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Background: Assessing functional ability is an important component of understanding healthy aging. Objective measures of functional ability include grip strength, gait speed, sit-to-stand time, and 6-min walk distance. Using samples from a weight loss clinical trial in older adults with obesity, we examined the association between changes in physical function and DNA-methylation-based biological age at baseline and 12 weeks in 16 individuals. Peripheral blood DNA methylation was measured (pre/post) with the Illumina HumanMethylationEPIC array and the Hannum, Horvath, and PhenoAge DNA methylation age clocks were used. Linear regression models adjusted for chronological age and sex tested the relationship between DNA methylation age and grip strength, gait speed, sit-to-stand, and 6-min walk. Results: Participant mean weight loss was 4.6 kg, and DNA methylation age decreased 0.8, 1.1, and 0.5 years using the Hannum, Horvath, and PhenoAge DNA methylation clocks respectively. Mean grip strength increased 3.2 kg. Decreased Hannum methylation age was significantly associated with increased grip strength (β = −0.30, p = 0.04), and increased gait speed (β = 0.02, p = 0.05), in adjusted models. Similarly, decreased methylation age using the PhenoAge clock was associated with significantly increased gait speed (β = 0.02, p = 0.04). A decrease in Horvath DNA methylation age and increase in physical functional ability did not demonstrate a significant association. Conclusions: The observed relationship between increased physical functional ability and decreased biological age using DNA methylation clocks demonstrate the potential utility of DNA methylation clocks to assess interventional approaches to improve health in older obese adults. Trial registration: National Institute on Aging (NIA), NCT03104192. Posted April 7, 2017, https://clinicaltrials.gov/ct2/show/NCT03104192 [ABSTRACT FROM AUTHOR]

There is a small but growing body of literature regarding the predictive utility of a Let-7 micro RNA-binding-site polymorphism in the 3′-untranslated region (UTR) of KRAS ( KRAS- LCS6) for colorectal cancer outcome, although the results are conflicting. We performed a review and meta-analysis in an attempt to better clarify this relationship. A PubMed search was conducted to identify all studies reporting on KRAS let-7 micro RNA -binding site polymorphism ( LCS6; rs61764370) and colorectal cancer outcome. Hazard ratios ( HR) and corresponding 95% confidence intervals ( CI) were extracted or estimated from each manuscript. Log HRs and log CIs were combined across studies using the inverse-variance weight to calculate fixed- and random-effects summary estimates and corresponding 95% CIs for overall and progression-free survival. We did not observe any significant association between overall or progression-free survival, neither when considering all colorectal cancer patients nor for subgroup analyses (metastatic, anti- EGFR [epidermal growth factor receptor] treatment, or KRAS wild type). There was substantial heterogeneity across studies, overall and among subgroups analyzed. We have found no clear evidence to support an association between the KRAS- LCS6 genotype and overall or progression-free survival among colorectal cancer patients, even after conducting subgroup analyses by stage and anti- EGFR treatment status. This information helps to clarify the confusing body of literature regarding the clinical implications of the KRAS- LCS6 genetic variant on colorectal cancer outcomes, indicating that it should not be used at the present time to personalize therapeutic strategies ( PROSPERO registration number: CRD42013005325). [ABSTRACT FROM AUTHOR]

Down syndrome (DS) is a genetic disorder caused by an extra copy of chromosome 21 that presents developmental dysfunction and intellectual disability. To better understand the cellular changes associated with DS, we investigated the cell composition in blood, brain, and buccal swab samples from DS patients and controls using DNA methylation-based cell-type deconvolution. We used genome-scale DNA methylation data from Illumina HumanMethylation450k and HumanMethylationEPIC arrays to profile cell composition and trace fetal lineage cells in blood samples (DS N = 46; control N = 1469), brain samples from various regions (DS N = 71; control N = 101), and buccal swab samples (DS N = 10; control N = 10). In early development, the number of cells from the fetal lineage in the blood is drastically lower in DS patients (Δ = 17.5%), indicating an epigenetically dysregulated maturation process for DS patients. Across sample types, we observed significant alterations in relative cell-type proportions for DS subjects compared with the controls. Cell-type proportion alterations were present in samples from early development and adulthood. Our findings provide insight into DS cellular biology and suggest potential cellular interventional targets for DS. [ABSTRACT FROM AUTHOR]

