DNA Methylation-Derived Immune Cell Proportions and Cancer Risk, Including Lung Cancer, in Black Participants

Prior cohort studies assessing cancer risk based on immune cell subtype profiles have predominantly focused on White populations. This limitation obscures vital insights into how cancer risk varies across race. Immune cell subtype proportions were estimated using deconvolution based on leukocyte DNA methylation markers from blood samples collected at baseline on participants without cancer in the Atherosclerosis Risk in Communities (ARIC) Study. Over a mean of 17.5 years of follow-up, 668 incident cancers were diagnosed in 2,467 Black participants. Cox proportional hazards regression was used to examine immune cell subtype proportions and overall cancer incidence and site-specific incidence (lung, breast, and prostate cancers). Higher T regulatory cell proportions were associated with statistically significantly higher lung cancer risk (hazard ratio = 1.22, 95% confidence interval = 1.06-1.41 per percent increase). Increased memory B cell proportions were associated with significantly higher risk of prostate cancer (1.17, 1.04-1.33) and all cancers (1.13, 1.05-1.22). Increased CD8+ naïve cell proportions were associated with significantly lower risk of all cancers in participants ≥55 years (0.91, 0.83-0.98). Other immune cell subtypes did not display statistically significant associations with cancer risk. These results in Black participants align closely with prior findings in largely White populations. Findings from this study could help identify those at high cancer risk and outline risk stratifying to target patients for cancer screening, prevention, and other interventions. Further studies should assess these relationships in other cancer types, better elucidate the interplay of B cells in cancer risk, and identify biomarkers for personalized risk stratification.

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.

Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer

Background: Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited. Methods: DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering. Results: Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood. Conclusion: Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.

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.

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.

Genome-Scale Methylation Analysis Identifies Immune Profiles and Age Acceleration Associations with Bladder Cancer Outcomes

BACKGROUND

Immune profiles have been associated with bladder cancer outcomes and may have clinical applications for prognosis. However, associations of detailed immune cell subtypes with patient outcomes remain underexplored and may contribute crucial prognostic information for better managing bladder cancer recurrence and survival.

METHODS

Bladder cancer case peripheral blood DNA methylation was measured using the Illumina HumanMethylationEPIC array. Extended cell-type deconvolution quantified 12 immune cell-type proportions, including memory, naïve T and B cells, and granulocyte subtypes. DNA methylation clocks determined biological age. Cox proportional hazards models tested associations of immune cell profiles and age acceleration with bladder cancer outcomes. The partDSA algorithm discriminated 10-year overall survival groups from clinical variables and immune cell profiles, and a semi-supervised recursively partitioned mixture model (SS-RPMM) with DNA methylation data was applied to identify a classifier for 10-year overall survival.

RESULTS

Higher CD8T memory cell proportions were associated with better overall survival [HR = 0.95, 95% confidence interval (CI) = 0.93-0.98], while higher neutrophil-to-lymphocyte ratio (HR = 1.36, 95% CI = 1.23-1.50), CD8T naïve (HR = 1.21, 95% CI = 1.04-1.41), neutrophil (HR = 1.04, 95% CI = 1.03-1.06) proportions, and age acceleration (HR = 1.06, 95% CI = 1.03-1.08) were associated with worse overall survival in patient with bladder cancer. partDSA and SS-RPMM classified five groups of subjects with significant differences in overall survival.

CONCLUSIONS

We identified associations between immune cell subtypes and age acceleration with bladder cancer outcomes.

IMPACT

The findings of this study suggest that bladder cancer outcomes are associated with specific methylation-derived immune cell-type proportions and age acceleration, and these factors could be potential prognostic biomarkers.

Epigenome-wide association study using peripheral blood leukocytes identifies genomic regions associated with periodontal disease and edentulism in the Atherosclerosis Risk in Communities study

AIM

To investigate individual susceptibility to periodontitis by conducting an epigenome-wide association study using peripheral blood.

MATERIALS AND METHODS

We included 1077 African American and 457 European American participants of the Atherosclerosis Risk in Communities (ARIC) study who had completed a dental examination or reported being edentulous at Visit 4 and had available data on DNA methylation from Visit 2 or 3. DNA methylation levels were compared by periodontal disease severity and edentulism through discovery analyses and subsequent testing of individual CpGs.

RESULTS

Our discovery analysis replicated findings from a previous study reporting a region in gene ZFP57 (6p22.1) that was significantly hypomethylated in severe periodontal disease compared with no/mild periodontal disease in European American participants. Higher methylation levels in a separate region in an unknown gene (located in Chr10: 743,992-744,958) was associated with significantly higher odds of edentulism compared with no/mild periodontal disease in African American participants. In subsequent CpG testing, four CpGs in a region previously associated with periodontitis located within HOXA4 were significantly hypermethylated in severe periodontal disease compared with no/mild periodontal disease in African American participants (odds ratio per 1 SD increase in methylation level: cg11015251: 1.28 (1.02, 1.61); cg14359292: 1.24 (1.01, 1.54); cg07317062: 1.30 (1.05, 1.61); cg08657492: 1.25 (1.01, 1.55)).

CONCLUSIONS

Our study highlights epigenetic variations in ZPF57 and HOXA4 that are significantly and reproducibly associated with periodontitis. Future studies should evaluate gene regulatory mechanisms in the candidate regions of these loci.

Placental microRNAs relate to early childhood growth trajectories

BACKGROUND

Poor placental function is a common cause of intrauterine growth restriction, which in turn is associated with increased risks of adverse health outcomes. Our prior work suggests that birthweight and childhood obesity-associated genetic variants functionally impact placental function and that placental microRNA are associated with birthweight. To address the influence of the placenta beyond birth, we assessed the relationship between placental microRNAs and early childhood growth.

METHODS

Using the SITAR package, we generated two parameters that describe individual weight trajectories of children (0-5 years) in the New Hampshire Birth Cohort Study (NHBCS, n = 238). Using negative binomial generalized linear models, we identified placental microRNAs that relate to growth parameters (FDR < 0.1), while accounting for sex, gestational age at birth, and maternal parity.

RESULTS

Genes targeted by the six growth trajectory-associated microRNAs are enriched (FDR < 0.05) in growth factor signaling (TGF/beta: miR-876; EGF/R: miR-155, Let-7c; FGF/R: miR-155; IGF/R: Let-7c, miR-155), calmodulin signaling (miR-216a), and NOTCH signaling (miR-629).

