Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.

Azithromycin promotes relapse by disrupting immune and metabolic networks after allogeneic stem cell transplantation.

Administration of azithromycin after allogeneic hematopoietic stem cell transplantation for hematologic malignancies has been associated with relapse in a randomized phase 3 controlled clinical trial. Studying 240 samples from patients randomized in this trial is a unique opportunity to better understand the mechanisms underlying relapse, the first cause of mortality after transplantation. We used multi-omics on patients' samples to decipher immune alterations associated with azithromycin intake and post-transplantation relapsed malignancies. Azithromycin was associated with a network of altered energy metabolism pathways and immune subsets, including T cells biased toward immunomodulatory and exhausted profiles. In vitro, azithromycin exposure inhibited T-cell cytotoxicity against tumor cells and impaired T-cell metabolism through glycolysis inhibition, down-regulation of mitochondrial genes, and up-regulation of immunomodulatory genes, notably SOCS1. These results highlight that azithromycin directly affects immune cells that favor relapse, which raises caution about long-term use of azithromycin treatment in patients at high risk of malignancies. The ALLOZITHRO trial was registered at www.clinicaltrials.gov as #NCT01959100.

A multiparametric niche-like drug screening platform in acute myeloid leukemia.

Functional precision medicine in AML often relies on short-term in vitro drug sensitivity screening (DSS) of primary patient cells in standard culture conditions. We designed a niche-like DSS assay combining physiologic hypoxia (O2 3%) and mesenchymal stromal cell (MSC) co-culture with multiparameter flow cytometry to enumerate lymphocytes and differentiating (CD11/CD14/CD15+) or leukemic stem cell (LSC)-enriched (GPR56+) cells within the leukemic bulk. After functional validation of GPR56 expression as a surrogate for LSC enrichment, the assay identified three patterns of response, including cytotoxicity on blasts sparing LSCs, induction of differentiation, and selective impairment of LSCs. We refined our niche-like culture by including plasma-like amino-acid and cytokine concentrations identified by targeted metabolomics and proteomics of primary AML bone marrow plasma samples. Systematic interrogation revealed distinct contributions of each niche-like component to leukemic outgrowth and drug response. Short-term niche-like culture preserved clonal architecture and transcriptional states of primary leukemic cells. In a cohort of 45 AML samples enriched for NPM1c AML, the niche-like multiparametric assay could predict morphologically (p = 0.02) and molecular (NPM1c MRD, p = 0.04) response to anthracycline-cytarabine induction chemotherapy. In this cohort, a 23-drug screen nominated ruxolitinib as a sensitizer to anthracycline-cytarabine. This finding was validated in an NPM1c PDX model.

Cystine uptake inhibition potentiates front-line therapies in acute myeloid leukemia.

By querying metabolic pathways associated with leukemic stemness and survival in multiple AML datasets, we nominated SLC7A11 encoding the xCT cystine importer as a putative AML dependency. Genetic and chemical inhibition of SLC7A11 impaired the viability and clonogenic capacity of AML cell lines in a cysteine-dependent manner. Sulfasalazine, a broadly available drug with xCT inhibitory activity, had anti-leukemic activity against primary AML samples in ex vivo cultures. Multiple metabolic pathways were impacted upon xCT inhibition, resulting in depletion of glutathione pools in leukemic cells and oxidative stress-dependent cell death, only in part through ferroptosis. Higher expression of cysteine metabolism genes and greater cystine dependency was noted in NPM1-mutated AMLs. Among eight anti-leukemic drugs, the anthracycline daunorubicin was identified as the top synergistic agent in combination with sulfasalazine in vitro. Addition of sulfasalazine at a clinically relevant concentration significantly augmented the anti-leukemic activity of a daunorubicin-cytarabine combination in a panel of 45 primary samples enriched in NPM1-mutated AML. These results were confirmed in vivo in a patient-derived xenograft model. Collectively, our results nominate cystine import as a druggable target in AML and raise the possibility to repurpose sulfasalazine for the treatment of AML, notably in combination with chemotherapy.