An assessment of compositional methods for the analysis of DNA methylation-based deconvolution estimates.

DNA methylation (DNAm)-based deconvolution estimates contain relative data, forming a composition, that standard methods (testing directly on cell proportions) are ill-suited to handle. In this study we examined the performance of an alternative method, analysis of compositions of microbiomes (ANCOM), for the analysis of DNAm-based deconvolution estimates. We performed two different simulation studies comparing ANCOM to a standard approach (two sample t-test performed directly on cell proportions) and analyzed a real-world data from the Women's Health Initiative to evaluate the applicability of ANCOM to DNAm-based deconvolution estimates. Our findings indicate that ANCOM can effectively account for the compositional nature of DNAm-based deconvolution estimates. ANCOM adequately controls the false discovery rate while maintaining statistical power comparable to that of standard methods.

DNA methylation (DNAm)-based deconvolution provides highly accurate estimates of the proportion of each cell type in a mixed-cell type biological sample (e.g., whole-blood). These estimates can be used for examining the association between cell type proportions and biological or clinical end points; for example, comparing the estimated neutrophil proportion in whole blood between smokers and non-smokers. Cell proportion data has unique features which present challenges for traditional and widely used statistical methods. In response to this issue, our work presents two simulation studies and a real-world analysis that benchmark the performance of current standard statistical methods against an alternative method called analysis composition of microbes (ANCOM), which was originally developed for the analysis of microbiome data. In our real-world analysis we used DNAm data collected from Women's Health Initiative Long Life Study I and compared the results of each method against a gold-standard that is typically not available for these analyses. In each of our simulation studies, ANCOM was able to detect true differences in cell proportions between the groups being compared but had a much lower rate of false discovery compared with the standard statistical methods. Our real-world analysis demonstrated similar findings. Overall, our study highlights the potential of ANCOM as a powerful and robust method for analyzing DNAm-derived deconvolution estimates when the interest is comparisons of cell type proportions and biological or clinical end points. ANCOM's ability to minimize false discovery while maintaining robust statistical power positions it as a valuable addition to the epigenomic analysis toolkit.

Summer Cancer Research Experience for High School Students from Historically Marginalized Populations in Kansas City.

The Accelerate Cancer Education (ACE) summer research program at The University of Kansas Cancer Center (KUCC) is a six-week, cancer-focused, summer research experience for high school students from historically marginalized populations in the Kansas City metropolitan area. Cancer affects all populations and continues to be the second leading cause of death in the United States, and a large number of disparities impact racial and ethnic minorities, including increased cancer incidence and mortality. Critically, strategies to bolster diversity, equity, inclusion, and accessibility are needed to address persistent cancer disparities. The ACE program offers an educational opportunity for a population of students who otherwise would not have easy access onto a medical center campus to make connections with cancer physicians and researchers and provides a vital response to the need for a more diverse and expansive oncology workforce. Students grow their technical, social, and professional skills and develop self-efficacy and long-lasting connections that help them matriculate and persist through post-secondary education. Developed in 2018, the ACE program has trained 37 high school junior and senior students. This article describes the need for and how we successfully developed and implemented the ACE program.

Neutrophil extracellular traps linked to idiopathic pulmonary fibrosis severity and survival.

Background: Idiopathic pulmonary fibrosis (IPF) leads to progressive loss of lung function and mortality. Understanding mechanisms and markers of lung injury in IPF is paramount to improving outcomes for these patients. Despite the lack of systemic involvement in IPF, many analyses focus on identifying circulating prognostic markers. Using a proteomic discovery method followed by ELISA validation in multiple IPF lung compartments and cohorts we explored novel markers of IPF survival. Methods: In our discovery analysis, agnostic label-free quantitative proteomics differentiated lung tissue protein expression based on survival trajectory (n=10). Following selection of the candidate pathway (neutrophil extracellular trap (NET) formation), we subsequently validated the presence of NETs in the IPF lung microenvironment using fully quantitative assays of known NET remnants in separate IPF cohorts (n=156 and n=52) with bronchoalveolar lavage fluid. We then assessed the correlation of these markers with baseline pulmonary function and survival. Results: Discovery lung tissue proteomics identified NET formation as significantly associated with poor IPF survival. Using fully quantitative confirmatory tests for reproducibility we confirmed the presence of NET markers in IPF BALF and found significant correlations with worse pulmonary function in both cohorts (p<0.03 and p = 0.04 respectively). In the survival cohort, higher levels of NET markers predicted worse survival after adjusting for gender, age, and baseline physiologic severity (hazard ratio range: 1.79-2.19). Conclusions: NET markers were associated with disease severity and worse survival in IPF. These findings suggest NET formation contributes to lung injury and decreased survival in IPF and may represent a potential therapeutic target.

optima: an open-source R package for the Tapestri platform for integrative single cell multiomics data analysis.