CONCLUSIONS

Growth-trajectory microRNAs target pathways affecting placental proliferation, differentiation and function. Our results suggest a role for microRNAs in regulating placental cellular dynamics and supports the Developmental Origins of Health and Disease hypothesis that fetal environment can have impacts beyond birth.

IMPACT

We found that growth trajectory associated placenta microRNAs target genes involved in signaling pathways central to the formation, maintenance and function of placenta; suggesting that placental cellular dynamics remain critical to infant growth to term and are under the control of microRNAs. Our results contribute to the existing body of research suggesting that the placenta plays a key role in programming health in the offspring. This is the first study to relate molecular patterns in placenta, specifically microRNAs, to early childhood growth trajectory.

Hierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation

Introduction

The human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolved.

Methods

Using DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells.

Results

We demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer's disease, autism, Huntington's disease, epilepsy, and schizophrenia.

Discussion

We expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.

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.

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.

Circulating IgG Antibodies to Periodontal Bacteria and Lung Cancer Risk in the CLUE Cohorts

BACKGROUND

Oral health is a key indicator of overall health, well-being, and quality of life. Several studies have provided new evidence about the role of oral diseases, specifically periodontitis, in generating risk for various forms of cancers including lung, colorectal, and pancreatic cancers.

METHODS

Incident lung cancer cases (n = 192) and matched controls (n = 192) were selected from participants of the CLUE I and CLUE II cohorts. Archived serum samples collected from participants in 1974 (in CLUE I) were analyzed using immunoblotting for IgG antibody levels to 13 bacteria of the periodontium. Associations between antibody levels and lung cancer were estimated using conditional logistic regression.

RESULTS

Most of the periodontal bacterial antibodies measured were inversely associated with lung cancer risk; of these, three were statistically significant (P. intermedia, A. naeslundii, and V. parvula). A statistically significant positive association was observed for one of the P. gingivalis strains after adjusting for P. intermedia. The sum of the logarithm of antibodies against the 13 measured bacteria was inversely associated with risk of lung cancer when the analysis was restricted to a longer follow-up (31-44 years after blood collection, highest vs lowest quartile: OR = 0.26, 95% CI = 0.08-0.84).

CONCLUSION

Findings from this study highlight the complexity of using serum IgG antibodies to periodontal bacteria to identify associations between oral pathogens and risk of lung cancer. The inverse associations observed for antibodies to periodontal bacteria suggest that these may represent markers of immunity that provide some advantage in reducing the development of lung cancer.

Abstract 3005: Pre-diagnostic methylation-based leukocyte profiles and non-small cell lung cancer risk in heavy smokers

Pre-diagnosis circulating leukocyte profiles, including higher white blood cell (WBC) counts and neutrophil-to-lymphocyte ratios (NLR), have been reported to be associated with non-small cell lung cancer (NSCLC) risk. Higher neutrophil levels were recently reported to drive NSCLC-WBC count associations, implicating the innate immune response in NSCLC risk and etiology. Though not directly measurable in bio-banked blood, leukocyte subtype proportions can be estimated using established deconvolution algorithms applied to genome-wide DNA methylation data. We previously reported that NSCLC risk was elevated in heavy smokers with greater pre-diagnosis methylation-derived NLR (mdNLR). Here, we examine the influence of each pre-diagnosis mdNLR leukocyte subtype (neutrophils, and lymphocytes: B, Natural Killer, CD8+T, and CD4+T cells), separately on NSCLC risk. In our nested case-control study from the Beta Carotene and Retinol Efficacy Trial of heavy smokers (≥20 pack years), 243 NSCLC cases were 1:1 matched to controls on age (±5 years), sex, race and ethnicity, enrollment (±2 years), smoking status (ever/never), asbestos exposure, and follow-up time. Methylation was assayed on the Illumina EPIC array in whole blood collected on average 4.4 years (range 0.1-10.1) before diagnosis in NSCLC cases. We assessed conditional logistic regression models for each mdNLR-related leukocyte subtype, dichotomized at the median in controls, and further adjusted for continuous age and smoking pack years at blood draw. We evaluated NSCLC risk overall, and in subgroup strata: histotype, sex, smoking status, asbestos exposure, stage at diagnosis, age at blood draw, age at diagnosis, and time between blood draw and diagnosis. A greater than median level of neutrophils was suggestively associated with NSCLC risk (OR=1.40, 95% CI: 0.93-2.12); this association was restricted to squamous cell carcinoma (SCC n=103 pairs; 2.00, 1.03-3.86), with no association for adenocarcinoma (n=132 pairs; 0.99, 0.57-1.73). SCC-neutrophil associations were strongest among former smokers (n=26 pairs; 6.81, 1.48-31.3), cases with ≤ 4.4 years between blood draw and diagnosis (n=48 pairs; 3.00, 1.10-8.14), cases aged &gt; 64.8 years at blood draw (n=56 pairs; 2.66, 1.05-6.73), and cases diagnosed at stage III or IV (n=61 pairs; 2.21, 0.92-5.30). Greater than median lymphocyte levels were suggestively associated with reduced NSCLC (0.71, 0.47-1.08) and SCC risk (0.62, 0.32-1.21), but less so for adenocarcinoma (0.85, 0.48-1.50). Though statistically imprecise, SCC-lymphocyte associations were strongest for CD4+T cells (0.56, 0.28-1.12). Our results indicate that greater pre-diagnosis neutrophil levels may be a biomarker for SCC, but not adenocarcinoma, in heavy smokers. Given the descriptive nature of this analysis, small sample size, and multiple testing, additional research is needed to replicate these findings.

Citation Format: Laurie Grieshober, Stefan Graw, Matt J. Barnett, Gary E. Goodman, Chu Chen, Devin C. Koestler, Carmen J. Marsit, Jennifer A. Doherty. Pre-diagnostic methylation-based leukocyte profiles and non-small cell lung cancer risk in heavy smokers [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 3005.

