Computational discovery of co-expressed antigens as dual targeting candidates for cancer therapy through bulk, single-cell, and spatial transcriptomics.

Motivation: Bispecific antibodies (bsAbs) that bind to two distinct surface antigens on cancer cells are emerging as an appealing therapeutic strategy in cancer immunotherapy. However, considering the vast number of surface proteins, experimental identification of potential antigen pairs that are selectively expressed in cancer cells and not in normal cells is both costly and time-consuming. Recent studies have utilized large bulk RNA-seq databases to propose bispecific targets for various cancers. However, co-expressed pairs derived from bulk RNA-seq do not necessarily indicate true co-expression of both markers in malignant cells. Single-cell RNA-seq (scRNA-seq) can circumvent this issue but the issues in low coverage of transcripts impede the large-scale characterization of co-expressed pairs. Results: We present a computational pipeline for bsAbs target identification which combines the advantages of bulk and scRNA-seq while minimizing the issues associated with using these approaches separately. We select hepatocellular carcinoma (HCC) as a case study to demonstrate the utility of the approach. First, using the bulk RNA-seq samples in the OCTAD database, we identified target pairs that most distinctly differentiate tumor cases from healthy controls. Next, we confirmed our findings on the scRNA-seq database comprising 39 361 healthy cells from vital organs and 18 000 cells from HCC tumors. The top pair was GPC3-MUC13, where both genes are co-expressed on the surface of over 30% of malignant hepatocytes and have very low expression in other cells. Finally, we leveraged the emerging spatial transcriptomic to validate the co-expressed pair Availability and implementation: A standalone R package (https://github.com/Bin-Chen-Lab/bsAbsFinder).