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Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens
Soorin Yim1,2, Woochang Hwang3, Namshik Han3,4
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Abstract:
Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 (PDCD1) binding PD-L1 (CD274), are crucial for cancer cell clearance. However, immune checkpoint inhibitors targeting these interactions are effective only in a subset of patients, requiring the identification of novel immunotherapy targets. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening in either cancer or immune cells has been employed to discover regulators of immune cell function. However, CRISPR screens in a single cell type complicate the identification of essential intercellular interactions. Further, pooled screening is associated with high noise levels. Herein, we propose intercellular CRISPR screens, a computational approach for the analysis of genome-wide CRISPR screens in every interacting cell type for the discovery of intercellular interactions as immunotherapeutic targets. We used two publicly available genome-wide CRISPR screening datasets obtained while triple-negative breast cancer (TNBC) cells and CTLs were interacting. We analyzed 4825 interactions between 1391 ligands and receptors on TNBC cells and CTLs to evaluate their effects on CTL function. Intercellular CRISPR screens discovered targets of approved drugs, a few of which were not identifiable in single datasets. To evaluate the method's performance, we used data for cytokines and costimulatory molecules as they constitute the majority of immunotherapeutic targets. Combining both CRISPR datasets improved the recall of discovering these genes relative to using single CRISPR datasets over two-fold. Our results indicate that intercellular CRISPR screens can suggest novel immunotherapy targets that are not obtained through individual CRISPR screens. The pipeline can be extended to other cancer and immune cell types to discover important intercellular interactions as potential immunotherapeutic targets.
Insights
Intercellular CRISPR screens identify novel cancer immunotherapy targets by analyzing interactions between cancer and immune cells. This computational approach improves discovery of therapeutic targets compared to single-cell screens.
Area of Science:
- Immunology
- Genomics
- Computational Biology
Background:
- Cancer immunotherapy relies on T cell-mediated killing of cancer cells, with immune checkpoints like PD-1/PD-L1 being key targets.
- Current immunotherapies are effective in a limited patient population, necessitating the discovery of new therapeutic targets.
- Genome-wide CRISPR screens are used to find regulators of immune cell function, but analyzing single cell types misses crucial intercellular interactions.
Purpose of the Study:
- To develop and validate a computational method, intercellular CRISPR screens, for discovering novel immunotherapy targets by analyzing genome-wide CRISPR screens in interacting cancer and immune cells.
- To identify essential intercellular interactions between triple-negative breast cancer (TNBC) cells and cytotoxic T lymphocytes (CTLs) that can serve as immunotherapeutic targets.
Main Methods:
- Developed a computational pipeline called intercellular CRISPR screens to analyze paired genome-wide CRISPR screen datasets from interacting cancer and immune cells.
- Applied the pipeline to analyze 4825 ligand-receptor interactions between TNBC cells and CTLs using publicly available datasets.
- Evaluated the method's performance by assessing its ability to identify known immunotherapeutic targets like cytokines and costimulatory molecules.
Main Results:
- Intercellular CRISPR screens successfully identified novel immunotherapy targets, including targets of approved drugs, some of which were missed in single-cell datasets.
- Combining CRISPR datasets from both TNBC cells and CTLs more than doubled the recall of discovering key immunotherapeutic genes compared to using individual datasets.
- The analysis highlighted the importance of considering intercellular interactions for comprehensive target discovery.
Conclusions:
- Intercellular CRISPR screens offer a powerful computational approach to uncover novel, previously unidentified immunotherapy targets by integrating data from interacting cell types.
- This method significantly enhances the discovery of therapeutic targets compared to traditional single-cell CRISPR screening approaches.
- The pipeline is adaptable for use with other cancer and immune cell types, promising broader applications in discovering new immunotherapeutic strategies.
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