Citations

The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.

e17535 Background: High-grade serous ovarian carcinoma (HGSOC) is the most frequent and deadliest type of ovarian cancer (OC). With the recent advances in transcriptomic and epigenomic profiling of cancer at single cell resolution, it has become clear that intra-tumor heterogeneity driven by genetic and epigenetic factors may be the basis of drug resistance. Current treatments are not homogeneously effective against all the cancer cell subpopulations, thus enabling resistance. With single cell sequencing technology, characterization of cell signatures and regulatory mechanisms may translate to prognostic markers and novel drug targets. Methods: The transcriptomic and epigenomic landscape of HGSOC was mapped for five tumor samples from debulking surgeries and one tissue patient-derived xenograft (PDX) using the 10X genomics sequencing platform. This strategy permits the combination of single cell ATAC-seq and single cell RNA-seq within the same nucleus (Multiome). Isolation of nuclei, library preparation and sequencing were performed following 10X genomics protocols. Sample quality and library quality were assessed by nuclear morphology and fragment size analysis, respectively. Sequenced reads were mapped to the human genome and downstream analysis including copy number variant prediction, pathway enrichment, motif enrichment and gene regulatory network analysis were performed. The sampled patients continue to be followed for clinical outcomes, such as response to therapy. Clinical data including patient age, grade, and stage of cancer, debulking status, CA-125 levels, and neoadjuvant status, were used in conjunction with the genomics data to characterize patient specific molecular regulatory signatures. Results: Cells from the six samples (total N= 26,421) were projected via UMAP. Immune and stromal cells were shown to cluster by cell-type while cancer cells clustered by patient. Cancer cells were identified as cycling cells, ciliated cells, cells with enrichment of the JAK-STAT signaling pathway, and cells exhibiting cancer stemness signatures. Transcription factor motifs and binding-site enrichment in open chromatin regions reveal transcription regulation-based subpopulations. Using the combined gene expression and open chromatin information from these cells, there is potential to uncover the genetic regulatory network(s) that drives treatment resistance. Conclusions: With single cell technology, specific clusters of cancer and tumor microenvironment cells can be classified. Beyond characterizing patient specific signatures, multiome enables the discovery of genomic, transcriptomic and epigenomic signatures that provide insight in tumor progression and treatment resistance.

Abstract Purpose: Preclinical studies in myeloid neoplasms have demonstrated efficacy of bromodomain and extra-terminal protein inhibitors (BETi). However, BETi demonstrates poor single-agent activity in clinical trials. Several studies suggest that combination with other anticancer inhibitors may enhance the efficacy of BETi. Experimental Design: To nominate BETi combination therapies for myeloid neoplasms, we used a chemical screen with therapies currently in clinical cancer development and validated this screen using a panel of myeloid cell line, heterotopic cell line models, and patient-derived xenograft models of disease. We used standard protein and RNA assays to determine the mechanism responsible for synergy in our disease models. Results: We identified PIM inhibitors (PIMi) as therapeutically synergistic with BETi in myeloid leukemia models. Mechanistically, we show that PIM kinase is increased after BETi treatment, and that PIM kinase upregulation is sufficient to induce persistence to BETi and sensitize cells to PIMi. Furthermore, we demonstrate that miR-33a downregulation is the underlying mechanism driving PIM1 upregulation. We also show that GM-CSF hypersensitivity, a hallmark of chronic myelomonocytic leukemia (CMML), represents a molecular signature for sensitivity to combination therapy. Conclusions: Inhibition of PIM kinases is a potential novel strategy for overcoming BETi persistence in myeloid neoplasms. Our data support further clinical investigation of this combination.

Abstract Ovarian cancer is one of the most common gynecologic malignancies, with high-grade serous carcinoma (HGSC) as the most frequent and deadliest type of ovarian cancer (OC). With the recent advances in cancer epigenomic profiling, it has become clear that intra-tumor heterogeneity driven by genetic and epigenetic factors may be the basis of resistance, where current treatments could efficiently target sub-populations of cells while a sub-group may be resistant, or prone to develop resistance. With epigenomic profiling and single cell sequencing technology, we aim to characterize cell signatures that translate as prognostic markers and novel drug targets. We have mapped the epigenomic landscape of HGSC in 12 patient-derived xenografts (PDX), and their metastatic counterparts (circulating tumor cells and ascites present in the PDX models) using an assay for transposase-accessible chromatin (or ATAC-seq) and compared it with normal fallopian tube organoids to identify tumor and metastatic-specific regulatory elements. We also performed RNA-seq to validate potential transcriptional dependencies associated with resistance to chemotherapy. In order to identify potentially chemotherapy-resistant subpopulations and further validate the activity of some of the transcription factors that can be driving resistance, we performed single cell multiomic profiling using the 10x Multiome sequencing platform (scRNA+scATAC from the same nuclei) in HGSC patient samples and PDX. Finally, we performed immunohistochemistry of patient tissue slides for target markers and correlated it with overall survival in OC patients. As previously suggested, a subset of cancer clonal cells can drive treatment resistance and metastasis and result in poor patient outcomes. Data derived from PDX ATAC-seq showed a cancer stem cell signature driven by SOX2 in metastatic samples compared to the paired PDX primary tumor, suggesting a role for SOX2 in disease progression. Interestingly, SOX2 expression in epithelial cells in the Multiome analysis is more patient-specific than other stem cell markers, like CD133. We also observed that SOX2 expression negatively correlated with prognosis. These data, taken together, point out the promising role of SOX2 as a prognostic and targetable marker for HGSC. Citation Format: Leticia Assad Maia Sandoval, Wazim Mohammed Ismail, Amelia Mazzone, Amik Munankarmy, Jagneet Kaur, Tsering Tashi, Xiaonan Hou, Nagarajan Kannan, Ann Oberg, Scott H. Kaufmann, Saravut John Weroha, Alexandre Gaspar-Maia. Single cell multiomic profiling of high-grade serous carcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4724.