Abstract 6675: Integration of associations of immune profiles in peripheral blood and tumor microenvironment with bladder cancer outcomes

Immune cell profiles in peripheral blood have been associated with bladder cancer outcomes, however, their association with response to immunotherapy and the tumor microenvironment is a major unresolved issue. Although tumor growth can be attenuated via the activation of tumor-infiltrating effector T cells, the relationship between tumor infiltration and immune activation remains unclear. This study explored the interaction between bladder cancer outcomes and immune profiles within peripheral blood and a tumor microenvironment (TME) based on DNA methylation profiles. Peripheral blood and the matched tumor FFPE DNA methylation profiles of 60 non-muscle-invasive bladder cancer (NMIBC) and 12 muscle-invasive bladder cancer (MIBC) patients. Cell-type deconvolution approaches were applied to estimate 12 peripheral immune cell-type proportions and 17 cell-type proportions within TME. We found a positive correlation between dendritic cell proportions in the TME with peripheral CD8T memory cell proportions (r = 0.35, P = 0.003) and a negative correlation between dendritic cell proportions in the TME with peripheral regulatory T cell proportions (r = -0.28, P = 0.021). In addition, monocyte cell proportions in TME had a positive correlation with peripheral B memory (r = 0.37, P = 0.002) and CD8T memory cell proportions (r = 0.43, P = 0.0002). To investigate associations of bladder cancer outcomes with immune cell profiles, using Cox proportional hazard models, we observed an association between the fraction of dendritic cells and the hazard of death (HR = 1.27, 95% CI = 1.06-1.53). Further, a high endothelial cell proportion was significantly associated with an increased hazard of death and tumor recurrence (HR = 1.06, 95% CI = 1.01-1.13) in TME. In addition, the peripheral neutrophil-to-lymphocyte ratio (HR = 1.49, 95% CI = 1.01-2.22), monocyte (HR = 1.17, 95% CI = 1.05-1.31), neutrophil (HR = 1.04, 95% CI = 1.01-1.07), and basophil (HR = 1.35, 95% CI = 1.01-1.81) cell proportions were associated with an increased hazard of death and tumor recurrence. Our results integrated the information on bladder cancer outcomes and cell profiles in TME and peripheral blood, providing biomarkers for estimating bladder cancer prognosis using genome-scale DNA methylation measures.

Citation Format: Ji-Qing Chen, Lucas A. Salas, John K. Wiencke, Devin C. Koestler, Annette M. Molinaro, Angeline S. Andrew, John D. Seigne, Margaret R. Karagas, Karl T. Kelsey, Brock C. Christensen. Integration of associations of immune profiles in peripheral blood and tumor microenvironment with bladder cancer outcomes. [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 6675.

Calculating detection limits and uncertainty of reference-based deconvolution of whole-blood DNA methylation data

DNA methylation (DNAm)-based cell mixture deconvolution (CMD) has become a quintessential part of epigenome-wide association studies where DNAm is profiled in heterogeneous tissue types. Despite being introduced over a decade ago, detection limits, which represent the smallest fraction of a cell type in a mixed biospecimen that can be reliably detected, have yet to be determined in the context of DNAm-based CMD. Moreover, there has been little attention given to approaches for quantifying the uncertainty associated with DNAm-based CMD. Here, analytical frameworks for determining both cell-specific limits of detection and quantification of uncertainty associated with DNAm-based CMD are described. This work may contribute to improved rigor, reproducibility and replicability of epigenome-wide association studies involving CMD.

Human placental microRNAs dysregulated by cadmium exposure predict neurobehavioral outcomes at birth

BACKGROUND

Prenatal cadmium (Cd) exposure has been implicated in both placental toxicity and adverse neurobehavioral outcomes. Placental microRNAs (miRNAs) may function to developmentally program adverse pregnancy and newborn health outcomes in response to gestational Cd exposure.

METHODS

In a subset of the Rhode Island Child Health Study (RICHS, n = 115) and the New Hampshire Birth Cohort Study (NHBCS, = 281), we used small RNA sequencing and trace metal analysis to identify Cd-associated expression of placental miRNAs using negative binomial generalized linear models. We predicted mRNAs targeted by Cd-associated miRNAs and relate them to neurobehavioral outcomes at birth through the integration of transcriptomic data and summary scores from the NICU Network Neurobehavioral Scale (NNNS).

RESULTS

Placental Cd concentrations are significantly associated with the expression level of five placental miRNAs in NHBCS, with similar effect sizes in RICHS. These miRNA target genes overrepresented in nervous system development, and their expression is correlated with NNNS metrics suggestive of atypical neurobehavioral outcomes at birth.

CONCLUSIONS

Gestational Cd exposure is associated with the expression of placental miRNAs. Predicted targets of these miRNAs are involved in nervous system development and may also regulate placental physiology, allowing their dysregulation to modify developmental programming of early life health outcomes.

IMPACT

This research aims to address the poor understanding of the molecular mechanisms governing adverse pregnancy and newborn health outcomes in response to Gestational cadmium (Cd) exposure. Our results outline a robust relationship between Cd-associated placental microRNA expression and NICU Network Neurobehavioral Scales (NNNS) at birth indicative of atypical neurobehavior. This study utilized healthy mother-infant cohorts to describe the role of Cd-associated dysregulation of placental microRNAs as a potential mechanism by which adverse neurobehavioral outcomes are developmentally programmed.

Antibodies to Oral Pathobionts and Colon Cancer Risk in the CLUE I Cohort Study

Periodontitis has been associated with an increased risk for gastrointestinal cancers. The objective of our study was to investigate the association of antibodies to oral bacteria and the risk of colon cancer in a cohort setting. Using the CLUE I cohort, a prospective cohort initiated in 1974 in Washington County, Maryland, we conducted a nested case-control study to examine the association of levels of IgG antibodies to 11 oral bacterial species (13 total strains) with risk of colon cancer diagnosed a median of 16 years later (range: 1-26 years). Antibody response was measured using checkerboard immunoblotting assays. We included 200 colon cancer cases and 200 controls matched on age, sex, cigarette smoking status, time of blood draw and pipe or cigar smoking status. Controls were selected using incidence density sampling. Conditional logistic regression models were used to assess the association between antibody levels and colon cancer risk. In the overall analysis, we observed significant inverse associations for 6 of the 13 antibodies measured (P-trends <.05) and one positive association for antibody levels to Aggregatibacter actinomycetemcomitans (ATCC 29523; P-trend = .04). While we cannot rule out a role for periodontal disease in colon cancer risk, findings from our study suggest that a strong adaptive immune response may be associated with a lower risk of colon cancer. More studies will need to examine whether the positive associations we observed with antibodies to A. actinomycetemcomitans reflect a true causal association for this bacterium.

