We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. Significance: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB. See related commentary by Letai, p. 290. This article is highlighted in the In This Issue feature, p. 275

Myeloid neoplasms with erythroid or megakaryocytic differentiation include pure erythroid leukemia, myelodysplastic syndrome with erythroid features, and acute megakaryoblastic leukemia (FAB M7) and are characterized by poor prognosis and limited treatment options. Here, we investigate the drug sensitivity landscape of these rare malignancies. We show that acute myeloid leukemia (AML) cells with erythroid or megakaryocytic differentiation depend on the antiapoptotic protein B-cell lymphoma (BCL)-XL, rather than BCL-2, using combined ex vivo drug sensitivity testing, genetic perturbation, and transcriptomic profiling. High-throughput screening of >500 compounds identified the BCL-XL–selective inhibitor A-1331852 and navitoclax as highly effective against erythroid/megakaryoblastic leukemia cell lines. In contrast, these AML subtypes were resistant to the BCL-2 inhibitor venetoclax, which is used clinically in the treatment of AML. Consistently, genome-scale CRISPR-Cas9 and RNAi screening data demonstrated the striking essentiality of BCL-XL-encoding BCL2L1 but not BCL2 or MCL1, for the survival of erythroid/megakaryoblastic leukemia cell lines. Single-cell and bulk transcriptomics of patient samples with erythroid and megakaryoblastic leukemias identified high BCL2L1 expression compared with other subtypes of AML and other hematological malignancies, where BCL2 and MCL1 were more prominent. BCL-XL inhibition effectively killed blasts in samples from patients with AML with erythroid or megakaryocytic differentiation ex vivo and reduced tumor burden in a mouse erythroleukemia xenograft model. Combining the BCL-XL inhibitor with the JAK inhibitor ruxolitinib showed synergistic and durable responses in cell lines. Our results suggest targeting BCL-XL as a potential therapy option in erythroid/megakaryoblastic leukemias and highlight an AML subgroup with potentially reduced sensitivity to venetoclax-based treatments.

The BCL-2 inhibitor venetoclax has revolutionized the treatment of acute myeloid leukemia (AML) in patients not benefiting from intensive chemotherapy. Nevertheless, treatment failure remains a challenge, and predictive markers are needed, particularly for relapsed or refractory AML. Ex vivo drug sensitivity testing may correlate with outcomes, but its prospective predictive value remains unexplored. Here we report the results of the first stage of the prospective phase II VenEx trial evaluating the utility and predictiveness of venetoclax sensitivity testing using different cell culture conditions and cell viability assays in patients receiving venetoclax-azacitidine. Participants with de novo AML ineligible for intensive chemotherapy, relapsed or refractory AML, or secondary AML were included. The primary endpoint was the treatment response in participants showing ex vivo sensitivity and the key secondary endpoints were the correlation of sensitivity with responses and survival. Venetoclax sensitivity testing was successful in 38/39 participants. Experimental conditions significantly influenced the predictive accuracy. Blast-specific venetoclax sensitivity measured in conditioned medium most accurately correlated with treatment outcomes; 88% of sensitive participants achieved a treatment response. The median survival was significantly longer for participants who were ex vivo-sensitive to venetoclax (14.6 months for venetoclax-sensitive patients vs. 3.5 for venetoclax-insensitive patients, P<0.001). This analysis illustrates the feasibility of integrating drug-response profiling into clinical practice and demonstrates excellent predictivity. This trial is registered with ClinicalTrials.gov identifier: NCT04267081.

Machine learning and single-cell data inform personalized drug combinations that selectively co-inhibit cancer cell populations.

The B-cell lymphoma 2 inhibitor venetoclax has shown promise for treating acute myeloid leukemia (AML). However, identifying patients likely to respond remains a challenge, especially for those with relapsed/refractory (R/R) disease. We evaluated the utility of ex vivo venetoclax sensitivity testing to predict treatment responses to venetoclax-azacitidine in a prospective, multicenter, phase 2 trial. The trial recruited 104 participants with previously untreated (n = 48), R/R (n = 39), or previously treated secondary AML (sAML) (n = 17). The primary end point was complete remission or complete remission with incomplete hematologic recovery (CR/CRi) rate in ex vivo sensitive trial participants during the first 3 therapy cycles. The key secondary end points included the correlations between ex vivo drug sensitivity, responses, and survival. Venetoclax sensitivity was successfully assessed in 102 of 104 participants, with results available within a median of 3 days from sampling. In previously untreated AML, ex vivo sensitivity corresponded to an 85% (34/40) CR/CRi rate, with a median overall survival (OS) of 28.7 months, compared with 5.5 months for ex vivo resistant patients (P = .002). For R/R/sAML, ex vivo sensitivity resulted in a 62% CR/CRi rate (21/34) and median OS of 9.7 vs 3.3 months for ex vivo resistant patients (P < .001). In univariate and multivariate analysis, ex vivo venetoclax sensitivity was the strongest predictor for a favorable treatment response and survival. This trial demonstrates the feasibility of integrating ex vivo drug testing into clinical practice to identify patients with AML, particularly in the R/R setting, who benefit from venetoclax. This trial was registered at www.clinicaltrials.gov as #NCT04267081.

Combination therapies are one potential approach to improve the outcomes of patients with relapsed/refractory (R/R) disease. However, comprehensive testing in scarce primary patient material is hampered by the many drug combination possibilities. Furthermore, inter- and intrapatient heterogeneity necessitates personalized treatment optimization approaches that effectively exploit patient-specific vulnerabilities to selectively target both the disease- and resistance-driving cell populations. In this study, we developed a systematic combinatorial design strategy that uses machine learning to prioritize the most promising drug combinations for patients with 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 coinhibit treatment-resistant cancer cells individually in each sample of patients with AML. 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, whereas the same combinations elicited nonsynergistic effects in the diagnostic stage and minimal coinhibitory effects on normal cells. In preliminary experiments on clinical trial samples, the approach predicted clinical outcomes of venetoclax–azacitidine combination therapy in patients with AML. Overall, the computational–experimental approach provides a rational means to identify personalized combinatorial regimens for individual patients with AML with R/R disease that target treatment-resistant leukemic cells, thereby increasing their likelihood of clinical translation. Significance: A predictive model identifies patient-tailored combinations that coinhibit multiple drivers to selectively and synergistically target leukemia cells, which could reduce therapy resistance and enhance treatment outcomes in patients with advanced disease.