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DSCN: Double-target selection guided by CRISPR screening and network
Enze Liu1,2,3, Xue Wu2, Lei Wang2
1Division of Hematology and Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America.
Abstract:
Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.
Insights
Developing effective cancer therapies requires identifying optimal gene target combinations. The new DSCN computational method efficiently predicts these combinations by integrating CRISPR screening data with patient gene expression and protein-protein interaction networks.
Area of Science:
- Computational Biology
- Genomics
- Cancer Research
Background:
- Cancer is a complex disease driven by multiple mechanisms, necessitating combination therapies for effective treatment.
- Predicting synergistic gene target combinations is challenging, limiting the development of novel cancer therapies.
- Current genome-wide screening methods are limited in identifying effective target combinations.
Purpose of the Study:
- To develop an effective computational approach for selecting candidate gene target combinations for cancer therapy.
- To ensure translational relevance of predicted target combinations between cell lines and cancer patients.
- To improve the prediction accuracy and computational efficiency of identifying synergistic gene pairs.
Main Methods:
- Developed DSCN (double-target selection guided by CRISPR screening and network), a method integrating gene expression, CRISPR screening, and protein-protein interaction (PPI) networks.
- Utilized a sub-sampling approach to model gene knockdown effects on the PPI network and facilitate second target selection.
- Employed a 'diffusion-path' scoring scheme to differentiate synthetic lethal (SL) gene pairs and evaluated performance against existing algorithms.
Main Results:
- The DSCN sub-sampling model showed a high correlation (R2 = 0.75) with observed gene expression changes in pancreatic cell lines after MAP2K1/MAP2K2 inhibition.
- The 'diffusion-path' method significantly identified known SL gene pairs in pancreatic cancer (P = 0.001).
- DSCN demonstrated superior performance and at least ten times faster computational speed compared to OptiCon and VIPER algorithms.
- DSCNi, a sample-specific application, showed a high correlation between predicted and real synergistic drug combinations (P = 1e-5).
Conclusions:
- DSCN is a highly effective and efficient computational method for selecting synergistic gene target combinations in cancer.
- The method facilitates the identification of therapeutically relevant target combinations with translational potential.
- DSCN offers a significant advancement in computational approaches for precision cancer therapy development.
