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DrugComboRanker: drug combination discovery based on target network analysis
Lei Huang1, Fuhai Li2, Jianting Sheng2
1Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USADepartment of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China and NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, Houston Methodist Hospital Research Institute; Weill Cornell Medical College of Cornell University, Houston, TX 77030, USA.
Motivation:
Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities.
Results:
In this study, we propose a novel systematic computational tool DRUGCOMBORANKER: to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients' genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations.
Availability And Implementation:
The program is available on request.
Insights
This study introduces DRUGCOMBORANKER, a computational tool to identify effective drug combinations for cancer treatment by analyzing drug networks and disease signaling pathways. It aims to overcome drug resistance and improve patient outcomes by prioritizing synergistic drug pairs.
Area of Science:
- Computational biology
- Bioinformatics
- Drug discovery
Background:
- Cancer is a complex disease driven by multiple signaling pathways, leading to drug resistance.
- Drug combinations are a promising strategy to improve efficacy and overcome resistance.
- Existing computational methods are insufficient for identifying optimal drug combinations.
Purpose of the Study:
- To develop a systematic computational tool, DRUGCOMBORANKER, for prioritizing synergistic drug combinations.
- To uncover the mechanisms of action for predicted drug combinations.
- To provide a feasible approach for identifying effective drug combinations from numerous possibilities.
Main Methods:
- Constructed a drug functional network using genomic profiles and partitioned it into communities with Bayesian non-negative matrix factorization.
- Identified drug targets by applying a recommendation system on drug communities.
- Built disease-specific signaling networks and identified drug combinations targeting complementary signaling modules.
Main Results:
- DRUGCOMBORANKER successfully prioritized synergistic drug combinations for lung adenocarcinoma and breast cancer.
- The tool mapped drug targets onto disease signaling networks, elucidating mechanisms of action.
- Case studies validated the effectiveness of the identified drug combinations.
Conclusions:
- DRUGCOMBORANKER offers a novel computational approach to discover effective drug combinations for cancer therapy.
- The tool aids in understanding the mechanisms underlying drug synergy.
- This approach facilitates the development of combination therapies to combat cancer and drug resistance.
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