Back

Integrating Human Genetics and Protective Genome Editing to Enable ADGRE2-Directed AML Therapy

Keschner, Y.; Etchin, J.; Silva, M.; Mager, H.; Hoyt, H.; Xavier-Ferrucio, J.; Manalo, N.; Halfond, A.; Qiu, H.; Wang, R.; Lin, M. I.; Ge, H. G.; Scherer, J.; Chakraborty, T.; Lydeard, J. R.

2025-08-22 cancer biology
10.1101/2025.08.21.671614 bioRxiv
Show abstract

Acute myeloid leukemia (AML) remains a major therapeutic challenge due to extensive disease heterogeneity and lack of cancer-specific antigens. ADGRE2 has emerged as a promising AML target with broad expression in AML patient blast and leukemic stem cell-enriched populations. However, comparable expression in healthy hematopoietic stem and progenitor cells (HSPCs) and myeloid lineages suggests a high susceptibility to on-target, off-tumor myelotoxicity with ADGRE2-targeted therapies. Guided by human genetics data identifying loss-of-function variants, we evaluated whether ADGRE2 is dispensable in hematopoietic stem cells as a protective approach for transplant-based shielding from ADGRE2-directed therapies. Using CRISPR-Cas9 and adenine base editors, we achieved high-efficiency ADGRE2 knockout (>94%) in HSPCs with corresponding protein loss without impairing cell viability, differentiation, and cytokine release in vitro, or long-term engraftment, multilineage differentiation, and persistence of gene editing in mouse xenografts. We also developed novel ADGRE2-specific chimeric antigen receptor (CAR) T cells that demonstrated potent cytotoxicity against AML cells, even at low antigen levels. Together, these findings establish ADGRE2 as a compelling AML target and provide a framework for hematopoietic stem cell transplant with protective gene editing to enable ADGRE2-directed immunotherapies while minimizing myelotoxicity.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.