Chekalin Evgenii, Paithankar Shreya, Shankar Rama, Xing Jing, Xu Wenfeng, Chen Bin
Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, United States.
Hengenix Biotech, Inc., Milpitas, CA 95035, United States.
Bioinform Adv. 2024 Jun 20;4(1):vbae096. doi: 10.1093/bioadv/vbae096. eCollection 2024.
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.
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 .
A standalone R package (https://github.com/Bin-Chen-Lab/bsAbsFinder).
能够结合癌细胞上两种不同表面抗原的双特异性抗体（bsAbs）正在成为癌症免疫治疗中一种有吸引力的治疗策略。然而，考虑到表面蛋白的数量众多，通过实验鉴定在癌细胞而非正常细胞中选择性表达的潜在抗原对既昂贵又耗时。最近的研究利用大量的批量RNA测序（RNA-seq）数据库为各种癌症提出双特异性靶点。然而，从批量RNA-seq中得出的共表达对并不一定表明两种标志物在恶性细胞中真正共表达。单细胞RNA-seq（scRNA-seq）可以规避这个问题，但转录本低覆盖度的问题阻碍了对共表达对的大规模表征。
我们提出了一种用于鉴定双特异性抗体靶点的计算流程，该流程结合了批量RNA-seq和scRNA-seq的优点，同时将单独使用这些方法所带来的问题降至最低。我们选择肝细胞癌（HCC）作为案例研究来证明该方法的实用性。首先，利用OCTAD数据库中的批量RNA-seq样本，我们鉴定出最能将肿瘤病例与健康对照区分开来的靶点对。接下来，我们在包含来自重要器官的39361个健康细胞和来自HCC肿瘤的1800个细胞的scRNA-seq数据库上证实了我们的发现。排名第一的对是GPC3 - MUC13，这两个基因在超过30%的恶性肝细胞表面共表达，而在其他细胞中的表达非常低。最后，我们利用新兴的空间转录组学技术验证了这个共表达对。