Epigenetic age and lung cancer risk in the CLUE II prospective cohort study

BACKGROUND

Epigenetic age, a robust marker of biological aging, has been associated with obesity, low-grade inflammation and metabolic diseases. However, few studies have examined associations between different epigenetic age measures and risk of lung cancer, despite great interest in finding biomarkers to assist in risk stratification for lung cancer screening.

METHODS

A nested case-control study of lung cancer from the CLUE II cohort study was conducted using incidence density sampling with 1:1 matching of controls to lung cancer cases (n = 208 matched pairs). Prediagnostic blood samples were collected in 1989 (CLUE II study baseline) and stored at -70°C. DNA was extracted from buffy coat and DNA methylation levels were measured using Illumina MethylationEPIC BeadChip Arrays. Three epigenetic age acceleration (i.e., biological age is greater than chronological age) measurements (Horvath, Hannum and PhenoAge) were examined in relation to lung cancer risk using conditional logistic regression.

RESULTS

We did not observe associations between the three epigenetic age acceleration measurements and risk of lung cancer overall; however, inverse associations for the two Hannum age acceleration measures (intrinsic and extrinsic) were observed in men and among younger participants, but not in women or older participants. We did not observe effect modification by time from blood draw to diagnosis.

CONCLUSION

Findings from this study do not support a positive association between three different biological age acceleration measures and risk of lung cancer. Additional studies are needed to address whether epigenetic age is associated with lung cancer in never smokers.

Variation in placental microRNA expression associates with maternal family history of cardiovascular disease

In the United States, cardiovascular disease is the leading cause of death and the rate of maternal mortality remains among the highest of any industrialized nation. Maternal cardiometabolic health throughout gestation and postpartum is representative of placental health and physiology. Both proper placental functionality and placental microRNA expression are essential to successful pregnancy outcomes, and both are highly sensitive to genetic and environmental sources of variation. Placental pathologies, such as preeclampsia, are associated with maternal cardiovascular health but may also contribute to the developmental programming of chronic disease in offspring. However, the role of more subtle alterations to placental function and microRNA expression in this developmental programming remains poorly understood. We performed small RNA sequencing to investigate microRNA in placentae from the Rhode Island Child Health Study (n = 230). MicroRNA counts were modeled on maternal family history of cardiovascular disease using negative binomial generalized linear models. MicroRNAs were considered to be differentially expressed at a false discovery rate (FDR) less than 0.10. Parallel mRNA sequencing data and bioinformatic target prediction software were then used to identify potential mRNA targets of differentially expressed microRNAs. Nine differentially expressed microRNAs were identified (FDR < 0.1). Bioinformatic target prediction revealed 66 potential mRNA targets of these microRNAs, many of which are implicated in TGFβ signaling pathway but also in pathways involving cellular metabolism and immunomodulation. A robust association exists between familial cardiovascular disease and placental microRNA expression which may be implicated in both placental insufficiencies and the developmental programming of chronic disease.

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

Comparative analysis of the DNA methylation landscape in CD4, CD8, and B memory lineages

BACKGROUND

There is considerable evidence that epigenetic mechanisms and DNA methylation are critical drivers of immune cell lineage differentiation and activation. However, there has been limited coordinated investigation of common epigenetic pathways among cell lineages. Further, it remains unclear if long-lived memory cell subtypes differentiate distinctly by cell lineages.

RESULTS

We used the Illumina EPIC array to investigate the consistency of DNA methylation in B cell, CD4 T, and CD8 T naïve and memory cells states. In the process of naïve to memory activation across the three lineages, we identify considerable shared epigenetic regulation at the DNA level for immune memory generation. Further, in central to effector memory differentiation, our analyses revealed specific CpG dinucleotides and genes in CD4 T and CD8 T cells with DNA methylation changes. Finally, we identified unique DNA methylation patterns in terminally differentiated effector memory (TEMRA) CD8 T cells compared to other CD8 T memory cell subtypes.

CONCLUSIONS

Our data suggest that epigenetic alterations are widespread and essential in generating human lymphocyte memory. Unique profiles are involved in methylation changes that accompany memory genesis in the three subtypes of lymphocytes.

Evaluation of cross-platform compatibility of a DNA methylation-based glucocorticoid response biomarker

Background

Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450).

Results

The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays.

Conclusions

We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01352-1.

HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data

Background

Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.

Results

We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.

Conclusion

We developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.

Selenium-associated differentially expressed microRNAs and their targeted mRNAs across the placental genome in two U.S. birth cohorts

Selenium is an important micronutrient for foetal development. MicroRNAs play an important role in the function of the placenta, in communication between the placenta and maternal systems, and their expression can be altered through environmental and nutritional cues. To investigate the associations between placental selenium concentration and microRNA expression in the placenta, our observational study included 393 mother-child pairs from the New Hampshire Birth Cohort Study (NHBCS) and the Rhode Island Child Health Study (RICHS). Placental selenium concentrations were quantified using inductively coupled plasma mass spectrometry, and microRNA transcripts were measured using RNA-seq. We fit negative binomial additive models for assessing the association between selenium and microRNAs. We used the microRNA Data Integration Portal (mirDIP) to predict the target mRNAs of the differentially expressed microRNAs and verified the relationships between miRNA and mRNA targets in a subset of samples using existing whole transcriptome data (N = 199). We identified a non-monotonic association between selenium concentration and the expression of miR-216a-5p/miR-217-5p cluster (effective degrees of freedom, EDF = 2.44 and 2.08; FDR = 3.08 × 10-5) in placenta. Thirty putative target mRNAs of miR-216a-5p and/or miR-217-5p were identified computationally and empirically and were enriched in selenium metabolic pathways (driven by selenoprotein coding genes, TXNRD2 and SELENON). Our findings suggest that selenium influences placental microRNA expression. Further, miR-216a-5p and its putative target mRNAs could be the potential mechanistic targets of the health effect of selenium.

Assessing equivalent and inverse change in genes between diverse experiments

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

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.

Synergistic anti-proliferative activity of JQ1 and GSK2801 in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) constitutes 10-20% of breast cancers and is challenging to treat due to a lack of effective targeted therapies. Previous studies in TNBC cell lines showed in vitro growth inhibition when JQ1 or GSK2801 were administered alone, and enhanced activity when co-administered. Given their respective mechanisms of actions, we hypothesized the combinatorial effect could be due to the target genes affected. Hence the target genes were characterized for their expression in the TNBC cell lines to prove the combinatorial effect of JQ1 and GSK2801.

