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Purpose: One of the most frequently reported effects of cancer and its treatments is cancer-related cognitive impairment (CRCI). Viral infections may affect inflammation and immune function and therefore may influence patient symptoms, including CRCI. The goal of this study was to describe the prevalence of cytomegalovirus (CMV) infections at diagnosis, during, and after chemotherapy in individuals with ovarian cancer and explore CMV infection at diagnosis with cancer-related cognitive impairment (CRCI) following chemotherapy., Methods: We recruited adults newly diagnosed with ovarian, primary peritoneal or fallopian tube cancer at a single academic cancer center into two prospective studies. In Study 1 (N = 71), participants provided blood samples at diagnosis. In Study 2 (N = 18), participants provided blood samples and completed symptom surveys before, during and after front-line adjuvant chemotherapy. Serum CMV DNA levels were assessed using digital PCR; >100 copies/mL of serum was considered positive for active CMV infection (CMV+). CRCI was measured using the Functional Assessment of Cancer Therapy - Cognitive Function (FACT-Cog) questionnaire. Changes in FACT-Cog scores were compared by CMV status at diagnosis using t-tests at each time point., Results: At diagnosis, 29.2% were CMV+ (28.2% in Study 1, 33.3% in Study 2). Following three cycles of chemotherapy (Study 2), CMV positivity rose to 60.0% and then back down to 31.3% after chemotherapy. We observed significant differences in CRCI following chemotherapy by CMV status at diagnosis., Conclusion: Our data suggest that active CMV infection is common among patients undergoing treatment for ovarian cancer and may contribute to symptoms of CRCI., (© 2023. The Author(s).)

Background: HPV infection is an established risk factor for oropharyngeal cancer, and it has been proposed that cigarette smoking may potentiate HPV infection in the oral epithelium. We sought to test the hypothesis that cigarette smoking increases HPV infection in an HPV16 serology study of cancer-free individuals., Methods: Subjects were participants in a risk factor study for head and neck cancer, and were required to have no prior history of either HNSCC or any other cancer. Tobacco use and other risk factor data were gathered through interviewer-assisted questionnaires, while serology was conducted in a blinded fashion using a glutathione S-transferase capture enzyme-linked immunosorbent assay (ELISA) to detect antibodies against HPV16 L1, E1, E2, E4, E6 and E7 proteins. The differences in tobacco use by HPV serology were evaluated by ANOVA; and the reported odds ratios and 95% confidence intervals were determined by using unconditional logistic regression., Results: We found no overall association of HPV16 serological markers with smoking. However, when the data were stratified by median age, smoking was positively associated with seropositivity for the HPV16 L1 capsid antigen in the younger controls while the older controls were less likely to be HPV16 L1 positive if they smoked (pinteraction < 0.002). There was no similar association of smoking and age with serological response to the early proteins (i.e E6, E7)., Conclusions: Exposure to HPV16 capsid protein (L1) is increased among relatively younger adults who smoke and diminished among older smokers. However, this pattern is not accompanied by a differential susceptibility for active infection (as determined by the early gene proteins such as E6 and E7) among young and older smokers.

Background: Global hypomethylation of repetitive DNA sequences is believed to occur early in tumorigenesis. There is a great interest in identifying factors that contribute to global DNA hypomethylation and associated cancer risk. We tested the hypothesis that plasma S-adenosylmethionine (SAM) level alone or in combination with genetic variation in DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) was associated with global DNA methylation extent at long interspersed nucleotide element-1 (LINE-1) sequences., Methods: Plasma SAM level and LINE-1 DNA methylation index were measured using stored blood samples collected from 440 healthy Singaporean Chinese adults during 1994-1999. Genetic polymorphisms of 13 loci in DNMT1, DNMT3A and DNMT3B were determined., Results: LINE-1 methylation index was significantly higher in men than in women (p = 0.001). LINE-1 methylation index was positively associated with plasma SAM levels (p ≤ 0.01), with a plateau at approximately 78% of LINE-1 methylation index (55 nmol/L plasma SAM) in men and 77% methylation index (50 nmol/L plasma SAM) in women. In men only, the T allele of DNMT1 rs21124724 was associated with a statistically significantly higher LINE-1 methylation index (ptrend = 0.001). The DNMT1 rs2114724 genotype modified the association between plasma SAM and LINE-1 methylation index at low levels of plasma SAM in men., Conclusions: Circulating SAM level was associated with LINE-1 methylation status among healthy Chinese adults. The DNMT1 genetic polymorphism may exert a modifying effect on the association between SAM and LINE-1 methylation status in men, especially when plasma SAM level is low. Our findings support a link between plasma SAM and global DNA methylation status at LINE-1 sequences.

Background: There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls., Results: Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach., Conclusions: Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.

Background: While a possible link between assisted reproductive technology (ART) and rare imprinting disorders has been found, it is not clear if this is indicative of subtler disruptions of epigenetic mechanisms. Results from previous studies have been mixed, but some methylation differences have been observed., Methods: Children conceived through ART and children conceived spontaneously were recruited for this cross-sectional study. Information about reproductive history, demographic factors, birth characteristics, and infertility treatment was obtained from maternal interview and medical records. Peripheral blood lymphocytes and buccal cell samples were collected from participating children. Methylation analysis was performed on five loci using pyrosequencing. Statistical analysis of methylation differences was performed using linear regression with generalized estimating equations. Results are reported as differences with 95% confidence intervals (CI)., Results: A total of 67 ART children and 31 spontaneously conceived (SC) children participated. No significant difference in methylation in lymphocyte samples was observed between groups for any loci. Possible differences were found in buccal cell samples for IGF2 DMR0 (Difference: 2.07; 95% confidence interval (CI): -0.28, 4.42; p = 0.08) and IGF2R (Difference: -2.79; 95% CI: -5.74, 0.16; p = 0.06). Subgroup analysis indicated potential lower methylation in those whose parents used ART for unexplained infertility., Conclusions: Observed differences in methylation between the ART and SC groups were small for all loci in the two sample types examined and no statistical differences were observed. It is still unclear whether or not small differences observed in several studies represent a real difference between groups and if this difference is biologically meaningful. Larger studies with long term follow-up are needed to fully answer these questions.

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.