DNA methylation is a well-recognized epigenetic mechanism that has been the subject of a growing body of literature typically focused on the identification and study of profiles of DNA methylation and their association with human diseases and exposures. In recent years, a number of unsupervised clustering algorithms, both parametric and non-parametric, have been proposed for clustering large-scale DNA methylation data. However, most of these approaches do not incorporate known biological relationships of measured features, and in some cases, rely on unrealistic assumptions regarding the nature of DNA methylation. Here, we propose a modified version of a recursively partitioned mixture model (RPMM) that integrates information related to the proximity of CpG loci within the genome to inform correlation structures from which subsequent clustering analysis is based. Using simulations and four methylation data sets, we demonstrate that integrating biologically informative correlation structures within RPMM resulted in improved goodness-of-fit, clustering consistency, and the ability to detect biologically meaningful clusters compared to methods which ignore such correlation. Integrating biologically-informed correlation structures to enhance modeling techniques is motivated by the rapid increase in resolution of DNA methylation microarrays and the increasing understanding of the biology of this epigenetic mechanism. [ABSTRACT FROM AUTHOR]

Background Although much is known about molecular and chromosomal characteristics that distinguish glioma histological subtypes, DNA methylation patterns of gliomas and their association with other tumor features such as mutation of isocitrate dehydrogenase (IDH) genes have only recently begun to be investigated. Methods DNA methylation of glioblastomas, astrocytomas, oligodendrogliomas, oligoastrocytomas, ependymomas, and pilocytic astrocytomas (n = 131) from the Brain Tumor Research Center at the University of California San Francisco, as well as nontumor brain tissues (n = 7), was assessed with the Illumina GoldenGate methylation array. Methylation data were subjected to recursively partitioned mixture modeling (RPMM) to derive methylation classes. Differential DNA methylation between tumor and nontumor was also assessed. The association between methylation class and IDH mutation (IDH1 and IDH2) was tested using univariate and multivariable analysis for tumors (n = 95) with available substrate for sequencing. Survival of glioma patients carrying mutant IDH (n = 57) was compared with patients carrying wild-type IDH (n = 38) using a multivariable Cox proportional hazards model and Kaplan–Meier analysis. All statistical tests were two-sided. Results We observed a statistically significant association between RPMM methylation class and glioma histological subtype (P < 2.2 × 10−16). Compared with nontumor brain tissues, across glioma tumor histological subtypes, the differential methylation ratios of CpG loci were statistically significantly different (permutation P < .0001). Methylation class was strongly associated with IDH mutation in gliomas (P = 3.0 × 10−16). Compared with glioma patients whose tumors harbored wild-type IDH, patients whose tumors harbored mutant IDH showed statistically significantly improved survival (hazard ratio of death = 0.27, 95% confidence interval = 0.10 to 0.72). Conclusion The homogeneity of methylation classes for gliomas with IDH mutation, despite their histological diversity, suggests that IDH mutation is associated with a distinct DNA methylation phenotype and an altered metabolic profile in glioma. [ABSTRACT FROM PUBLISHER]

Although tumor size and lymph node involvement are the current cornerstones of breast cancer prognosis, they have not been extensively explored in relation to tumor methylation attributes in conjunction with other tumor and patient dietary and hormonal characteristics. Using primary breast tumors from 162 (AJCC stage I-IV) women from the Kaiser Division of Research Pathways Study and the Illumina GoldenGate methylation bead-array platform, we measured 1,413 autosomal CpG loci associated with 773 cancer-related genes and validated select CpG loci with Sequenom EpiTYPER. Tumor grade, size, estrogen and progesterone receptor status, and triple negative status were significantly (Q-values <0.05) associated with altered methylation of 209, 74, 183, 69, and 130 loci, respectively. Unsupervised clustering, using a recursively partitioned mixture model (RPMM), of all autosomal CpG loci revealed eight distinct methylation classes. Methylation class membership was significantly associated with patient race (P<0.02) and tumor size (P<0.001) in univariate tests. Using multinomial logistic regression to adjust for potential confounders, patient age and tumor size, as well as known disease risk factors of alcohol intake and total dietary folate, were all significantly (P,0.0001) associated with methylation class membership. Breast cancer prognostic characteristics and risk-related exposures appear to be associated with gene-specific tumor methylation, as well as overall methylation patterns. [ABSTRACT FROM AUTHOR]