Abstract Background: COVID-19 causes significant morbidity and mortality, albeit with considerable heterogeneity among affected individuals. It remains unclear which host factors determine disease severity and survival. Given the propensity of clonal hematopoiesis (CH) to promote inflammation in healthy individuals, we investigated its effect on COVID-19 outcomes. Methods: We performed a multi-omics interrogation of the genome, epigenome, transcriptome, and proteome of peripheral blood mononuclear cells from COVID-19 patients (n=227). We obtained clinical data, laboratory studies, and survival outcomes. We determined CH status and TET2-related DNA methylation. We performed single-cell proteogenomics to understand clonal composition in relation to cell phenotype. We interrogated single-cell gene expression in isolation and in conjunction with DNA accessibility. We integrated these multi-omics data to understand the effect of CH on clonal composition, gene expression, methylation of cis-regulatory elements, and lineage commitment in COVID-19 patients. We performed shRNA knockdowns to validate the effect of one candidate transcription factor in myeloid cell lines. Results: The presence of CH was strongly associated with COVID-19 severity and all-cause mortality, independent of age (HR 3.48, 95% CI 1.45-8.36, p=0.005). Differential methylation of promoters and enhancers was prevalent in TET2-mutant, but not DNMT3A-mutant CH. TET2-mutant CH was associated with enhanced classical/intermediate monocytosis and single-cell proteogenomics confirmed an enrichment of TET2 mutations in these cell types. We identified cell-type specific gene expression changes associated with TET2 mutations in 102,072 single cells (n=34). Single-cell RNA-seq confirmed the skewing of hematopoiesis towards classical and intermediate monocytes and demonstrated the downregulation of EGR1 (a transcription factor important for monocyte differentiation) along with up-regulation of the lncRNA MALAT1 in monocytes. Combined scRNA-/scATAC-seq in 43,160 single cells (n=18) confirmed the skewing of hematopoiesis and up-regulation of MALAT1 in monocytes along with decreased accessibility of EGR1 motifs in known cis-regulatory elements. Using myeloid cell lines for functional validation, shRNA knockdowns of EGR1 confirmed the up-regulation of MALAT1 (in comparison to wildtype controls). Conclusions: CH is an independent prognostic factor in COVID-19 and skews hematopoiesis towards monocytosis. TET2-mutant CH is characterized by differential methylation and accessibility of enhancers binding myeloid transcriptions factors including EGR1. The ensuing loss of EGR1 expression in monocytes causes MALAT1 overexpression, a factor known to promote monocyte differentiation and inflammation. These data provide a mechanistic insight to the adverse prognostic impact of CH in COVID-19. Citation Format: Moritz Binder, Terra L. Lasho, Wazim Mohammed Ismail, Nana A. Ben-Crentsil, Jenna A. Fernandez, Minsuk Kim, Susan M. Geyer, Amelia Mazzone, Christy M. Finke, Abhishek A. Mangaonkar, Jeong-Heon Lee, Kwan Hyun Kim, Vernadette A. Simon, Fariborz Rakhshan Rohakthar, Amik Munankarmy, Susan M. Schwager, Jonathan J. Harrington, Melissa R. Snyder, Nathalie M. Droin, Eric Solary, Keith D. Robertson, Eric D. Wieben, Eric Padron, Nicholas Chia, Alexandre Gaspar-Maia, Mrinal M. Patnaik. Enhancer deregulation inTET2-mutant clonal hematopoiesis is associated with increased COVID-19 severity and mortality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 806.

