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Tyrosine kinase inhibitor therapy revolutionized chronic myeloid leukemia treatment and showed how targeted therapy and molecular monitoring could be used to substantially improve survival outcomes. We used chronic myeloid leukemia as a model to understand a critical question: why do some patients have an excellent response to therapy, while others have a poor response? We studied gene expression in whole blood samples from 112 patients from a large phase III randomized trial (clinicaltrials gov. Identifier: NCT00471497), dichotomizing cases into good responders (BCR::ABL1 ≤10% on the International Scale by 3 and 6 months and ≤0.1% by 12 months) and poor responders (failure to meet these criteria). Predictive models based on gene expression demonstrated the best performance (area under the curve =0.76, standard deviation =0.07). All of the top 20 pathways overexpressed in good responders involved immune regulation, a finding validated in an independent data set. This study emphasizes the importance of pretreatment adaptive immune response in treatment efficacy and suggests biological pathways that can be targeted to improve response.

Mutations in the gene Additional Sex-Combs Like 1 (ASXL1) are recurrent in myeloid malignancies as well as the pre-malignant condition clonal hematopoiesis, where they are universally associated with poor prognosis. However, the role of ASXL1 in myeloid lineage maturation is incompletely described. To define the role of ASXL1 in myelopoiesis, we employed single cell RNA sequencing and a murine model of hematopoietic-specific Asxl1 deletion. In granulocyte progenitors, Asxl1 deletion leads to hyperactivation of MYC and a quantitative decrease in neutrophil production. This loss of granulocyte production was not accompanied by significant changes in the landscape of covalent histone modifications. However, Asxl1 deletion results in a decrease in RNAPII promoter-proximal pausing in granulocyte progenitors, indicative of a global increase in productive transcription. These results suggest that ASXL1 inhibits productive transcription in granulocyte progenitors, identifying a new role for this epigenetic regulator in myeloid development.

Drug resistance in chronic myeloid leukaemia (CML) may occur via mutations in the causative BCR::ABL1 fusion or BCR::ABL1-independent mechanisms. We analysed 48 patients with BCR::ABL1-independent resistance for the presence of secondary fusion genes by RNA sequencing. We identified 10 of the most frequently detected secondary fusions in 21 patients. Validation studies, cell line models, gene expression analysis and drug screening revealed differences with respect to proliferation rate, differentiation and drug sensitivity. Notably, expression of RUNX1::MECOM led to resistance to ABL1 tyrosine kinase inhibitors in vitro. These results suggest secondary fusions contribute to BCR::ABL1-independent resistance and may be amenable to combined therapies.

Colony stimulating factor 3 receptor (CSF3R) mutations lead to JAK pathway activation and are the molecular hallmark of chronic neutrophilic leukemia (CNL). Approximately half of patients with CNL also have mutations in SET binding protein 1 (SETBP1). In this study, we developed models of SETBP1-mutated leukemia to understand the role that SETBP1 plays in CNL. SETBP1 mutations promote self-renewal of CSF3R-mutated hematopoietic progenitors in vitro and prevent cells from undergoing terminal differentiation. In vivo, SETBP1 mutations accelerate leukemia progression, leading to the rapid development of hepatosplenomegaly and granulocytosis. Through transcriptomic and epigenomic profiling, we found that SETBP1 enhances progenitor-associated programs, most strongly upregulating Myc and Myc target genes. This upregulation of Myc can be reversed by LSD1 inhibitors. In summary, we found that SETBP1 mutations promote aggressive hematopoietic cell expansion when expressed with mutated CSF3R through the upregulation of Myc-associated gene expression programs.

