CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny.

BACKGROUND: The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. RESULTS: We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. CONCLUSIONS: CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Predicting transcription factor activity using prior biological information.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

Multicancer Early Detection Tests: An Overview of Early Results From Prospective Clinical Studies and Opportunities for Oncologists.

A multitude of blood-based multicancer early detection (MCED) tests assessing cancer-related alterations in circulating genomic analytes and other associated signatures are currently being developed with the potential to disrupt current single-organ screening paradigms. Pathways for clinical implementation of these novel MCED tests have not been delineated, particularly for the patients with signal positive results requiring additional confirmatory testing. In this overview, we highlight early results from prospective clinical studies testing the efficacy of genomic MCED tests in cohorts of patients without known cancer diagnoses. Additionally, we discuss a proposed professional expansion of the oncology practice relating to the diagnostic workup of individuals found to have an MCED signal positive for cancer. As MCED blood tests have the potential to dramatically upend current cancer screening paradigms and downstream cancer therapy, it is imperative for oncologists to be aware of important clinical studies and the multitude of unanswered questions. The current gaps in the clinical implication of these tests may serve as a meaningful and rewarding expansion of oncology practice.

Systematic benchmarking of single-cell ATAC-sequencing protocols.

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.

sciMET-cap: High-throughput single-cell methylation analysis with a reduced sequencing burden.

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Sufficient off-target coverage further enables the production of near-complete methylomes for individual cell types. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Disruption of the MYC Superenhancer Complex by Dual Targeting of FLT3 and LSD1 in Acute Myeloid Leukemia.

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.

Asxl1 deletion disrupts MYC and RNA polymerase II function in granulocyte progenitors.

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.

Integrative analysis of drug response and clinical outcome in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

GoPeaks: histone modification peak calling for CUT&Tag.

Genome-wide mapping of histone modifications is critical to understanding transcriptional regulation. CUT&Tag is a new method for profiling histone modifications, offering improved sensitivity and decreased cost compared with ChIP-seq. Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. We compare the performance of GoPeaks against commonly used peak calling algorithms to detect histone modifications that display a range of peak profiles and are frequently used in epigenetic studies. We find that GoPeaks robustly detects genome-wide histone modifications and, notably, identifies a substantial number of H3K27ac peaks with improved sensitivity compared to other standard algorithms.

PU.1 and MYC transcriptional network defines synergistic drug responses to KIT and LSD1 inhibition in acute myeloid leukemia.

Responses to kinase-inhibitor therapy in AML are frequently short-lived due to the rapid development of resistance, limiting the clinical efficacy. Combination therapy may improve initial therapeutic responses by targeting pathways used by leukemia cells to escape monotherapy. Here we report that combined inhibition of KIT and lysine-specific demethylase 1 (LSD1) produces synergistic cell death in KIT-mutant AML cell lines and primary patient samples. This drug combination evicts both MYC and PU.1 from chromatin driving cell cycle exit. Using a live cell biosensor for AKT activity, we identify early adaptive changes in kinase signaling following KIT inhibition that are reversed with the addition of LSD1 inhibitor via modulation of the GSK3a/b axis. Multi-omic analyses, including scRNA-seq, ATAC-seq and CUT&Tag, confirm these mechanisms in primary KIT-mutant AML. Collectively, this work provides rational for a clinical trial to assess the efficacy of KIT and LSD1 inhibition in patients with KIT-mutant AML.

Mutated SETBP1 activates transcription of Myc programs to accelerate CSF3R-driven myeloproliferative neoplasms.

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.

Mutant SETBP1 enhances NRAS-driven MAPK pathway activation to promote aggressive leukemia.

Mutations in SET-binding protein 1 (SETBP1) are associated with poor outcomes in myeloid leukemias. In the Ras-driven leukemia, juvenile myelomonocytic leukemia, SETBP1 mutations are enriched in relapsed disease. While some mechanisms for SETBP1-driven oncogenesis have been established, it remains unclear how SETBP1 specifically modulates the biology of Ras-driven leukemias. In this study, we found that when co-expressed with Ras pathway mutations, SETBP1 promoted oncogenic transformation of murine bone marrow in vitro and aggressive myeloid leukemia in vivo. We demonstrate that SETBP1 enhances the NRAS gene expression signature, driving upregulation of mitogen-activated protein kinase (MAPK) signaling and downregulation of differentiation pathways. SETBP1 also enhances NRAS-driven phosphorylation of MAPK proteins. Cells expressing NRAS and SETBP1 are sensitive to inhibitors of the MAPK pathway, and treatment with the MEK inhibitor trametinib conferred a survival benefit in a mouse model of NRAS/SETBP1-mutant disease. Our data demonstrate that despite driving enhanced MAPK signaling, SETBP1-mutant cells remain susceptible to trametinib in vitro and in vivo, providing encouraging preclinical data for the use of trametinib in SETBP1-mutant disease.

An expanded universe of cancer targets.

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.

Outgrowth of a CSF3R-mutant clone drives a second myeloproliferative neoplasm in a chronic myeloid leukemia patient: a case report.

BACKGROUND: Chronic myeloid leukemia (CML) and chronic neutrophilic leukemia (CNL) are two myeloproliferative neoplasms with mutually exclusive diagnostic criteria. A hallmark of CML is the Philadelphia chromosome (Ph), which results in a BCR-ABL1 fusion gene and constitutive tyrosine kinase activity. CNL is a Ph-negative neoplasm and is defined in part by the presence of CSF3R mutations, which drive constative JAK/STAT signaling. CASE PRESENTATION: Here, we report the exceedingly rare co-occurrence of two granulocytic myeloproliferative neoplasms in a 69-year old male patient. After an initial diagnosis of chronic myeloid leukemia, the patient's clinical course was shaped by hematologic toxicity, the emergence of treatment-resistant BCR-ABL1 clones, and the expansion of a CSF3R-mutant clone without ABL1 mutations under selective pressure from tyrosine kinase inhibitors. The emergence of the CSF3R-mutant, neutrophilic clone led to the diagnosis of CNL as a second myeloproliferative neoplasm in the same patient. CONCLUSIONS: This is the first reported case of CNL arising subsequent to CML, which occurred under selective pressure from targeted therapy in a patient with complex clonal architecture. Patients with such molecularly complex disease may ultimately benefit from combination therapy that targets multiple oncogenic pathways.

Navigating Challenges in Monitoring Chronic Myeloid Leukemia with Multiple BCR-ABL1 Transcripts.

Quantitative PCR-based strategies are typically effective for monitoring BCR-ABL1 transcript levels in chronic myeloid leukemia (CML). Additionally, some patients treated with tyrosine kinase inhibitors can experience long-term treatment-free remission after discontinuation of the inhibitor. However, this outcome hinges on effectively monitoring the patient's response to therapy. We present a patient with CML and multiple BCR-ABL1 transcripts, including a rare isoform that lacks qPCR standardization. We describe unexpected discrepancies in transcript quantification, further having an impact on clinical decision-making regarding duration of treatment. To better inform clinical practice, we suggest monitoring patients at the same testing facility to better track transcript trend.