No abstract

Background Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention. Methods We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion. Results We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq. Conclusions Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.

Relapse and refractory T cell acute lymphoblastic leukemia (T-ALL) has a poor prognosis and new combination therapies are sorely needed. Here, we used an ex vivo high-throughput screening platform to identify drug combinations that kill zebrafish T-ALL and then validated top drug combinations for preclinical efficacy in human disease. This work uncovered potent drug synergies between AKT/mTORC1 inhibitors and the general tyrosine kinase-inhibitor, dasatinib. Importantly, these same drug combinations effectively killed a subset of relapse and dexamethasone-resistant zebrafish T-ALL. Clinical trials are currently underway using the combination of mTORC1 inhibitor temsirolimus and dasatinib in other pediatric cancer indications, leading us to prioritize this therapy for preclinical testing. This combination effectively curbed T-ALL growth in human cell lines and primary human T-ALL and was well tolerated and effective in suppressing leukemia growth in patient-derived xenografts grown in mice. Mechanistically, dasatinib inhibited phosphorylation and activation of the lymphocyte-specific protein tyrosine kinase (LCK) to blunt the T-cell receptor (TCR) signaling pathway and when complexed with mTORC1 inhibition, induced potent T-ALL cell killing through reducing MCL-1 protein expression. In total, our work uncovered unexpected roles for the LCK kinase and its regulation of downstream TCR signaling in suppressing apoptosis and driving continued leukemia growth. Analysis of a wide array of primary human T-ALLs and PDXs grown in mice suggest that combination of temsirolimus and dasatinib treatment will be efficacious for a large fraction of human T-ALLs.

Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 280 cases with undefined/B-other molecular phenotype, resulting in a single revised subtype for 85.4% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides the first comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries, identifying subgroups with differential survival outcomes.