Schreiner P, Velasquez MP, Gottschalk S, et al. Unifying Heterogeneous Expression Data to Predict Targets for CAR-T Cell Therapy. OncoImmunology. 2021; 10 (1) (doi: 10.1080/2162402X.2021.2000109).
Pinpointing ideal antigens — meaning they are highly expressed and tumor-specific — has been elusive for some cancers, until researchers developed a computational method of identifying strong targets for chimeric antigen receptor (CAR) T-cell therapy. The team used the tool to perform a data transformation to support the comparison of publicly accessible gene expression data — including microarray, RNA-sequencing, and proteomics data — across multiple datasets and platforms. The transformed expression values (TEVs) that resulted were applied to an antigen prediction algorithm, which successfully predicted tumor-associated antigens (TAAs) already under investigation and novel TAAs in pediatric megakaryoblastic AML (acute myeloid leukemia). Finding appropriate antigen targets in the setting of AML has been particularly challenging due to the overlapping presence of antigens on leukemic blasts and normal hematopoietic stem progenitor cells, lymphoid cells, and other tissues. This technique for TAA prediction, the study authors note, "can also be extrapolated to identify suitable surface antigens as immunotherapy targets in other contexts."