Using ex vivo drug screening of primary patient specimens, we identified the combination of the p38 MAPK inhibitor doramapimod (DORA) with the BCL2 inhibitor venetoclax (VEN) as demonstrating broad, enhanced efficacy compared with each single agent across 335 acute myeloid leukemia (AML) patient samples while sparing primary stromal cells. Single-agent DORA and VEN sensitivity was associated with distinct, nonoverlapping tumor cell differentiation states. In particular, increased monocytes, M4/M5 French-American-British classification, and CD14+ immunophenotype tracked with sensitivity to DORA and resistance to VEN but were mitigated with the combination. Increased expression of MAPK14 and BCL2, the respective primary targets of DORA and VEN, were observed in monocytic and undifferentiated leukemias, respectively. Enrichment for DORA and VEN sensitivities was observed in AML with monocyte-like and progenitor-like transcriptomic signatures, respectively, and these associations diminished with the combination. The mechanism underlying the combination’s enhanced efficacy may result from inhibition of p38 MAPK-mediated phosphorylation of BCL2, which in turn enhances sensitivity to VEN. These findings suggest exploiting complementary drug sensitivity profiles with respect to leukemic differentiation state, such as dual targeting of p38 MAPK and BCL2, offers opportunity for broad, enhanced efficacy across the clinically challenging heterogeneous landscape of AML.

Decades before its clinical onset, epigenetic changes start to accumulate in the progenitor cells of Acute Myelogenous Leukemia (AML). Delineating these changes can improve risk-stratification for patients and shed insights into AML etiology, dynamics and mechanisms. Towards this goal, we extracted “epigenetic signatures” through two parallel machine learning approaches: a supervised regression model using frequently mutated genes as labels and an unsupervised topic modeling approach to factorize covarying epigenetic changes into a small number of “topics”. First, we created regression models forDNMT3AandTET2, the two most frequently mutated epigenetic drivers in AML. Our model differentiated wild-type vs. mutant genotypes based on their downstream epigenetic impacts with very high accuracy: AUROC 0.9 and 0.8, respectively. Methylation loci frequently selected by the models recapitulated known downstream pathways and identified several novel recurrent targets. Second, we used topic modeling to systematically factorize the high dimensional methylation profiles to a latent space of 15 topics. We annotated identified topics with biological and clinical features such as mutation status, prior malignancy and ELN criteria. Topic modeling successfully deconvoluted the combined effects of multiple upstream epigenetic drivers into individual topics including relatively infrequent cytogenetic events, improving the methylation-based subtyping of AML. Furthermore, they revealed complimentary and synergistic interactions between drivers, grouped them based on the similarity of their downstream methylation impact and linked them to prognostic criteria. Our models identify new signatures and methylation pathways, refine risk-stratification and inform detection and drug response studies for AML patients.KEY POINTSSupervised and unsupervised models reveal new methylation pathways of AML driver events and validate previously known associations.IndividualDNMT3AandTET2signatures are accurate and robust, despite the complex genetic and epigenetic make-up of samples at diagnosis.Unsupervised topic modeling factorizes covarying methylation changes and isolates methylation signatures caused by rare mutations.Topic modeling reveals a group of mutations with similar downstream methylation impacts and mapped to adverse-risk class by ELN.Topic modeling uncovers methylation signatures of infrequent cytogenetic events, significantly improving methylation-based subtyping.Our models can be leveraged to build predictive models for AML-risk.Our models show that cytogenetic events, such as t(15;17) have widespreadtransdownstream methylation impacts.

Purpose: Targeted therapeutics are a goal of medicine. Methods for targeting T-cell lymphoma lack specificity for the malignant cell, leading to elimination of healthy cells. The T-cell receptor (TCR) is designed for antigen recognition. T-cell malignancies expand from a single clone which expresses one of 48 TCR variable beta (Vβ) genes, providing a distinct therapeutic target. We hypothesized that a monoclonal antibody that is exclusive to a specific Vβ would eliminate the malignant clone while having minimal effects on healthy T-cells. Experimental Design: We identified a patient with large granular T-cell leukemia, and sequenced his circulating T-cell population, 95% of which expressed Vβ13.3. We developed a panel of anti-Vβ13.3 antibodies to test for binding and elimination of the malignant T-cell clone. Results: Therapeutic antibody candidates bound the malignant clone with high affinity. Antibodies killed engineered cell lines expressing the patient TCR Vβ13.3 by antibody-dependent cellular cytotoxicity, TCR-mediated activation-induced cell death, and exhibited specific killing of patient malignant T-cells in combination with exogenous NK cells. EL4 cells expressing the patient’s TCR Vβ13.3 were also killed by antibody administration in an in vivo murine model. Conclusions: This approach serves as an outline for development of therapeutics that can treat clonal T-cell based malignancies and potentially other T-cell-mediated diseases.

