Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly aggressive and heterogeneous disease, underscoring the need for improved therapeutic options and methods to optimally predict responses. With the wealth of available data resources, including clinical features, multiomics analysis, and ex vivo drug screening from AML patients, development of drug response prediction models has become feasible. Knowledge graphs (KGs) embed the relationships between different entities or features, allowing for explanation of a wide breadth of drug sensitivity and resistance mechanisms. We designed AML drug response prediction models guided by KGs. Our models included engineered features, relative gene expression between marker genes for each drug and regulators (e.g., transcription factors). We identified relative gene expression of FGD4-MIR4519, NPC2-GATA2, and BCL2-NFKB2 as predictive features for venetoclax ex vivo drug response. The KG-guided models provided high accuracy in independent test sets, overcame potential platform batch effects, and provided candidate drug sensitivity biomarkers for further validation.