.
- The SNPLS method was validated using simulation data and applied to large-scale gene expression and drug-response data.
Main Results:
- The SNPLS method effectively identified joint modular patterns in gene expression and drug-response data.
- Analysis of 641 cancer cell lines revealed 20 significant gene-drug co-modules.
- These co-modules, averaging 137 genes and 2 drugs, showed substantial functional implications and coordinated gene-drug associations.
Conclusions:
- The study provides new insights into the molecular mechanisms of anticancer drugs.
- Identified gene-drug co-modules suggest potential new drug targets for specific cancer types.
- The SNPLS method is a valuable tool for analyzing complex pharmacogenomic data.
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