SUMMARY: The Tapestri platform offers DNA and protein analysis at the single-cell level. Integrating both types of data is beneficial for studying multiple cell populations in heterogeneous microenvironments, such as tumor tissues. Here, we present optima, an R package for the processing and analysis of data generated from the Tapestri platform. This package provides streamlined functionality for raw data filtering, integration, normalization, transformation, and visualization. Insights gained from the optima package help users to identify unique cell populations and uncover surface protein expression patterns. The results generated by optima help researchers elucidate dynamic changes at the single-cell level in heterogeneous microenvironments. AVAILABILITY AND IMPLEMENTATION: This package is available in Github: https://github.com/rachelgriffard/optima.

Assessing Breast Cancer Molecular Subtypes Using Extracellular Vesicles' mRNA.

Extracellular vesicles (EVs) carry RNA cargo that is believed to be associated with the cell-of-origin and thus have the potential to serve as a minimally invasive liquid biopsy marker for supplying molecular information to guide treatment decisions (i.e., precision medicine). We report the affinity isolation of EV subpopulations with monoclonal antibodies attached to the surface of a microfluidic chip that is made from a plastic to allow for high-scale production. The EV microfluidic affinity purification (EV-MAP) chip was used for the isolation of EVs sourced from two-orthogonal cell types and was demonstrated for its utility in a proof-of-concept application to provide molecular subtyping information for breast cancer patients. The orthogonal selection process better recapitulated the epithelial tumor microenvironment by isolating two subpopulations of EVs: EVEpCAM (epithelial cell adhesion molecule, epithelial origin) and EVFAPα (fibroblast activation protein α, mesenchymal origin). The EV-MAP provided recovery >80% with a specificity of 99 ± 1% based on exosomal mRNA (exo-mRNA) and real time-droplet digital polymerase chain reaction results. When selected from the plasma of healthy donors and breast cancer patients, EVs did not differ in size or total RNA mass for both markers. On average, 0.5 mL of plasma from breast cancer patients yielded ∼2.25 ng of total RNA for both EVEpCAM and EVFAPα, while in the case of cancer-free individuals, it yielded 0.8 and 1.25 ng of total RNA from EVEpCAM and EVFAPα, respectively. To assess the potential of these two EV subpopulations to provide molecular information for prognostication, we performed the PAM50 test (Prosigna) on exo-mRNA harvested from each EV subpopulation. Results suggested that EVEpCAM and EVFAPα exo-mRNA profiling using subsets of the PAM50 genes and a novel algorithm (i.e., exo-PAM50) generated 100% concordance with the tumor tissue.

Assessment of immune cell profiles among post-menopausal women in the Women's Health Initiative using DNA methylation-based methods.

BACKGROUND: Over the past decade, DNA methylation (DNAm)-based deconvolution methods that leverage cell-specific DNAm markers of immune cell types have been developed to provide accurate estimates of the proportions of leukocytes in peripheral blood. Immune cell phenotyping using DNAm markers, termed immunomethylomics or methylation cytometry, offers a solution for determining the body's immune cell landscape that does not require fresh blood and is scalable to large sample sizes. Despite significant advances in DNAm-based deconvolution, references at the population level are needed for clinical and research interpretation of these additional immune layers. Here we aim to provide some references for immune populations in a group of multi-ethnic post-menopausal American women. RESULTS: We applied DNAm-based deconvolution to a large sample of post-menopausal women enrolled in the Women's Health Initiative (baseline, N = 58) or the ancillary Long Life Study (WHI-LLS, N = 1237) to determine the reference ranges of 58 immune parameters, including proportions and absolute counts for 19 leukocyte subsets and 20 derived cell ratios. Participants were 50-94 years old at the time of blood draw, and N = 898 (69.3%) self-identified as White. Using linear regression models, we observed significant associations between age at blood draw and absolute counts and proportions of naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ memory, neutrophils, and natural killer cells. We also assessed the same immune profiles in a subset of paired longitudinal samples collected 14-18 years apart across N = 52 participants. Our results demonstrate high inter-individual variability in rates of change of leukocyte subsets over this time. And, when conducting paired t tests to test the difference in counts and proportions between the baseline visit and LLS visit, there were significant changes in naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ cells and neutrophils, similar to the results seen when analyzing the association with age in the entire cohort. CONCLUSIONS: Here, we show that derived cell counts largely reflect the immune profile associated with proportions and that these novel methods replicate the known immune profiles associated with age. Further, we demonstrate the value this methylation cytometry approach can add as a potential application in epidemiological studies.

Comprehensive exploration of JQ1 and GSK2801 targets in breast cancer using network pharmacology and molecular modeling approaches.

