A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia

Abstract

Combination therapies are one potential approach to improve the outcomes of patients with refractory or relapsed disease. However, comprehensive testing in scarce primary patient material is hampered by the many drug combination possibilities. Furthermore, inter- and intra-patient heterogeneity necessitates personalized treatment optimization approaches that effectively exploit patient-specific vulnerabilities to selectively target both the disease- and resistance-driving cell populations. Here, we developed a systematic combinatorial design strategy that uses machine learning to prioritize the most promising drug combinations for patients with relapsed/refractory (R/R) acute myeloid leukemia (AML). The predictive approach leveraged single-cell transcriptomics and single-agent response profiles measured in primary patient samples to identify targeted combinations that co-inhibit treatment resistant cancer cells individually in each AML patient sample. Cell type compositions evolved dynamically between the diagnostic and R/R stages uniquely in each patient, hence requiring personalized drug combination strategies to target therapy-resistant cancer cells. Cell population-specific drug combination assays demonstrated how patient-specific and disease stage-tailored combination predictions led to treatments with synergy and strong potency in R/R AML cells, while the same combinations elicited non-synergistic effects in the diagnostic stage and minimal co-inhibitory effects on normal cells. In preliminary experiments on clinical trial samples, the approach predicted clinical outcomes to venetoclax-azacitidine combination therapy in patients with AML. Overall, the computational-experimental approach provides a rational means to identify personalized combinatorial regimens for individual AML patients with R/R disease that target treatment-resistant leukemic cells, thereby increasing their likelihood for clinical translation.