Normalized Read Pileup and Differential Enrichment Clusters of MOLM13 H3K27Ac CUT&Tag (2 Hours After Drug Treatment)

STAT5 and MYC play a key role in the response to FLT3/LSD1 inhibition.

RNA PolII Pause Index Analysis from MOLM13 RBP1 CUT&Tag (6 Hours After Drug Treatment)

GO Analysis from Regions of Differential MOLM13 H3K27Ac CUT&Tag (6 Hours After Drug Treatment) Pileup at Enhancers and Promoters

Supplementary Materials and Methods

Log2 Normalized Counts of Differentially Expressed Genes from Primary AML Blast Bulk RNA-seq (24 Hours After Drug Treatment)

Stat5 over-expression diminishes the efficacy of combined FLT3/LSD1 inhibition.

Single cell ATAC-seq cell population dynamics in primary patient samples follow-ing treatment with dual FLT3/LSD1 inhibition.

Combined FLT3/LSD1 inhibition does not substantially alter myeloid differentiation.

Differential Phosphoprotein Network Enrichment in MOLM13 Cells (24 Hours After Drug Treatment)

Efficacy of dual FLT3/LSD1 inhibition in MOLM13 cells.

Normalized Read Pileup and Differential Enrichment Clusters of MOLM13 H3K27Ac CUT&Tag (6 Hours After Drug Treatment) at Enhancers and Promoters

Epigenetic impact of dual FLT3/LSD1 inhibition.

SPI1 knockdown does not disrupt dual FLT3/LSD1 inhibition synergy.

Dual FLT3/LSD1 inhibition suppresses MYC expression and activity.

Mutations in Fms-like tyrosine kinase 3 (FLT3) are common drivers in acute myeloid leukemia (AML) yet FLT3 inhibitors only provide modest clinical benefit. Prior work has shown that inhibitors of lysine-specific demethylase 1 (LSD1) enhance kinase inhibitor activity in AML. Here we show that combined LSD1 and FLT3 inhibition induces synergistic cell death in FLT3-mutant AML. Multi-omic profiling revealed that the drug combination disrupts STAT5, LSD1, and GFI1 binding at the MYC blood superenhancer, suppressing superenhancer accessibility as well as MYC expression and activity. The drug combination simultaneously results in the accumulation of repressive H3K9me1 methylation, an LSD1 substrate, at MYC target genes. We validated these findings in 72 primary AML samples with the nearly every sample demonstrating synergistic responses to the drug combination. Collectively, these studies reveal how epigenetic therapies augment the activity of kinase inhibitors in FLT3-ITD (internal tandem duplication) AML.Implications:This work establishes the synergistic efficacy of combined FLT3 and LSD1 inhibition in FLT3-ITD AML by disrupting STAT5 and GFI1 binding at the MYC blood-specific superenhancer complex.

Transcription Factor Enrichment Analysis of Differentially Downregulated Genes by The Drug Combination Previously Identified as Depleting Genes in a Genome-Wide CRISPR Dropout Screen

Transcription Factor Enrichment Analysis of MOLM13 Bulk RNA-Seq (24 Hours After Drug Treatment)

Transcription Factor Enrichment Analysis of Differentially Downregulated Genes by The Drug Combination Previously Identified as Depleting Genes in a Genome-Wide CRISPR Dropout Screen

Response to entrectinib on AML patient samples with TP53 mutations in vivo and expression of RUNX1 isoforms in AML patient samples and MOLM13 parental and CRISPR modified cells.

XLSX file - 24K, PCR Primer sequences.

Complete methods

PDF file - 113K, Bcl2 protein expression in wild-type and Tyk2 knockout mice.

PDF file - 74K, Knockdown efficiency and BCL2 expression in JURKAT cells.

Dose response curve analyses

XLSX file - 525K, Summary of inducible shRNA screen on three T-ALL cell lines.

PDF file - 138K

Confirmation of CRISPR KOs in MOLM13, MV4;11 and OCI-AML2 cells and additional apoptotic protein profiles in MOLM13 cells.

Supplementary Table from Luxeptinib (CG-806) Targets FLT3 and Clusters of Kinases Operative in Acute Myeloid Leukemia

PDF file - 32K, Expressions of IL-10 and IL-10 receptors, and effect of an anti-IL10 neutralizing antibody on growth of T-ALL cell lines.