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Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor
Published on: August 25, 2023
In silico method for identification of promising anticancer drug targets
O N Koborova1, D A Filimonov, A V Zakharov
1Institute of Biomedical Chemistry of Russian Academy of Medical Sciences, Moscow, Russia. okoborova@gmail.com
Abstract:
In recent years, the accumulation of the genomics, proteomics, transcriptomics data for topological and functional organization of regulatory networks in a cell has provided the possibility of identifying the potential targets involved in pathological processes and of selecting the most promising targets for future drug development. We propose an approach for anticancer drug target identification, which, using microarray data, allows discrete modelling of regulatory network behaviour. The effect of drugs inhibiting a particular protein or a combination of proteins in a regulatory network is analysed by simulation of a blockade of single nodes or their combinations. The method was applied to the four groups of breast cancer, HER2/neu-positive breast carcinomas, ductal carcinoma, invasive ductal carcinoma and/or a nodal metastasis, and to generalized breast cancer. As a result, some promising specific molecular targets and their combinations were identified. Inhibitors of some identified targets are known as potential drugs for therapy of malignant diseases; for some other targets we identified hits in the commercially available sample databases.
Insights
This study introduces a novel computational approach for identifying anticancer drug targets using gene expression data. The method simulates network behavior to pinpoint promising molecular targets for breast cancer therapy.
Area of Science:
- Computational biology
- Genomics
- Cancer research
Background:
- Advancements in genomics, proteomics, and transcriptomics enable the study of cellular regulatory networks.
- Identifying key molecular targets is crucial for developing effective anticancer drugs.
Purpose of the Study:
- To propose and validate a computational approach for identifying anticancer drug targets.
- To utilize microarray data for discrete modeling of regulatory network behavior in breast cancer.
Main Methods:
- Employing discrete modeling of regulatory networks based on microarray data.
- Simulating the effect of inhibiting single proteins or combinations of proteins within the network.
- Applying the method to various breast cancer subtypes, including HER2/neu-positive, ductal carcinoma, and invasive ductal carcinoma with nodal metastasis.
Main Results:
- Identification of several promising specific molecular targets and their combinations for breast cancer.
- Validation of identified targets, with some inhibitors already known for cancer therapy.
- Discovery of hits for other identified targets in commercially available sample databases.
Conclusions:
- The proposed discrete modeling approach is effective for anticancer drug target identification.
- This method can accelerate the selection of promising targets for drug development in breast cancer.
- The findings provide a foundation for further investigation and therapeutic strategies.
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