Background: Solid tumors, including head and neck squamous cell carcinomas (HNSCC), arise as a result of genetic and epigenetic alterations in a sustained stress environment. Little work has been done that simultaneously examines the spectrum of both types of changes in human tumors on a genome-wide scale and results so far have been limited and mixed. Since it has been hypothesized that epigenetic alterations may act by providing the second carcinogenic hit in gene silencing, we sought to identify genome-wide DNA copy number alterations and CpG dinucleotide methylation events and examine the global/local relationships between these types of alterations in HNSCC. Methodology/Principal Findings: We have extended a prior analysis of 1,413 cancer-associated loci for epigenetic changes in HNSCC by integrating DNA copy number alterations, measured at 500,000 polymorphic loci, in a case series of 19 primary HNSCC tumors. We have previously demonstrated that local copy number does not bias methylation measurements in this array platform. Importantly, we found that the global pattern of copy number alterations in these tumors was significantly associated with tumor methylation profiles (p<0.002). However at the local level, gene promoter regions did not exhibit a correlation between copy number and methylation (lowest q = 0.3), and the spectrum of genes affected by each type of alteration was unique. Conclusion/Significance: This work, using a novel and robust statistical approach demonstrates that, although a ''second hit'' mechanism is not likely the predominant mode of action for epigenetic dysregulation in cancer, the patterns of methylation events are associated with the patterns of allele loss. Our work further highlights the utility of integrative genomics approaches in exploring the driving somatic alterations in solid tumors. [ABSTRACT FROM AUTHOR]

Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variations in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1,413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (P,0.0001) and were significant predictors of tissue origin (P,0.0001). In solid tissues (n = 119) we found striking, highly significant CpG island-dependent correlations between age and methylation; loci in CpG islands gained methylation with age, loci not in CpG islands lost methylation with age (P,0.001), and this pattern was consistent across tissues and in an analysis of blood-derived DNA. Our data clearly demonstrate age- and exposure-related differences in tissue-specific methylation and significant age-associated methylation patterns which are CpG island context-dependent. This work provides novel insight into the role of aging and the environment in susceptibility to diseases such as cancer and critically informs the field of epigenomics by providing evidence of epigenetic dysregulation by age-related methylation alterations. Collectively we reveal key issues to consider both in the construction of reference and disease-related epigenomes and in the interpretation of potentially pathologically important alterations. [ABSTRACT FROM AUTHOR]

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rapidly fatal asbestos-associated malignancy with a median survival time of < 1 year following diagnosis. Treatment strategy is determined in part using known prognostic factors. OBJECTIVE: The aim of this study was to examine the relationship between asbestos exposure and survival outcome in MPM in an effort to advance the understanding of the contribution of asbestos exposure to MPM prognosis. METHODS: We studied incident cases of MPM patients enrolled through the International Mesothelioma Program at Brigham and Women's Hospital in Boston, Massachusetts, using survival follow-up, self-reported asbestos exposure (n = 128), and a subset of cases (n = 80) with quantitative asbestos fiber burden measures. RESULTS: Consistent with the established literature, we found independent, significant associations between male sex and reduced survival (p < 0.04), as well as between nonepithelioid tumor histology and reduced survival (p < 0.02). Although self-reported exposure to asbestos was not predictive of survival among our cases, stratifying quantitative asbestos fiber burden [number of asbestos bodies per gram of lung (wet weight)] into groups of low (0-99 asbestos bodies), moderate (100-1,099), and high fiber burden (> 1,099), suggested a survival duration association among these groups (p = 0.06). After adjusting for covariates in a Cox model, we found that patients with a low asbestos burden had a 3-fold elevated risk of death compared to patients with a moderate fiber burden [95% confidence interval (CI), 0.95-9.5; p = 0.06], and patients with a high asbestos burden had a 4.8-fold elevated risk of death (95% CI, 1.5-15.0; p < 0.01) versus those with moderate exposure. CONCLUSION: Our data suggest that patient survival is associated with asbestos fiber burden in MPM and is perhaps modified by susceptibility. [ABSTRACT FROM AUTHOR]

Background: Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner. Results: We propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age. Conclusion: Our proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data. [ABSTRACT FROM AUTHOR]