JQ1 and GSK2801 are bromo domain inhibitors (BDI) known to exhibit enhanced anti-cancer activity when combined with other agents. However, the underlying molecular mechanisms behind such enhanced activity remain unclear. We used network-pharmacology approaches to understand the shared molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 when used together to treat breast cancer (BC). The gene targets of JQ1 and GSK2801 were intersected with known BC-targets and their putative targets against BC were derived. The key genes were explored through gene-ontology-enrichment, Protein-Protein-Interaction (PPI) networking, survival analysis, and molecular modeling simulations. The genes, CTSB, MAPK14, MET, PSEN2 and STAT3, were found to be common targets for both drugs. In total, 49 biological processes, five molecular functions and 61 metabolic pathways were similarly enriched for JQ1 and GSK2801 BC targets among which several terms are related to cancer: IL-17, TNF and JAK-STAT signaling pathways. Survival analyses revealed that all five putative synergistic targets are significantly associated with survival in BC (log-rank p < 0.05). Molecular modeling studies showed stable binding of JQ1 and GSK2801 against their targets. In conclusion, this study explored and illuminated the possible molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 against BC and suggests synergistic action through their similar BC-targets and gene-ontologies.

Enrichment of a neutrophil-like monocyte transcriptional state in glioblastoma myeloid suppressor cells.

Glioblastomas (GBM) are lethal central nervous system cancers associated with tumor and systemic immunosuppression. Heterogeneous monocyte myeloid-derived suppressor cells (M-MDSC) are implicated in the altered immune response in GBM, but M-MDSC ontogeny and definitive phenotypic markers are unknown. Using single-cell transcriptomics, we revealed heterogeneity in blood M-MDSC from GBM subjects and an enrichment in a transcriptional state reminiscent of neutrophil-like monocytes (NeuMo), a newly described pathway of monopoiesis in mice. Human NeuMo gene expression and Neu-like deconvolution fraction algorithms were created to quantitate the enrichment of this transcriptional state in GBM subjects. NeuMo populations were also observed in M-MDSCs from lung and head and neck cancer subjects. Dexamethasone (DEX) and prednisone exposures increased the usage of Neu-like states, which were inversely associated with tumor purity and survival in isocitrate dehydrogenase wildtype (IDH WT) gliomas. Anti-inflammatory ZC3HA12/Regnase-1 transcripts were highly correlated with NeuMo expression in tumors and in blood M-MDSC from GBM, lung, and head and neck cancer subjects. Additional novel transcripts of immune-modulating proteins were identified. Collectively, these findings provide a framework for understanding the heterogeneity of M-MDSCs in GBM as cells with different clonal histories and may reshape approaches to study and therapeutically target these cells.

DNA methylation as a pharmacodynamic marker of glucocorticoid response and glioma survival.

Assessing individual responses to glucocorticoid drug therapies that compromise immune status and affect survival outcomes in neuro-oncology is a great challenge. Here we introduce a blood-based neutrophil dexamethasone methylation index (NDMI) that provides a measure of the epigenetic response of subjects to dexamethasone. This marker outperforms conventional approaches based on leukocyte composition as a marker of glucocorticoid response. The NDMI is associated with low CD4 T cells and the accumulation of monocytic myeloid-derived suppressor cells and also serves as prognostic factor in glioma survival. In a non-glioma population, the NDMI increases with a history of prednisone use. Therefore, it may also be informative in other conditions where glucocorticoids are employed. We conclude that DNA methylation remodeling within the peripheral immune compartment is a rich source of clinically relevant markers of glucocorticoid response.

A core of differentially methylated CpG loci in gMDSCs isolated from neonatal and adult sources.

BACKGROUND: Myeloid-derived suppressor cells (MDSCs), which include monocytic (mMDSCs) and granulocytic (gMDSCs) cells, are an immunosuppressive, heterogeneous population of cells upregulated in cancer and other pathologic conditions, in addition to normal conditions of stress. The origin of MDSCs is debated, and the regulatory pattern responsible for gMDSC differentiation remains unknown. Since DNA methylation (DNAm) contributes to lineage differentiation, we have investigated whether it contributes to the acquisition of the gMDSC phenotype. RESULTS: Using the Illumina EPIC array to measure DNAm of gMDSCs and neutrophils from diverse neonatal and adult blood sources, we found 189 differentially methylated CpGs between gMDSCs and neutrophils with a core of ten differentially methylated CpGs that were consistent across both sources of cells. Genes associated with these loci that are involved in immune responses include VCL, FATS, YAP1, KREMEN2, UBTF, MCC-1, and EFCC1. In two cancer patient groups that reflected those used to develop the methylation markers (head and neck squamous cell carcinoma (HNSCC) and glioma), all of the CpG loci were differentially methylated, reaching statistical significance in glioma cases and controls, while one was significantly different in the smaller HNSCC group. CONCLUSIONS: Our findings indicate that gMDSCs have a core of distinct DNAm alterations, informing future research on gMDSC differentiation and function.

Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples.

Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such "interaction-based" methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.

Evolutionary Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reveals Genomic Divergence with Implications for Universal Vaccine Efficacy.

Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the pressing contemporary public health challenges. Investigations into the genomic structure of SARS-CoV-2 may inform ongoing vaccine development efforts and/or provide insights into vaccine efficacy to fight against COVID-19. Evolutionary analysis of 540 genomes spanning 20 different countries/territories was conducted and revealed an increase in the genomic divergence across successive generations. The ancestor of the phylogeny was found to be the isolate from the 2019/2020 Wuhan outbreak. Its transmission was outlined across 20 countries/territories as per genomic similarity. Our results demonstrate faster evolving variations in the genomic structure of SARS-CoV-2 when compared to the isolates from early stages of the pandemic. Genomic alterations were predominantly located and mapped onto the reported vaccine candidates of structural genes, which are the main targets for vaccine candidates. S protein showed 34, N protein 25, E protein 2, and M protein 3 amino acid variations in 246 genomes among 540. Among identified mutations, 23 in S protein, 1 in E, 2 from M, and 7 from N protein were mapped with the reported vaccine candidates explaining the possible implications on universal vaccines. Hence, potential target regions for vaccines would be ideally chosen from the structural regions of the genome that lack high variation. The increasing variations in the genome of SARS-CoV-2 together with our observations in structural genes have important implications for the efficacy of a successful universal vaccine against SARS-CoV-2.

Ewing sarcoma family of tumors-derived small extracellular vesicle proteomics identify potential clinical biomarkers.

PURPOSE: Ewing Sarcoma Family of Tumors (ESFT), the second most common pediatric osseous malignancy, are characterized by the pathognomonic chromosomal EWS-ETS translocation. Outside of tumor biopsy, no clinically relevant ESFT biomarkers exist. Additionally, tumor burden assessment at diagnosis, monitoring of disease responsiveness to therapy, and detection of disease recurrence are limited to radiographic imaging. To identify new, clinically relevant biomarkers we evaluated the proteome of a subset of ESFT-derived small extracellular vesicles (sEVs). MATERIALS AND METHODS: We performed the first high quality proteomic study of ESFT-derived sEVs from 5 ESFT cell lines representing the most common EWS-ETS fusion types and identified 619 proteins composing the core ESFT sEV proteome. We compared these core proteins to databases of common plasma-based proteins and sEV-associated proteins found within healthy plasma to identify proteins unique or enriched within ESFT. RESULTS: From these analyses, two membrane bound proteins with biomarker potential were selected, CD99/MIC2 and NGFR, to develop a liquid-based assay enriching of ESFT-associated sEVs and detection of sEV mRNA cargo (i.e., EWS-ETS transcripts). We employed this immuno-enrichment approach to diagnosis of ESFT utilizing plasma (250 µl) from both localized and metastatic ESFT pediatric patients and cancer-free controls, and showed significant diagnostic power [AUC = 0.92, p = 0.001 for sEV numeration, with a PPV = 1.00, 95% CI = (0.63, 1.00) and a NPV = 0.67, 95% CI = (0.30, 0.93)]. CONCLUSIONS: In this study, we demonstrate utilization of circulating ESFT-associated sEVs in pediatric patients as a source of minimally invasive diagnostic and potentially prognostic biomarkers.

Potent Antitumor Effects of a Combination of Three Nutraceutical Compounds.

Head and neck squamous cell carcinoma (HNSCC) is associated with low survival, and the current aggressive therapies result in high morbidity. Nutraceuticals are dietary compounds with few side effects. However, limited antitumor efficacy has restricted their application for cancer therapy. Here, we examine combining nutraceuticals, establishing a combination therapy that is more potent than any singular component, and delineate the mechanism of action. Three formulations were tested: GZ17-S (combined plant extracts from Arum palaestinum, Peganum harmala and Curcuma longa); GZ17-05.00 (16 synthetic components of GZ17-S); and GZ17-6.02 (3 synthetic components of GZ17S; curcumin, harmine and isovanillin). We tested the formulations on HNSCC proliferation, migration, invasion, angiogenesis, macrophage viability and infiltration into the tumor and tumor apoptosis. GZ17-6.02, the most effective formulation, significantly reduced in vitro assessments of HNSCC progression. When combined with cisplatin, GZ17-6.02 enhanced anti-proliferative effects. Molecular signaling cascades inhibited by GZ17-6.02 include EGFR, ERK1/2, and AKT, and molecular docking analyses demonstrate GZ17-6.02 components bind at distinct binding sites. GZ17-6.02 significantly inhibited growth of HNSCC cell line, patient-derived xenografts, and murine syngeneic tumors in vivo (P < 0.001). We demonstrate GZ17-6.02 as a highly effective plant extract combination and pave the way for future clinical application in HNSCC.