METHODS

RNASeq data sets of TNBC cell lines (MDA-MB-231, HCC-1806 and SUM-159) were analyzed to identify the differentially expressed genes in single and combined treatments. The topmost downregulated genes were characterized for their downregulated expression in the TNBC cell lines treated with JQ1 and GSK2801 under different dose concentrations and combinations. The optimal lethal doses were determined by cytotoxicity assays. The inhibitory activity of the drugs was further characterized by molecular modelling studies.

RESULTS

Global expression profiling of TNBC cell lines using RNASeq revealed different expression patterns when JQ1 and GSK2801 were co-administered. Functional enrichment analyses identified several metabolic pathways (i.e., systemic lupus erythematosus, PI3K-Akt, TNF, JAK-STAT, IL-17, MAPK, Rap1 and signaling pathways) enriched with upregulated and downregulated genes when combined JQ1 and GSK2801 treatment was administered. RNASeq identified downregulation of PTPRC, MUC19, RNA5-8S5, KCNB1, RMRP, KISS1 and TAGLN (validated by RT-qPCR) and upregulation of GPR146, SCARA5, HIST2H4A, CDRT4, AQP3, MSH5-SAPCD1, SENP3-EIF4A1, CTAGE4 and RNASEK-C17orf49 when cells received both drugs. In addition to differential gene regulation, molecular modelling predicted binding of JQ1 and GSK2801 with PTPRC, MUC19, KCNB1, TAGLN and KISS1 proteins, adding another mechanism by which JQ1 and GSK2801 could elicit changes in metabolism and proliferation.

CONCLUSION

JQ1-GSK2801 synergistically inhibits proliferation and results in selective gene regulation. Besides suggesting that combinatorial use could be useful therapeutics for the treatment of TNBC, the findings provide a glimpse into potential mechanisms of action for this combination therapy approach.

A Novel Framework for the Identification of Reference DNA Methylation Libraries for Reference-Based Deconvolution of Cellular Mixtures

Reference-based deconvolution methods use reference libraries of cell-specific DNA methylation (DNAm) measurements as a means toward deconvoluting cell proportions in heterogeneous biospecimens (e.g., whole-blood). As the accuracy of such methods depends highly on the CpG loci comprising the reference library, recent research efforts have focused on the selection of libraries to optimize deconvolution accuracy. While existing approaches for library selection work extremely well, the best performing approaches require a training data set consisting of both DNAm profiles over a heterogeneous cell population and gold-standard measurements of cell composition (e.g., flow cytometry) in the same samples. Here, we present a framework for reference library selection without a training dataset (RESET) and benchmark it against the Legacy method (minfi:pickCompProbes), where libraries are constructed based on a pre-specified number of cell-specific differentially methylated loci (DML). RESET uses a modified version of the Dispersion Separability Criteria (DSC) for comparing different libraries and has four main steps: 1) identify a candidate set of cell-specific DMLs, 2) randomly sample DMLs from the candidate set, 3) compute the Modified DSC of the selected DMLs, and 4) update the selection probabilities of DMLs based on their contribution to the Modified DSC. Steps 2–4 are repeated many times and the library with the largest Modified DSC is selected for subsequent reference-based deconvolution. We evaluated RESET using several publicly available datasets consisting of whole-blood DNAm measurements with corresponding measurements of cell composition. We computed the RMSE and R² between the predicted cell proportions and their measured values. RESET outperformed the Legacy approach in selecting libraries that improve the accuracy of deconvolution estimates. Additionally, reference libraries constructed using RESET resulted in cellular composition estimates that explained more variation in DNAm as compared to the Legacy approach when evaluated in the context of epigenome-wide association studies (EWAS) of several publicly available data sets. This finding has implications for the statistical power of EWAS. RESET combats potential challenges associated with existing approaches for reference library assembly and thus, may serve as a viable strategy for library construction in the absence of a training data set.

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.

Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling

DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.

Rates of bone reabsorption and union in mandibular reconstruction using the osteocutaneous radial forearm free flap

BACKGROUND

Historical concerns over bone resorption and malunion of the osteocutaneous radial forearm free flap (OCRFFF) limited its widespread adoption for head and neck reconstruction, despite lack of outcomes data evaluating this notion.

METHODS

A retrospective cohort study was performed including patients 18 years or older who underwent reconstruction of the mandible using an OCRFFF. Linear modeling and logistic regression were used to evaluate the change in bone volume and union over time.

RESULTS

One hundred and twenty-one patients were included in the study. A mixed effects linear model incorporating age, sex, treatment type, and number of bone segments did not demonstrate a significant loss of bone volume over time. A logistic regression model identified lack of adjuvant treatment and time to be significantly associated with complete union.

CONCLUSION

This study supports that the OCRFFF is a stable form of osseus reconstruction for defects of the head and neck.

Immune profiles and DNA methylation alterations related with non-muscle-invasive bladder cancer outcomes

BACKGROUND

Non-muscle-invasive bladder cancer (NMIBC) patients receive frequent monitoring because ≥ 70% will have recurrent disease. However, screening is invasive, expensive, and associated with significant morbidity making bladder cancer the most expensive cancer to treat per capita. There is an urgent need to expand the understanding of markers related to recurrence and survival outcomes of NMIBC.

METHODS AND RESULTS

We used the Illumina HumanMethylationEPIC array to measure peripheral blood DNA methylation profiles of NMIBC patients (N = 603) enrolled in a population-based cohort study in New Hampshire and applied cell type deconvolution to estimate immune cell-type proportions. Using Cox proportional hazard models, we identified that increasing CD4T and CD8T cell proportions were associated with a statistically significant decreased hazard of tumor recurrence or death (CD4T: HR = 0.98, 95% CI = 0.97-1.00; CD8T: HR = 0.97, 95% CI = 0.95-1.00), whereas increasing monocyte proportion and methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) were associated with the increased hazard of tumor recurrence or death (monocyte: HR = 1.04, 95% CI = 1.00-1.07; mdNLR: HR = 1.12, 95% CI = 1.04-1.20). Then, using an epigenome-wide association study (EWAS) approach adjusting for age, sex, smoking status, BCG treatment status, and immune cell profiles, we identified 2528 CpGs associated with the hazard of tumor recurrence or death (P < 0.005). Among these CpGs, the 1572 were associated with an increased hazard and were significantly enriched in open sea regions; the 956 remaining CpGs were associated with a decreased hazard and were significantly enriched in enhancer regions and DNase hypersensitive sites.