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health‐related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell‐type deconvolution with EAA predicted by six widely‐used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging‐related diseases, precision medicine, and therapeutic interventions. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNA) in extracellular vesicles and particles (EVPs) in maternal circulation during pregnancy and in human milk postpartum are hypothesized to facilitate maternal–offspring communication via epigenetic regulation. However, factors influencing maternal EVP miRNA profiles during these two critical developmental windows remain largely unknown. In a pilot study of 54 mother–child dyads in the New Hampshire Birth Cohort Study, we profiled 798 EVP miRNAs, using the NanoString nCounter platform, in paired maternal second-trimester plasma and mature (6-week) milk samples. In adjusted models, total EVP miRNA counts were lower for plasma samples collected in the afternoon compared with the morning (p = 0.024). Infant age at sample collection was inversely associated with total miRNA counts in human milk EVPs (p = 0.040). Milk EVP miRNA counts were also lower among participants who were multiparous after delivery (p = 0.047), had a pre-pregnancy BMI > 25 kg/m2 (p = 0.037), or delivered their baby via cesarean section (p = 0.021). In post hoc analyses, we also identified 22 specific EVP miRNA that were lower among participants who delivered their baby via cesarean section (Q < 0.05). Target genes of delivery mode-associated miRNAs were over-represented in pathways related to satiety signaling in infants (e.g., CCKR signaling) and mammary gland development and lactation (e.g., FGF signaling, EGF receptor signaling). In conclusion, we identified several key factors that may influence maternal EVP miRNA composition during two critical developmental windows, which should be considered in future studies investigating EVP miRNA roles in maternal and child health. [ABSTRACT FROM AUTHOR]

Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by the Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration. [ABSTRACT FROM AUTHOR]

Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real‐time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real‐time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real‐time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well‐differentiated tumours, and to map tumours to their original anatomical position/orientation. [ABSTRACT FROM AUTHOR]

Background: Among men, prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer death. Etiologic factors associated with both prostate carcinogenesis and somatic alterations in tumors are incompletely understood. While genetic variants associated with PCa have been identified, epigenetic alterations in PCa are relatively understudied. To date, DNA methylation (DNAm) and gene expression (GE) in PCa have been investigated; however, these studies did not correct for cell-type proportions of the tumor microenvironment (TME), which could confound results. Methods: The data (GSE183040) consisted of DNAm and GE data from both tumor and adjacent non-tumor prostate tissue of 56 patients who underwent radical prostatectomies prior to any treatment. This study builds upon previous studies that examined methylation patterns and GE in PCa patients by using a novel tumor deconvolution approach to identify and correct for cell-type proportions of the TME in its epigenome-wide association study (EWAS) and differential expression analysis (DEA). Results: The inclusion of cell-type proportions in EWASs and DEAs reduced the scope of significant alterations associated with PCa. We identified 2,093 significantly differentially methylated CpGs (DMC), and 51 genes associated with PCa, including PCA3, SPINK1, and AMACR. Conclusions: This work illustrates the importance of correcting for cell types of the TME when performing EWASs and DEAs on PCa samples, and establishes a more confounding-adverse methodology. We identified a more tumor-cell-specific set of altered genes and epigenetic marks that can be further investigated as potential biomarkers of disease or potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Deep learning models can infer cancer patient prognosis from molecular and anatomic pathology information. Recent studies that leveraged information from complementary multimodal data improved prognostication, further illustrating the potential utility of such methods. However, current approaches: 1) do not comprehensively leverage biological and histomorphological relationships and 2) make use of emerging strategies to "pretrain" models (i.e., train models on a slightly orthogonal dataset/modeling objective) which may aid prognostication by reducing the amount of information required for achieving optimal performance. In addition, model interpretation is crucial for facilitating the clinical adoption of deep learning methods by fostering practitioner understanding and trust in the technology. Methods: Here, we develop an interpretable multimodal modeling framework that combines DNA methylation, gene expression, and histopathology (i.e., tissue slides) data, and we compare performance of crossmodal pretraining, contrastive learning, and transfer learning versus the standard procedure. Results: Our models outperform the existing state-of-the-art method (average 11.54% C-index increase), and baseline clinically driven models (average 11.7% C-index increase). Model interpretations elucidate consideration of biologically meaningful factors in making prognosis predictions. Discussion: Our results demonstrate that the selection of pretraining strategies is crucial for obtaining highly accurate prognostication models, even more so than devising an innovative model architecture, and further emphasize the all-important role of the tumor microenvironment on disease progression. [ABSTRACT FROM AUTHOR]

