Two promising therapeutic strategies in oncology are chimeric antigen receptor-T cell (CAR-T) therapies and antibody-drug conjugates (ADCs). To be effective and safe, these immunotherapies require surface antigens to be sufficiently expressed in tumors and less or not expressed in normal tissues. To identify new targets for ADCs and CAR-T specifically targeting breast cancer (BC) molecular and pathology-based subtypes, we propose a novel in silico strategy based on multiple publicly available datasets and provide a comprehensive explanation of the workflow for a further implementation.
We carried out differential gene expression analyses on The Cancer Genome Atlas BC RNA-sequencing data to identify BC subtype-specific upregulated genes. To fully explain the proposed target-discovering methodology, as proof of concept, we selected the 200 most upregulated genes for each subtype and undertook a comprehensive analysis of their protein expression in BC and normal tissues through several publicly available databases to identify the potentially safest and viable targets.
We identified 36 potentially suitable and subtype-specific tumor surface antigens (TSAs), including fibroblast growth factor receptor-4 (FGFR4), carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), GDNF family receptor alpha 1 (GFRA1), integrin beta-6 (ITGB6) and ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1). We also identified 63 potential TSA pairs that might be appropriate for co-targeting strategies. Finally, we validated subtype specificity in a cohort of our patients, multiple BC cell lines and the METABRIC database.
Overall, our in silico analysis provides a framework to identify novel and specific TSAs for the development of new CAR-T and antibody-based therapies in BC.

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