Abstract 1198: Discovering novel bispecific antibody targets through the mining of large-scale bulk and single cell RNA-seq databases

Abstract

Abstract Bispecific antibodies (bsAbs) that bind two distinct surface proteins in cancer cells are emerging as an appealing therapeutic strategy in cancer immunotherapy. Among thousands of surface proteins, experimentally identifying the best target pairs that are expressed only in cancer cells, but not in normal cells, is costly and time-consuming. The open bulk RNASeq and single-cell (sc)RNASeq offers a great resource to identify novel bispecific targets. Bulk RNASeq has been widely explored to identify therapeutic targets and biomarkers, resulting in voluminous data for various cancers and normal tissues, but the mixture of cell types in bulk RNASeq could not characterize the precise expression of targets in cancer cells, resulting in considerable false positives. scRNASeq provides a high resolution of target expression in individual cells, however, the challenges in solving the dropout issue and cell type classification hinder the direct use of scRNASeq in bsAbs target identification. Utilizing the OCTAD database consisting of over 20,000 bulk RNASeq samples, we proposed an approach that identifies target pairs that separate tumors from healthy the most, taking into account cluster heterogeneity, the distance between tumors and healthy as well as the angle between potential markers. Among the top pairs in Hepatocellular Carcinoma (HCC), CD33~PLVAP, for example, was a false positive because CD33 is myeloid and lymphoid cell lineage-specific. We thus assembled a scRNAseq database of healthy vital organs containing 39361 cells to aid selection. By comparing their expression with the expression of 18000 malignant cells predicted from 72000 cells of eight HCC samples, we identified target pairs that mostly express on the surface of the malignant HCC cells and had low or zero expression in vital organs. The most promising marker pair was GPC3~MUC13, presenting on the surface of over 30% of malignant HCC cells, with very low expression in vital organs. We further developed an R package to navigate the bsAbs target selection from open bulk RNASeq and scRNASeq. Citation Format: Eugene Chekalin, Shreya Paithankar, Bin Chen. Discovering novel bispecific antibody targets through the mining of large-scale bulk and single cell RNA-seq databases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1198.