This pilot study sought to evaluate the circulating levels of immune cells, particularly regulatory T-cell (Treg) subsets, before and after lung resection for non-small cell lung cancer. Twenty-five patients consented and had specimens collected. Initially, peripheral blood of 21 patients was collected for circulating immune cell studies. Two of these patients were excluded due to technical issues, leaving 19 patients for the analyses of circulating immune cells. Standard gating and high-dimensional unsupervised clustering flow cytometry analyses were performed. The blood, tumors and lymph nodes were analyzed via single-cell RNA and TCR sequencing for Treg analyses in a total of five patients (including four additional patients from the initial 21 patients). Standard gating flow cytometry revealed a transient increase in neutrophils immediately following surgery, with a variable neutrophil–lymphocyte ratio and a stable CD4–CD8 ratio. Unexpectedly, the total Treg and Treg subsets did not change with surgery with standard gating in short- or long-term follow-up. Similarly, unsupervised clustering of Tregs revealed a dominant cluster that was stable perioperatively and long-term. Two small FoxP3hi clusters slightly increased following surgery. In the longer-term follow-up, these small FoxP3hi Treg clusters were not identified, indicating that they were likely a response to surgery. Single-cell sequencing demonstrated six CD4+FoxP3+ clusters among the blood, tumors and lymph nodes. These clusters had a variable expression of FoxP3, and several were mainly, or only, present in tumor and lymph node tissue. As such, serial monitoring of circulating Tregs may be informative, but not completely reflective of the Tregs present in the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Background: We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM). Aims: We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia. Methods: Spatial–temporal data were extracted from consecutive 3D-HDAM studies performed between 2018 and 2020 at Dartmouth-Hitchcock Health. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity. Results: 302 3D-HDAM studies representing 1208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy [AUC 0.91 (95% confidence interval 0.89–0.93)] to traditional [AUC 0.93(0.92–0.95)] and hybrid [AUC 0.96(0.94–0.97)] predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive [odds ratio 4.21(2.78–6.38)] versus traditional/hybrid approaches. Conclusions: Deep learning is capable of considering complex spatial–temporal information on 3D-HDAM technology. Future studies are needed to evaluate the clinical context of these preliminary findings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1-10 cells) spots per patient sampled in a honeycomb pattern, coregistered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x-y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural networks to predict spatial RNA patterns at the Visium spots under the hypothesis that the transformer- and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model’s ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, the results indicate that the transformer- and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on under studied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article presents a study which explores the phenotypic heterogeneity driven by plasticity of the intermediate epithelial-to-mesenchymal transition (EMT) state governs disease progression and metastasis in breast cancer. It mentions that study reveals complex EMT spectrum and its regulatory networks, as well as the contributions of epithelial-mesenchymal plasticity (EMP) in tumor heterogeneity in breast cancer.

Natural killer (NK)-cell phenotype is partially mediated through binding of killer-cell immunoglobulin-like receptors (KIR) with HLA class I ligands. The KIR gene family is highly polymorphic and not well captured by standard genome-wide association study approaches. Here, we tested the hypothesis that variations in KIR gene content combined with HLA class I ligand status is associated with keratinocyte skin cancers using a population- based study of basal cell carcinoma (BCC) and squamous cell carcinomas (SCC). We conducted an interaction analysis of KIR gene content variation and HLA-B (Bw4 vs. Bw6) and HLA-C (C1 vs. C2). KIR centromeric B haplotype was associated with significant risk of multiple BCC tumors (OR, 2.39; 95% confidence interval, 1.10-5.21), and there was a significant interaction between HLA-C and the activating gene KIR2DS3 for BCC (Pinteraction = 0.005). Furthermore, there was significant interaction between HLA-B and telomeric KIR B haplotype (containing the activating genes KIR3DS1 and KIR2DS1) as well as HLA-B and the activating KIR gene KIR2DS5 (Pinteraction 0.001 and 0.012, respectively). Similar but greatly attenuated associations were observed for SCC. Moreover, previous in vitro models demonstrated that p53 is required for upregulation of NK ligands, and accordingly, we observed there was a strong association between the KIR B haplotype and p53 alteration in BCC tumors, with a higher likelihood that KIR B carriers harbor abnormal p53 (P < 0.004). Taken together, our data suggest that functional interactions between KIR and HLA modify risks of BCC and SCC and that KIR encoded by the B genes provides selective pressure for altered p53 in BCC tumors. [ABSTRACT FROM AUTHOR]

Background: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). Methods: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. Results: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. Conclusions: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach. [ABSTRACT FROM AUTHOR]

Cytomegalovirus (CMV) is a highly prevalent human herpes virus that exerts a strong influence on immune repertoire which may influence cancer risk. We have tested whether CMV immunoglobulin G (IgG) serostatus is associated with immune cell proportions (n = 132 population controls), human papillomavirus (HPV) co-infection and head and neck cancer risk (n = 184 cancer cases and 188 controls) and patient survival. CMV status was not associated with the proportion of Natural Killer cells, B cells or the neutrophil-to-lymphocyte ratio. However, CD8+ T cells increased with increasing categories of IgG titers (P =1.7 × 10−10), and titers were inversely associated with the CD4:CD8 ratio (P = 5.6 × 10−5). Despite these differences in T cell proportions, CMV was not associated with HPV16 co-infection. CMV seropositivity was similar in cases (52%) and controls (47%) and was not associated with patient survival (hazard ratio [HR] 1.14, 95% confidence interval [CI]: 0.70 to 1.86). However, those patients with the highest titers had the worst survival (HR 1.91, 95% CI: 1.13 to 3.23). Tumor-based data from The Cancer Genome Atlas demonstrated that the presence of CMV transcripts was associated with worse patient survival (HR 1.79, 95% CI: 0.96 to 2.78). These findings confirm that a history of CMV infection alters T cell proportions, but this does not translate to HPV16 co-infection or head and neck cancer risk. Our data suggest that high titers and active CMV virus in the tumor environment may confer worse survival. [ABSTRACT FROM AUTHOR]