CONCLUSIONS

Our results expand on the knowledge of immune profiles and methylation alteration associated with NMIBC outcomes and represent a first step toward the development of DNA methylation-based biomarkers of tumor recurrence.

shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area

PURPOSE

The University of Kansas Cancer Center (KU Cancer Center) recently developed a data warehouse to Organize and Prioritize Trends to Inform KU Cancer Center (OPTIK). The OPTIK database aggregates and standardizes data collected across the bistate catchment area served by the KU Cancer Center. To improve the usability of the OPTIK database, we developed shinyOPTIK, a user-friendly, interactive web application for visualizing cancer risk factor and mortality rate data across the KU Cancer Center Catchment area.

METHODS

Data in the OPTIK database were first consolidated at the county level across the KU Cancer Center catchment area. Next, the shinyOPTIK development team met with the KU Cancer Center leadership to discuss the needs and priorities of the shinyOPTIK web application. shinyOPTIK was developed under the R Shiny framework and consists of a user interface (ui.R) and a web server (server.R). At present, shinyOPTIK can be used to generate county-level geographical heatmaps; bar plots of demographic, screening, and risk factors; and line plots to visualize temporal trends at different Rural-Urban Continuum Codes (RUCCs), rural-urban status, metropolitan, or county levels across the KU Cancer Center catchment area.

RESULTS

Two examples, adult obesity prevalence and lung cancer mortality, are presented to illustrate how researchers can use shinyOPTIK. Each example is accompanied by post hoc visualizations to help explain key observations in terms of rural-urban disparities.

CONCLUSION

Although shinyOPTIK was developed to improve understanding of spatial and temporal trends across the population served by the KU Cancer Center, our hope is that the description of the steps involved in the creation of this tool along with open-source code for our application provided herein will serve as a guide for other research centers in the development of similar tools.

pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples

Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, "bridging samples", to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M, and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE, a simulation-based approach to estimate the bridging sample size, M, in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE, we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer's disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.

Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering

Understanding the relationship between molecular markers and a phenotype of interest is often obfuscated by patient-level heterogeneity. To address this challenge, Chang et al. recently published a novel method called Component-wise Sparse Mixture Regression (CSMR), a regression-based clustering method that promises to detect heterogeneous relationships between molecular markers and a phenotype of interest under high-dimensional settings. In this Letter to the Editor, we raise awareness to several issues concerning the assessment of CSMR in Chang et al., particularly its assessment in settings where the number of features, P, exceeds the study sample size, N, and advocate for additional metrics/approaches when assessing the performance of regression-based clustering methodologies.

Methylation-derived inflammatory measures and lung cancer risk and survival

BACKGROUND

Examining immunity-related DNA methylation alterations in blood could help elucidate the role of the immune response in lung cancer etiology and aid in discovering factors that are key to lung cancer development and progression. In a nested, matched case-control study, we estimated methylation-derived NLR (mdNLR) and quantified DNA methylation levels at loci previously linked with circulating concentrations of C-reactive protein (CRP). We examined associations between these measures and lung cancer risk and survival.

RESULTS

Using conditional logistic regression and further adjusting for BMI, batch effects, and a smoking-based methylation score, we observed a 47% increased risk of non-small cell lung cancer (NSCLC) for one standard deviation (SD) increase in mdNLR (n = 150 pairs; OR: 1.47, 95% CI 1.08, 2.02). Using a similar model, the estimated CRP Scores were inversely associated with risk of NSCLC (e.g., Score 1 OR: 0.57, 95% CI: 0.40, 0.81). Using Cox proportional hazards models adjusting for age, sex, smoking status, methylation-predicted pack-years, BMI, batch effect, and stage, we observed a 28% increased risk of dying from lung cancer (n = 145 deaths in 205 cases; HR: 1.28, 95% CI: 1.09, 1.50) for one SD increase in mdNLR.

CONCLUSIONS

Our study demonstrates that immunity status measured with DNA methylation markers is associated with lung cancer a decade or more prior to cancer diagnosis. A better understanding of immunity-associated methylation-based biomarkers in lung cancer development could provide insight into critical pathways.

Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)

Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called "bridge samples", to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinalComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.

Perturbation of BRMS1 interactome reveals pathways that impact metastasis

Breast Cancer Metastasis Suppressor 1 (BRMS1) expression is associated with longer patient survival in multiple cancer types. Understanding BRMS1 functionality will provide insights into both mechanism of action and will enhance potential therapeutic development. In this study, we confirmed that the C-terminus of BRMS1 is critical for metastasis suppression and hypothesized that critical protein interactions in this region would explain its function. Phosphorylation status at S237 regulates BRMS1 protein interactions related to a variety of biological processes, phenotypes [cell cycle (e.g., CDKN2A), DNA repair (e.g., BRCA1)], and metastasis [(e.g., TCF2 and POLE2)]. Presence of S237 also directly decreased MDA-MB-231 breast carcinoma migration in vitro and metastases in vivo. The results add significantly to our understanding of how BRMS1 interactions with Sin3/HDAC complexes regulate metastasis and expand insights into BRMS1's molecular role, as they demonstrate BRMS1 C-terminus involvement in distinct protein-protein interactions.

Interactions of Age and Blood Immune Factors and Non-Invasive Prediction of Glioma Survival

BACKGROUND

Tumor-based classification of human glioma portends patient prognosis; however, considerable unexplained survival variability remains. Host factors (eg, age) also strongly influence survival times, partly reflecting a compromised immune system. How blood epigenetic measures of immune characteristics and age augment molecular classifications in glioma survival has not been investigated. We assess the prognostic impact of immune-cell fractions and epigenetic age in archived blood across glioma molecular subtypes for the first time.

METHODS

We evaluated immune-cell fractions and epigenetic age in archived blood from the University of California San Francisco Adult Glioma Study, including a training set of 197 IDH-wildtype, 1p19q intact, TERT wildtype (IDH/1p19q/TERT-WT) glioma patients, an evaluation set of 350 patients with other subtypes of glioma, and 454 subjects without glioma.

