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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.

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.

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.

Dual FLT3/LSD1 inhibition suppresses MYC expression and activity.

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

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.

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

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (‘dark’) proteins from analyzed datasets in the context of Reactome’s manually curated pathways.

Transcription factors are critical regulators of cellular gene expression programs. Disruption of normal transcription factor regulation is associated with a broad range of diseases. In order to understand the mechanisms that underly disease pathogenesis, it is critical to detect aberrant transcription factor activity. We have developed Priori, a computational method to predict transcription factor activity from RNA sequencing data. Priori has several key advantages over existing methods. Priori utilizes literature-supported regulatory relationship information to identify known transcription factor target genes. Using these transcriptional relationships, Priori uses linear models to determine the impact and direction of transcription factor regulation on the expression of its target genes. In our work, we evaluated the ability of Priori and 16 other methods to detect aberrant activity from 124 single-gene perturbation experiments. We show that Priori identifies perturbed transcription factors with greater sensitivity and specificity than other methods. Furthermore, our work demonstrates that Priori can be used to discover significant determinants of survival in breast cancer as well as identify mediators of drug response in leukemia from primary patient samples.

Extracellular vesicles (EVs) have been shown as key mediators of extracellular small RNA transport. However, carriers of cell-free messenger RNA (cf-mRNA) in human biofluid and their association with cancer remain poorly understood. Here, we performed a transcriptomic analysis of size-fractionated plasma from lung cancer, liver cancer, multiple myeloma, and healthy donors. Morphology and size distribution analysis showed the successful separation of medium and small EVs and non-vesicular carriers. We developed a strategy to purify and sequence ultra-low amounts of cf-mRNA from vesicular and non-vesicular subpopulations with the implementation of RNA spike-ins to control for technical variability and to normalize for intrinsic drastic differences in the amount of cf-mRNA carried in each plasma fraction. We found that the majority of cf-mRNA was enriched and protected in EVs with remarkable stability in RNase-rich environments. We observed specific enrichment patterns of cancer-associated cf-mRNA in each vesicular and non-vesicular subpopulation. The EV-enriched differentiating genes were associated with specific biological pathways, such as immune systems, liver function, and toxic substance regulation in lung cancer, liver cancer, and multiple myeloma, respectively. Our results suggest that dissecting the complexity of EVs subpopulations illuminates their biological significance and offers a promising liquid biopsy approach.

Decades before its clinical onset, epigenetic changes start to accumulate in the progenitor cells of Acute Myelogenous Leukemia– caused by tumor intrinsic driver mutations and tumor extrinsic environmental factors. There are millions of cancer-associated epigenetic changes that are persistent and stable throughout the leukemogenesis and at relapse. By grouping correlated loci into “epigenetic signatures”, we can gain insights on the AML etiology, dynamics as well as mechanisms around the methylation pathways of driver events. We approached this problem in two parts: First, we computationally constructed and analyzed methylation signatures that are associated with DNMT3A and TET2 mutations. Both of them are frequently mutated in AML and they are major causes of changes in the methylation landscape of healthy individuals, conferring a broad risk of transformation to a myeloid malignancy with the serial acquisition of an additional cooperating mutation. We built highly accurate ridge regression models that can differentiate the wild type vs mutant genotypes for DNMT3A and TET2 based on their downstream epigenetic impacts (AUROC 0.9 and 0.8, respectively). It suggests that despite the complicated genetic and epigenetic make-up of post-diagnosis samples, epigenetic regulator mutations’ signatures are distinguishable, and these distinctive marks remain stable over time. We further evaluated methylation loci selected by the models and showed that known downstream targets of DNMT3A such as EVI1/MECOM and AEBP2 (polycomb repressive complex member) were frequently selected by the models. We also identified multiple novel recurrent sites that were previously unassociated with DNMT3A and TET2. Second, we modeled different sources of variations in epigenetic patterns with Topic Modeling. Topic Modeling maps the sparse and high dimensional space of methylation profiles to a small and non-sparse latent space of small number of factors (topics). Each sample is represented as a combination of few topics in that latent space. We annotated these topics with biological and clinical features such as mutation status, prior malignancy, age and gender to shed light on the prevalence and combination of the mutations and other extrinsic factors present at the time of diagnosis. In the future, we aim to identify the etiologies of new signatures to inform detection, monitoring, risk stratifying and drug response studies for AML patients and track these changes throughout leukemogenesis in early detection cohorts. Citation Format: Burcu Gurun, Jeffrey W. Tyner, Emek Demir, Brian J. Druker, Paul T. Spellman. Computational modeling of methylation impact of AML driver events [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A15.

Heme oxygenase-1 (HO-1) is a stress-induced enzyme that catalyzes the breakdown of heme into biliverdin, carbon monoxide, and iron. Targeting HO-1 to treat severe COVID-19 has been suggested by several groups, yet the role of HO-1 in SARS-CoV-2 infection remains unclear. Based on this, we aimed to investigate the antiviral activity of Hemin, an activator of HO-1. Infectivity of SARS-CoV-2 was decreased in Vero E6 cells treated with Hemin. Hemin also decreased TMPRSS2 and ACE2 mRNA levels in non-infected cells, possibly explaining the observed decrease in infectivity. TMPRSS2 protein expression and proteolytic activity were decreased in Vero E6 cells treated with Hemin. Besides that, experimental studies supported with in silico calculations. Overall, our study supports further exploration of Hemin as a potential antiviral and inflammatory drug for the treatment of COVID-19.

Background and AimsMetastatic pancreatic adenocarcinoma (mPDAC) is lethal, yet a subset of patients who have metastatic disease that spreads only to the lung have better outcomes. We identified unique transcriptomic and immune features that distinguish patients who develop metastases in the liver (liver cohort) versus those with lung-avid but liver-averse mPDAC (lung cohort).MethodsWe used clinical data from the Oregon Pancreas Tissue Registry to identify PDAC patients with liver and/or lung metastases. Gene expression and genomic alteration data from 290 PDAC tumors were used to identify features unique to patients from the liver and lung cohorts. In parallel, T cell receptor sequencing data from 289 patients were used to identify immune features unique to patients in the lung cohort.ResultsLung cohort patients had better survival outcomes than liver cohort patients. Primary tumors from patients in the liver cohort expressed a novel gene signature associated with ongoing replication stress (RS) response predictive of poor patient outcome independent from known subtypes. In contrast, patients with tumors lacking the RS response signature survived longer, especially if their tumors had alterations in DNA damage repair genes. A subset of patients in the lung cohort demonstrated new T cell clonal development in their primary and metastatic tumors leading to diverse peripheral blood TCR repertoires.ConclusionLiver-avid metastatic PDAC is associated with an ongoing RS response, whereas tumors lacking the RS response with ongoing T cell clonal responses may have unique vulnerabilities allowing long-term survival in patients with lung-avid, liver-averse metastatic PDAC.

The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.