Background: Tumor-based classification of human glioma portends patient prognosis, but 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, which included a training set of 197 patients with IDH-wild type, 1p19q intact, TERT wild type (IDH/1p19q/TERT-WT) glioma, an evaluation set of 350 patients with other subtypes of glioma, and 454 patients 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 people without glioma. Recursive partitioning analysis delineated 4 statistically significantly different survival groups for patients with IDH/1p19q/TERT-WT based on an interaction between chronological age and 2 blood immune factors, CD4+ T cells, and neutrophils. Median overall survival ranged from 0.76 years (95% confidence interval = 0.55-0.99) for the worst survival group (n = 28) to 9.72 years (95% confidence interval = 6.18 to not available) for the best (n = 33). The recursive partitioning analysis also statistically significantly delineated 4 risk groups in patients with other glioma subtypes.Conclusions: 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 affect survival. The ability of DNA methylation-based markers of immune status to capture diverse, clinically relevant information may facilitate noninvasive, personalized patient evaluation in the neuro-oncology clinic. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: The use of sodium bisulfite (BS) treatment followed by hybridization to an Illumina Infinium BeadChip (HumanMethylation450 and MethylationEPIC) is a common method for interrogating 5-methylcytosine (5mC) at single nucleotide resolution. However, standard treatment of DNA with BS does not allow disambiguation of 5mC from an additional cytosine modification, 5- hydroxymethylcytosine (5hmC). Recently, it has been demonstrated that paired BS and oxidative bisulfite (oxBS) treatment on the same sample followed by hybridization to an Infinium microarray permits the differentiation of 5hmC from 5mC. Nevertheless, estimation of 5hmC and 5mC from tandem-treated arrays has been shown to produce irregular estimates of cytosine modifications. Results: We present a novel method using maximum likelihood estimation to accurately estimate the parameters of unmethylated cytosine (5C), 5mC and 5hmC from Infinium microarray data given the signal intensities from the oxBS and BS replicates. [ABSTRACT FROM AUTHOR]

Immunity and inflammatory pathways are important in the genesis of non-melanoma skin cancers (NMSC). Functional genetic variation in immune modulators has the potential to affect disease etiology. We investigated associations between common variants in two key regulators, MIR146A and RNASEL, and their relation to NMSCs. Using a large population-based case-control study of basal cell (BCC) and squamous cell carcinoma (SCC), we investigated the impact of MIR146A SNP rs2910164 on cancer risk, and interaction with a SNP in one of its putative targets (RNASEL, rs486907). To examine associations between genotype and BCC and SCC, occurrence odds ratios (OR) and 95% confidence intervals (95%CI) were calculated using unconditional logistic regression, accounting for multiple confounding factors. We did not observe an overall change in the odds ratios for SCC or BCC among individuals carrying either of the RNASEL or MIR146A variants compared with those who were wild type at these loci. However, there was a sex-specific association between BCC and MIR146A in women (ORGC = 0.73, [95%CI = 0.52–1.03]; ORCC = 0.29, [95% CI = 0.14–0.61], p-trend<0.001), and a reduction in risk, albeit not statistically significant, associated with RNASEL and SCC in men (ORAG = 0.88, [95%CI = 0.65–1.19]; ORAA = 0.68, [95%CI = 0.43–1.08], p-trend = 0.10). Most striking was the strong interaction between the two genes. Among individuals carrying variant alleles of both rs2910164 and rs486907, we observed inverse relationships with SCC (ORSCC = 0.56, [95%CI = 0.38–0.81], p-interaction = 0.012) and BCC (ORBCC = 0.57, [95%CI = 0.40–0.80], p-interaction = 0.005). Our results suggest that genetic variation in immune and inflammatory regulators may influence susceptibility to NMSC, and novel SNP-SNP interaction for a microRNA and its target. These data suggest that RNASEL, an enzyme involved in RNA turnover, is controlled by miR-146a and may be important in NMSC etiology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AAs) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA of all other taxa, and those false changes could lead to false positive/negative findings. We propose a novel analysis approach (IFAA) to make robust inference on AA of an ecosystem that can circumvent the issues induced by the CD problem and compositional structure of RA. IFAA can also address the issues of overdispersion and handle zero-inflated data structures. IFAA identifies microbial taxa associated with the covariates in Phase 1 and estimates the association parameters by employing an independent reference taxon in Phase 2. Two real data applications are presented and extensive simulations show that IFAA outperforms other established existing approaches by a big margin in the presence of unbalanced library size. for this article are available online. [ABSTRACT FROM AUTHOR]