RESULTS

IDH/1p19q/TERT-WT patients had lower lymphocyte fractions (CD4+T, CD8+T, natural killer, and B cells) and higher neutrophil fractions than subjects without glioma. Recursive partitioning analysis delineated four statistically significantly different survival groups for IDH/1p19q/TERT-WT patients based on an interaction between chronological age and two blood immune factors, CD4+T cells, and neutrophils with median overall survival ranging from 0.76 years [95% confidence intervaI = 0.55 to 0.99] for the worst survival group (n = 28) to 9.72 years [95% confidence intervaI = 6.18 to NA] for the best (n = 33). The Recursive partitioning analysis also statistically significantly delineated four risk groups in patients with other glioma subtypes.

CONCLUSION

The delineation of different survival groups in the training and evaluation sets based on an interaction between chronological age and blood immune characteristics suggests that common host immune factors among different glioma types may impact survival. The ability of DNA methylation-based markers of immune status to capture diverse, clinically relevant information may facilitate non-invasive personalized patient evaluation in the neuro-oncology clinic.

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.

Pre-diagnosis neutrophil-to-lymphocyte ratio and mortality in individuals who develop lung cancer

Purpose

The neutrophil-to-lymphocyte ratio (NLR) is a marker of systemic inflammation that has been reported to be associated with survival after chronic disease diagnoses, including lung cancer. We hypothesized that the inflammatory profile reflected by pre-diagnosis NLR, rather than the well-studied pre-treatment NLR at diagnosis, may be associated with increased mortality after lung cancer is diagnosed in high-risk heavy smokers.

Methods

We examined associations between pre-diagnosis methylation-derived NLR (mdNLR) and lung cancer-specific and all-cause mortality in 279 non-small lung cancer (NSCLC) and 81 small cell lung cancer (SCLC) cases from the β-Carotene and Retinol Efficacy Trial (CARET). Cox proportional hazards models were adjusted for age, sex, smoking status, pack years, and time between blood draw and diagnosis, and stratified by stage of disease. Models were run separately by histotype.

Results

Among SCLC cases, those with pre-diagnosis mdNLR in the highest quartile had 2.5-fold increased mortality compared to those in the lowest quartile. For each unit increase in pre-diagnosis mdNLR, we observed 22–23% increased mortality (SCLC-specific hazard ratio [HR] = 1.23, 95% confidence interval [CI]: 1.02, 1.48; all-cause HR = 1.22, 95% CI 1.01, 1.46). SCLC associations were strongest for current smokers at blood draw (Interaction Ps = 0.03). Increasing mdNLR was not associated with mortality among NSCLC overall, nor within adenocarcinoma (N = 148) or squamous cell carcinoma (N = 115) case groups.

Conclusion

Our findings suggest that increased mdNLR, representing a systemic inflammatory profile on average 4.5 years before a SCLC diagnosis, may be associated with mortality in heavy smokers who go on to develop SCLC but not NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10552-021-01469-3.

Epigenome-wide scan identifies differentially methylated regions for lung cancer using pre-diagnostic peripheral blood

BACKGROUND

DNA methylation markers have been associated with lung cancer risk and may identify aetiologically relevant genomic regions, or alternatively, be markers of disease risk factors or biological processes associated with disease development.

METHODS

In a nested case-control study, we measured blood leukocyte DNA methylation levels in pre-diagnostic samples collected from 430 participants (208 cases; 222 controls) in the 1989 CLUE II cohort. We compared DNA methylation levels with case/control status to identify novel genomic regions, both single CpG sites and differentially methylated regions (DMRs), while controlling for known DNA methylation changes associated with smoking using a previously described pack-years-based smoking methylation score. Stratification analyses were conducted over time from blood draw to diagnosis, histology, and smoking status.

RESULTS

We identified 16 single CpG sites and 40 DMRs significantly associated with lung cancer risk (q < 0.05). The identified genomic regions were associated with genes including H19, HOXA3/HOXA4, RUNX3, BRICD5, PLXNB2, and RP13. For the single CpG sites, the strongest association was noted for cg09736286 in the DIABLO gene (OR [for 1 SD] = 2.99, 95% CI: 1.95-4.59, P-value = 4.81 × 10-7). We found that CpG sites in the HOXA3/HOXA4 region were hypermethylated in cases compared to controls.

CONCLUSION

The single CpG sites and DMRs that we identified represented significant measurable differences in lung cancer risk, providing potential biomarkers for lung cancer risk stratification. Future studies will need to examine whether these regions are causally related to lung cancer.

Identification of a foetal epigenetic compartment in adult human kidney

The mammalian kidney has extensive repair capacity; however, identifying adult renal stem cells has proven elusive. We applied an epigenetic marker of foetal cell origin (FCO) in diverse human tissues as a probe for developmental cell persistence, finding a 5.4-fold greater FCO proportion in kidney. Normal kidney FCO proportions averaged 49% with extensive interindividual variation. FCO proportions were significantly negatively correlated with immune-related gene expression and positively correlated with genes expressed in the renal medulla, including those involved in renal organogenesis (e.g., FGF2, PAX8, and HOXB7). FCO associated genes also mapped to medullary nephron segments in mouse and rat, suggesting evolutionary conservation of this cellular compartment. Renal cancer patients whose tumours contained non-zero FCO scores survived longer. The kidney appears unique in possessing substantial foetal epigenetic features. Further study of FCO-related gene methylation may elucidate regenerative regulatory programmes in tissues without apparent discrete stem cell compartments.

A ketogenic diet differentially affects neuron and astrocyte transcription

Ketogenic diets (KDs) alter brain metabolism. Multiple mechanisms may account for their effects, and different brain regions may variably respond. Here, we considered how a KD affects brain neuron and astrocyte transcription. We placed male C57Bl6/N mice on either a 3-month KD or chow diet, generated enriched neuron and astrocyte fractions, and used RNA-Seq to assess transcription. Neurons from KD-treated mice generally showed transcriptional pathway activation while their astrocytes showed a mix of transcriptional pathway suppression and activation. The KD especially affected pathways implicated in mitochondrial and endoplasmic reticulum function, insulin signaling, and inflammation. An unbiased analysis of KD-associated expression changes strongly implicated transcriptional pathways altered in AD, which prompted us to explore in more detail the potential molecular relevance of a KD to AD. Our results indicate a KD differently affects neurons and astrocytes, and provide unbiased evidence that KD-induced brain effects are potentially relevant to neurodegenerative diseases such as AD.

