Image-based drug screening combined with molecular profiling identifies signatures and drivers of therapy resistance in pediatric AML

Abstract

Despite recent advances in the understanding of the genomic landscape of pediatric acute myeloid leukemia (pedAML), targeted treatments are only available for selected genomic alterations, and the functional link between genotype and outcome remains partially elusive. Functional precision medicine approaches to investigate treatment resistance and patient risk have not been applied systematically for pedAML. Here, we describe an advanced functional screening platform combining high-content imaging and deep learning-based phenotyping. In 45 patients with pedAML, we identify BCL2 and FLT3 inhibitors and standard chemotherapy as major drivers of the chemosensitivity landscape, reveal substantial differential sensitivities between risk groups, and may effectively predict individual measurable residual disease and patient risk. Integration with genomic and epigenomic data uncovers a chemotherapy-resistant primitive state vulnerable to combined BCL2 and MDM2 inhibition and HDAC inhibition. Overall, we identify early signatures of therapy resistance across genetic subgroups and prioritize targeted treatments for these functionally and epigenetically defined patient subsets.

Keywords: cellular differentiation; data integration; deep learning; drug screening; epigenetics; high-content imaging; pediatric AML; precision medicine.