Background: In previous studies using candidate gene approaches, low sperm count (oligospermia) has been associated with altered sperm mRNA content and DNA methylation in both imprinted and non-imprinted genes. We performed a genome-wide analysis of sperm DNA methylation and mRNA content to test for associations with sperm function Methods and Results: Sperm DNA and mRNA were isolated from 21 men with a range of semen parameters presenting to a tertiary male reproductive health clinic. DNA methylation was measured with the Illumina Infinium array at 27,578 CpG loci. Unsupervised clustering of methylation data differentiated the 21 sperm samples by their motility values. Recursively partitioned mixture modeling (RPMM) of methylation data resulted in four distinct methylation profiles that were significantly associated with sperm motility (P = 0.01). Linear models of microarray analysis (LIMMA) was performed based on motility and identified 9,189 CpG loci with significantly altered methylation (Q,0.05) in the low motility samples. In addition, the majority of these disrupted CpG loci (80%) were hypomethylated. Of the aberrantly methylated CpGs, 194 were associated with imprinted genes and were almost equally distributed into hypermethylated (predominantly paternally expressed) and hypomethylated (predominantly maternally expressed) groups. Sperm mRNA was measured with the Human Gene 1.0 ST Affymetrix GeneChip Array. LIMMA analysis identified 20 candidate transcripts as differentially present in low motility sperm, including HDAC1 (NCBI 3065), SIRT3 (NCBI 23410), and DNMT3A (NCBI 1788). There was a trend among altered expression of these epigenetic regulatory genes and RPMM DNA methylation class. Conclusions: Using integrative genome-wide approaches we identified CpG methylation profiles and mRNA alterations associated with low sperm motility [ABSTRACT FROM AUTHOR]

P53 is a key regulatory molecule in the cellular response to ultraviolet radiation, and TP53 mutation is the most common alteration in non-melanoma skin cancer. The MDM2 oncogene negatively regulates p53 protein levels, and both genes have functional polymorphisms that may modify skin cancer risk. Furthermore, prior research suggests that TP53 mutations preferentially occur on the arginine allele to selectively inactivate the p63 pathway. We tested these hypotheses of susceptibility and preferential mutation in non-melanoma skin cancer. The TP53 Arg72Pro and MDM2 309 polymorphisms were genotyped in a population-based case–control study of non-melanoma skin cancer, and TP53 alteration (mutation and immunohistochemistry staining) was evaluated in case tumors. In 902 cases of basal cell carcinoma (BCC), 676 cases of squamous cell carcinoma (SCC) and 812 controls, no association was found between the TP53 polymorphism and risk of non-melanoma skin cancer [odds ratio (OR)BCC 0.98, 95% confidence interval (CI) 0.80–1.20; ORSCC 0.93, 95% CI 0.75–1.16]. However, carriers of the MDM2 SNP309 G allele did have an elevated risk of non-melanoma skin cancer (ORBCC 1.15, 95% CI 0.93–1.42; ORSCC 1.29, 95% CI 1.02–1.63). We observed an association between TP53 alterations in the tumors and constitutive TP53 genotype (P < 0.01), with alterations preferentially occurring on the proline allele. Collectively, these data highlight the significant effects of genotype on gene-specific mutation events in carcinogenesis. [ABSTRACT FROM AUTHOR]