Comparisons of oral, intestinal, and pancreatic bacterial microbiomes in patients with pancreatic cancer and other gastrointestinal diseases

Background: Oral microbiota is believed to play important roles in systemic diseases, including cancer. Methods: We collected oral samples (tongue, buccal, supragingival, and saliva) and pancreatic tissue or intestinal samples from 52 subjects, and characterized 16S rRNA genes using high-throughput DNA sequencing. Results: Bray-Curtis plot showed clear separations between bacterial communities in the oral cavity and those in intestinal and pancreatic tissue samples. PERMANOVA tests indicated that bacterial communities from buccal samples were similar to supragingival and saliva samples, and pancreatic duct samples were similar to pancreatic tumor samples, but all other samples were significantly different from each other. A total of 73 unique Amplicon Sequence Variants (ASVs) were shared between oral and pancreatic or intestinal samples. Only four ASVs showed significant concordance, and two specific bacterial species (Gemella morbillorum and Fusobacterium nucleatum subsp. vincentii) showed consistent presence or absence patterns between oral and intestinal or pancreatic samples, after adjusting for within-subject correlation and disease status. Lastly, microbial co-abundance analyses showed distinct strain-level cluster patterns among microbiome members in buccal, saliva, duodenum, jejunum, and pancreatic tumor samples. Conclusions: Our findings indicate that oral, intestinal, and pancreatic bacterial microbiomes overlap but exhibit distinct co-abundance patterns in patients with pancreatic cancer and other gastrointestinal diseases.

Effect of the p53 P72R Polymorphism on Mutant TP53 Allele Selection in Human Cancer

BACKGROUND

TP53 mutations occur in more than 50% of cancers. We sought to determine the effect of the intragenic P72R SNP (rs1042522) on the oncogenic properties of mutant p53.

METHODS

P72R allelic selection in tumors was determined from genotype calls and a Gaussian distributed mixture model. The SNP effect on mutant p53 was determined in p53-negative cancer cell lines. RNA-sequencing, chromatin immunoprecipitation, and survival analysis were performed to describe the SNP effect. All statistical tests were 2-sided.

RESULTS

Among 409 patients with germline heterozygous P72R SNP who harbored somatic mutations in TP53, we observed a selection bias against missense TP53 mutants encoding the P72 SNP (P = 1.64 x 10-13). Exogenously expressed hotspot p53 mutants with the P72 SNP were negatively selected in cancer cells. Gene expression analyses showed the enrichment of p53 pathway genes and inflammatory genes in cancer cells transduced with mutants encoding P72 SNP. Immune gene signature is enriched in patients harboring missense TP53 mutations with homozygous P72 SNP. These patients have improved overall survival as compared to those with the R72 SNP (P = 0.04).

CONCLUSION

This is the largest study demonstrating a selection against the P72 SNP.  Missense p53 mutants with the P72 SNP retain partial wild type tumor-suppressive functions, which may explain the selection bias against P72 SNP across cancer types. Ovarian cancer patients with the P72 SNP have a better prognosis than with the R72 SNP. Our study describes a previously unknown role through which the rs1042522 SNP modifies tumor suppressor activities of mutant p53 in patients.

MicroRNA Profiling as a Methodology to Diagnose Ménière's Disease: Potential Application of Machine Learning

OBJECTIVE

Diagnosis and treatment of Ménière's disease remains a significant challenge because of our inability to understand what is occurring on a molecular level. MicroRNA (miRNA) perilymph profiling is a safe methodology and may serve as a "liquid biopsy" equivalent. We used machine learning (ML) to evaluate miRNA expression profiles of various inner ear pathologies to predict diagnosis of Ménière's disease.

STUDY DESIGN

Prospective cohort study.

SETTING

Tertiary academic hospital.

SUBJECTS AND METHODS

Perilymph was collected during labyrinthectomy (Ménière's disease, n = 5), stapedotomy (otosclerosis, n = 5), and cochlear implantation (sensorineural hearing loss [SNHL], n = 9). miRNA was isolated and analyzed with the Affymetrix miRNA 4.0 array. Various ML classification models were evaluated with an 80/20 train/test split and cross-validation. Permutation feature importance was performed to understand miRNAs that were critical to the classification models.

RESULTS

In terms of miRNA profiles for conductive hearing loss versus Ménière's, 4 models were able to differentiate and identify the 2 disease classes with 100% accuracy. The top-performing models used the same miRNAs in their decision classification model but with different weighted values. All candidate models for SNHL versus Ménière's performed significantly worse, with the best models achieving 66% accuracy. Ménière's models showed unique features distinct from SNHL.

CONCLUSIONS

We can use ML to build Ménière's-specific prediction models using miRNA profile alone. However, ML models were less accurate in predicting SNHL from Ménière's, likely from overlap of miRNA biomarkers. The power of this technique is that it identifies biomarkers without knowledge of the pathophysiology, potentially leading to identification of novel biomarkers and diagnostic tests.

DNA methylation ageing clocks and pancreatic cancer risk: pooled analysis of three prospective nested case-control studies

DNA methylation (DNAm) age may reflect age-related variations in biological changes and abnormalities related to ageing. DNAm age acceleration measures have been associated with a number of cancers, but to our knowledge, have not been examined in relation to pancreatic cancer risk or survival. DNAm levels in leukocytes of prediagnostic blood samples of 393 pancreatic cancer cases and 431 matched controls, pooled from three large prospective cohort studies, were used to estimate DNAm age, epigenetic age acceleration (AA), and intrinsic epigenetic age acceleration (IEAA) metrics. Logistic regression and Cox proportional hazard regression models were used to examine the relationship between the various AA and IEAA metrics and pancreatic cancer risk and survival, respectively. The results showed that pancreatic cancer risk was significantly increased across all IEAA metrics, ranging from 83% to 95% increased risk when comparing the third and highest quartiles to the lowest quartile of IEAA. Consistent with these findings, the results from multivariate spline regression analyses showed non-linear relationships between all three IEAA metrics and pancreatic cancer risk with apparent threshold effect including two turning points at minimal and at maximal risks, respectively. There is no evidence of a significant association between pancreatic cancer survival and any of the epigenetic AA or IEAA metrics. Our results indicate DNAm age acceleration, measured in blood prior to cancer diagnosis, is associated with an increased risk of pancreatic cancer in a complex nonlinear, dose-response manner. Epigenetic IEAA metrics may be a useful addition to current methods for pancreatic cancer risk prediction.