Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens

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.

Keywords: cell-cell communication; cytotoxic T cells; genome-wide CRISPR screen; immune checkpoint inhibitors; intercellular interactions; ligand-receptor interactions; target discovery; triple-negative breast cancer.

Figure 1

Intercellular interactions are potent targets for cancer immunotherapy. (A) Intercellular interactions between PVR, NECTIN2, CD226, TIGIT, and CD96. PVR and NECTIN2 are expressed on antigen-presenting cells (APCs) and some tumor cells. Their receptors, CD226, TIGIT, and CD96 are expressed on T cells or natural killer cells (NK cells). Upon binding to PVR or NECTIN2, CD226 and TIGIT trigger stimulatory and inhibitory signals, respectively. Whether the binding of PVR to CD96 delivers stimulatory or inhibitory signals is to be determined. The multiplicity of these interactions highlights the importance of focusing on intercellular interactions, rather than a single ligand or a receptor. (B) The percentage of proteins targeted by non-cancer, non-IO, and IO drugs that belong to each protein class. Membrane receptors, surface antigens, and adhesion proteins are preferentially targeted by IO drugs, relative to enzymes. APC, antigen-presenting cell; NK cell, natural killer cell; IO, immuno-oncology.

Figure 2

Overview of methods. (A) Data from two genome-wide pooled clustered regularly interspaced short palindromic repeats (CRISPR) screens were used. One CRISPR screen edited cytotoxic T lymphocytes (CTLs) to identify genes whose knockout increased the infiltration of CTLs into tumor tissue (upper) (15). The second screen edited two triple-negative breast cancer (TNBC) cell lines to identify genes whose knockout regulates the evasion of TNBC cells from CTL-mediated killing (lower) (8). (B) Genome-wide CRISPR screens yielded normalized read count matrices showing the amount of sgRNAs in each sample. TNBC CRISPR screen data had two matrices, one for each TNBC cell line. (C) We performed differential analysis to calculate fold changes of genes between knockout and control samples, yielding “Gene-level normZ scores”. Positive scores were assigned to genes that were more likely to activate CTL function, and negative scores were assigned to genes that were more likely to suppress CTL function. (D) We collected information for intercellular interactions from public databases and calculated the score of each intercellular interaction by ‘combining’ gene-level normZ scores of the interactants. Because two TNBC cell lines were used, two ‘cell-line specific intercellular normZ scores’ were obtained, one for each TNBC cell line. We combined cell-line-specific intercellular normZ scores to obtain the final intercellular normZ score. TIL, tumor-infiltrating lymphocyte; KO, knockout; CTRL, control; diff, differential analysis; exp, expression; comb, combination.

Figure 3

NormZ scores from the intercellular, CTL, and TNBC CRISPR screens. Intercellular normZ scores were calculated by the ‘Tolerant’ method. (A) Rank-ordered normZ scores from the intercellular (left), CTL (middle), and TNBC (right) CRISPR screens. Interactions/genes known to activate and suppress CTL function are marked in red and blue, respectively. The dot sizes are negatively proportional to the FDR. The top ten interactions/genes are represented in the inset. (B) Statistically significant (FDR < 5%) genes from the gene-level intercellular and CTL CRISPR screens. (C) Statistically significant (FDR < 5%) genes from the gene-level intercellular and TNBC CRISPR screens. Genes with positive and negative normZ scores are marked with red and blue, respectively. Well-known immunomodulators from the silver standard data are marked in bold. (D) The intercellular, CTL, and TNBC normZ scores of interactions/genes targeted by approved immunotherapeutic drugs, or phase III clinical trial drug candidates. * Statistically significant (FDR < 5%) interactions/genes.

Figure 4

The performance of the intercellular CRISPR screen (‘Tolerant’ method). (A) Confusion matrix of intercellular CRISPR screens. Predictions were made based on an FDR < 5%. (B) Precision, recall, and F1 scores of intercellular CRISPR screens. (C) AUROC, precision, recall, and F1 scores of CTL, TNBC, and intercellular CRISPR screens. AUROC, area under the receiver operating characteristic curve.