Unifying heterogeneous expression data to predict targets for CAR-T cell therapy

Abstract

Chimeric antigen receptor (CAR) T-cell therapy combines antigen-specific properties of monoclonal antibodies with the lytic capacity of T cells. An effective and safe CAR-T cell therapy strategy relies on identifying an antigen that has high expression and is tumor specific. This strategy has been successfully used to treat patients with CD19+ B-cell acute lymphoblastic leukemia (B-ALL). Finding a suitable target antigen for other cancers such as acute myeloid leukemia (AML) has proven challenging, as the majority of currently targeted AML antigens are also expressed on hematopoietic progenitor cells (HPCs) or mature myeloid cells. Herein, we developed a computational method to perform a data transformation to enable the comparison of publicly available gene expression data across different datasets or assay platforms. The resulting transformed expression values (TEVs) were used in our antigen prediction algorithm to assess suitable tumor-associated antigens (TAAs) that could be targeted with CAR-T cells. We validated this method by identifying B-ALL antigens with known clinical effectiveness, such as CD19 and CD22. Our algorithm predicted TAAs being currently explored preclinically and in clinical CAR-T AML therapy trials, as well as novel TAAs in pediatric megakaryoblastic AML. Thus, this analytical approach presents a promising new strategy to mine diverse datasets for identifying TAAs suitable for immunotherapy.

Keywords

• acute myeloid leukemia (aml)
• b-cell acute lymphoblastic (b-all)
• bioinformatics
• car-t cell therapy
• data heterogeneity
• immunotherapy
• leukemia
• megakaryoblastic aml (amkl)
• microarray
• rna-seq (rna sequencing)