Motivation: Patients with identical cancer diagnoses often progress differently. The disparity we see in disease progression and treatment response can be attributed to the idea that two histologically similar cancers may be completely different diseases on the molecular level. Methods for identifying cancer subtypes associated with patient survival have the capacity to be powerful instruments for understanding the biochemical processes that underlie disease progression as well as providing an initial step toward more personalized therapy for cancer patients. We propose a method called semi-supervised recursively partitioned mixture models (SS-RPMM) that utilizes array-based genetic and patient-level clinical data for finding cancer subtypes that are associated with patient survival.Results: In the proposed SS-RPMM, cancer subtypes are identified using a selected subset of genes that are associated with survival time. Since survival information is used in the gene selection step, this method is semi-supervised. Unlike other semi-supervised clustering classification methods, SS-RPMM does not require specification of the number of cancer subtypes, which is often unknown. In a simulation study, our proposed method compared favorably with other competing semi-supervised methods, including: semi-supervised clustering and supervised principal components analysis. Furthermore, an analysis of mesothelioma cancer data using SS-RPMM, revealed at least two distinct methylation profiles that are informative for survival.Availability: The analyses implemented in this article were carried out using R (http://www.r.project.org/).Contact: devin_koestler@brown.edu; e_andres_houseman@brown.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation beadarray in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment. [ABSTRACT FROM AUTHOR]

Background: Nucleotide-specific 5-hydroxymethylcytosine (5hmC) remains understudied in pediatric central nervous system (CNS) tumors. 5hmC is abundant in the brain, and alterations to 5hmC in adult CNS tumors have been reported. However, traditional approaches to measure DNA methylation do not distinguish between 5-methylcytosine (5mC) and its oxidized counterpart 5hmC, including those used to build CNS tumor DNA methylation classification systems. We measured 5hmC and 5mC epigenome-wide at nucleotide resolution in glioma, ependymoma, and embryonal tumors from children, as well as control pediatric brain tissues using tandem bisulfite and oxidative bisulfite treatments followed by hybridization to the Illumina Methylation EPIC Array that interrogates over 860,000 CpG loci. Results: Linear mixed effects models adjusted for age and sex tested the CpG-specific differences in 5hmC between tumor and non-tumor samples, as well as between tumor subtypes. Results from model-based clustering of tumors was used to test the relation of cluster membership with patient survival through multivariable Cox proportional hazards regression. We also assessed the robustness of multiple epigenetic CNS tumor classification methods to 5mC-specific data in both pediatric and adult CNS tumors. Compared to non-tumor samples, tumors were hypohydroxymethylated across the epigenome and tumor 5hmC localized to regulatory elements crucial to cell identity, including transcription factor binding sites and super-enhancers. Differentially hydroxymethylated loci among tumor subtypes tended to be hypermethylated and disproportionally found in CTCF binding sites and genes related to posttranscriptional RNA regulation, such as DICER1. Model-based clustering results indicated that patients with low 5hmC patterns have poorer overall survival and increased risk of recurrence. Our results suggest 5mC-specific data from OxBS-treated samples impacts methylation-based tumor classification systems giving new opportunities for further refinement of classifiers for both pediatric and adult tumors. Conclusions: We identified that 5hmC localizes to super-enhancers, and genes commonly implicated in pediatric CNS tumors were differentially hypohydroxymethylated. We demonstrated that distinguishing methylation and hydroxymethylation is critical in identifying tumor-related epigenetic changes. These results have implications for patient prognostication, considerations of epigenetic therapy in CNS tumors, and for emerging molecular neuropathology classification approaches. [ABSTRACT FROM AUTHOR]

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules—such as gene promoter context, CpG island relationship, or user-defined groupings—and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules. [ABSTRACT FROM AUTHOR]

Purpose: Germ cell tumors (GCTs) are cured with therapy based on cisplatin, although a clinically significant number of patients are refractory and die of progressive disease. Based on preclinical studies indicating that refractory testicular GCTs are hypersensitive to hypomethylating agents (HMAs), we conducted a phase I trial combining the next‐generation HMA guadecitabine (SGI‐110) with cisplatin in recurrent, cisplatin‐resistant GCT patients. Methods: Patients with metastatic GCTs were treated for five consecutive days with guadecitabine followed by cisplatin on day 8, for a 28‐day cycle for up to six cycles. The primary endpoint was safety and toxicity including dose‐limiting toxicity (DLT) and maximum tolerated dose (MTD). Results: The number of patients enrolled was 14. The majority of patients were heavily pretreated. MTD was determined to be 30 mg/m2 guadecitabine followed by 100 mg/m2 cisplatin. The major DLTs were neutropenia and thrombocytopenia. Three patients had partial responses by RECIST criteria, two of these patients, including one with primary mediastinal disease, completed the study and qualified as complete responses by serum tumor marker criteria with sustained remissions of 5 and 13 months and survival of 16 and 26 months, respectively. The overall response rate was 23%. Three patients also had stable disease indicating a clinical benefit rate of 46%. Conclusions: The combination of guadecitabine and cisplatin was tolerable and demonstrated activity in patients with platinum refractory germ cell cancer. [ABSTRACT FROM AUTHOR]