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Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
Published on: December 26, 2016
A deep learning framework for in silico screening of anticancer drugs at the single-cell level
Peijing Zhang1,2,3, Xueyi Wang1, Xufeng Cen2,4
1Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China.
Abstract:
Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for in silico screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates' tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong's prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.
Insights
This study introduces Shennong, a deep learning framework for anticancer drug screening using single-cell RNA sequencing data. Shennong predicts drug responses and tissue damage, accelerating targeted therapy development.
Area of Science:
- Oncology
- Bioinformatics
- Pharmacology
Background:
- Tumor heterogeneity is a key factor in cancer progression and treatment resistance.
- Single-cell RNA sequencing (scRNA-seq) offers insights into cellular heterogeneity and rare cell identification.
- Targeted therapeutic strategies can be improved by understanding cellular diversity in tumors.
Purpose of the Study:
- To develop a deep learning framework for *in silico* screening of anticancer drugs.
- To identify potential drug candidates targeting specific cell clusters within a pan-cancer and pan-tissue transcriptional landscape.
- To predict individual cell responses to drugs and evaluate their tissue-damaging effects and mechanisms.
Main Methods:
- Utilized a pan-cancer and pan-tissue single-cell transcriptional landscape.
- Developed and applied the Shennong deep learning framework for drug screening.
- Predicted cell-specific drug responses, tissue damage, and action mechanisms.
Main Results:
- Shennong successfully screened anticancer drugs *in silico* across diverse cell types.
- Prioritized compounds included FDA-approved drugs in trials and novel drug candidates with anti-tumor activity.
- Predicted tissue damaging effects correlated with known adverse events and drug development failures.
Conclusions:
- The Shennong framework provides a robust and explainable method for drug discovery.
- This approach has the potential to accelerate drug screening and enhance therapeutic strategy design.
- Shennong can improve the accuracy and efficiency of identifying effective and safe anticancer treatments.
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