Dysregulation of kinase signaling pathways favors tumor cell survival and therapy resistance in cancer. Here, we reveal a posttranslational regulation of kinase signaling and nuclear receptor activity via deubiquitination in T cell acute lymphoblastic leukemia (T-ALL). We observed that the ubiquitin-specific protease 11 (USP11) is highly expressed and associates with poor prognosis in T-ALL. USP11 ablation inhibits leukemia progression in vivo, sparing normal hematopoiesis. USP11 forms a complex with USP7 to deubiquitinate the oncogenic lymphocyte cell–specific protein-tyrosine kinase (LCK) and enhance its activity. Impairment of LCK activity leads to increased glucocorticoid receptor (GR) expression and glucocorticoids sensitivity. Genetic knockout of USP7 improved the antileukemic efficacy of glucocorticoids in vivo. The transcriptional activation of GR target genes is orchestrated by the deubiquitinase activity and mediated via an increase in enhancer-promoter interaction intensity. Our data unveil how dysregulated deubiquitination controls leukemia survival and drug resistance, suggesting previously unidentified therapeutic combinations toward targeting leukemia.

Abstract Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were ‘unmasked’ only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

Time-restricted feeding improves glucose homeostasis through epigenetic control of pancreatic β cell function.

Abstract Background: SARS-Cov-2 infections are associated with increased mortality and morbidity, largely due to inflammatory cascades and cytokine release syndrome (CRS). Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence and subsequent expansion of somatic, leukemia-associated driver mutations in apparently healthy individuals with normal blood counts. CHIP has been associated with increased inflammation, with cytokines such as IL1-b, IL6 and TNF-a being elevated at baseline in affected individuals. We hypothesized that the presence of CHIP in patients with COVID-19 would result in excessive inflammation-related mortality and morbidity. Methods: We used the Mayo Clinic COVID-19 database to identify patients with COVID-19 on whom peripheral blood mononuclear cells (PBMC) were available for research use (IRB approved). We carried out target-capture next generation sequencing for 220 CH related genes, by previously described methods (1000 x coverage, variant allele fraction/VAF detection limit >0.5%; Kusne Y et al AJH 2021). CHIP was defined by the presence of a CH mutation with a VAF>1% in an individual with normal baseline blood counts. Demographics, blood counts, and inflammatory markers (CRP and cytokine levels- ELISA assay) at COVID-19 diagnosis and during follow-up (as clinically indicated) were collected. COVID-19 disease severity was classified based on the presence and severity of CRS, graded using the Penn Grading Scale (Porter et. al., 2018), and the WHO ordinal scale (WHO Blueprint, 2020). We used Fisher's exact test and the Wilcoxon rank sum test to compare categorical and continuous variables. Survival analysis was performed using the Kaplan-Meier method. We accounted for differences in age and sex using multivariable-adjusted proportional hazards regression models. Results: Seventy-two CHIP mutations were detected in 56 (25%) of the 227 patients with COVID-19 that had PBMC available; median age 69 years (range; 42-99 years), 61% male. Fifteen (26%) patients had 2 CHIP mutations, while 1 patient had 3 CHIP mutations. Common mutations encountered included DNMT3A (32%) , TET2 (19%) , SF3B1 (8%) , ASXL1 (6%) , MPL (5%) , and TP53 (5%; Figure 1A). COVID-19 patients with CHIP were older in age (median 69 vs 57 years; p<0.0001) and had higher baseline MCP-1 (p=0.04) levels. However, there were no differences in sex, comorbidities, blood counts, IL1-b, IL6 and TNF-a levels between the two groups. The median follow-up for the entire cohort was 9 months. The relative change from baseline in blood counts and inflammatory markers (CRP and cytokines) during follow-up was similar in CHIP and non-CHIP patients, with the exception that COVID-19-onset neutropenia was more common in CHIP patients (8% vs 1%; p=0.017) compared to those without CHIP. At last follow up neutropenia had resolved in all patients. Both groups had comparable number of patients with CRS (61% CHIP vs 53% non-CHIP patients, p=0.354, Figure 1B), however, CHIP patients had more severe CRS (median Penn Grade 3 versus 2 in non-CHIP, p=0.018, Figure 1C). Based on the WHO ordinal scale, CHIP patients were more likely to experience hospitalization with severe disease and death (61% versus 45% in non-CHIP, p = 0.049). Moreover, COVID-19 CHIP patients experienced worse overall survival in comparison to patients without CHIP (median 13.1 months vs not reached, p<0.001, Figure 1D). This association remained consistent after adjusting for age and sex at the time of COVID-19 diagnosis (HR 3.15, 95% 1.32 - 7.55, p = 0.010). At last follow-up, 22 deaths were documented: 13 (23%) in patients with CHIP and 9 (5%) in the non-CHIP group (p=0.02), with the primary cause for mortality being hypoxic respiratory failure (62% in CHIP vs 44% non-CHIP, p=0.04). Conclusions: In this study, we observe an age-independent impact of CHIP on COVID-19 associated inflammatory morbidity (CRS) and mortality (hypoxemic respiratory failure). We are currently carrying out detailed single cell (ssDNA, RNA and ATAC-seq) and proteomic studies (O-link PEA assays) to better elucidate this pathophysiology. Figure 1 Figure 1. Disclosures Patnaik: Kura Oncology: Research Funding; StemLine: Research Funding.