BackgroundThere 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.ResultsHere 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.ConclusionsOur 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 Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.Methods In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing. FindingsOf 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1•84, 95% CI 1•53-2•21), male sex (1•63, 1•07-2•48), smoking status (former smoker vs never smoked: 1•60, 1•03-2•47), number of comorbidities (two vs none: 4•50, 1•33-15•28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3•89, 2•11-7•18), active cancer (progressing vs remission: 5•20, 2•77-9•77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2•93, 1•79-4•79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0•24, 0•07-0•84) or the US-Midwest (0•50, 0•28-0•90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality. Interpretation Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.

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.

BackgroundAlthough urinary concentrations of phthalate metabolites are frequently used as biomarkers in epidemiologic studies, variability during pregnancy has not been characterized.MethodsWe measured phthalate metabolite concentrations in spot urine samples collected from 246 pregnant Dominican and African-American women. Twenty-eight women had repeat urine samples collected over a 6-week period. We also analyzed 48-hr personal air samples (n = 96 women) and repeated indoor air samples (n = 32 homes) for five phthalate diesters. Mixed-effects models were fit to evaluate reproducibility via intraclass correlation coefficients (ICC). We evaluated the sensitivity and specificity of using a single specimen versus repeat samples to classify a woman’s exposure in the low or high category.ResultsPhthalates were detected in 85–100% of air and urine samples. ICCs for the unadjusted urinary metabolite concentrations ranged from 0.30 for mono-ethyl phthalate to 0.66 for monobenzyl phthalate. For indoor air, ICCs ranged from 0.48 [di-2-ethylhexyl phthalate (DEHP)] to 0.83 [butylbenzyl phthalate (BBzP)]. Air levels of phthalate diesters correlated with their respective urinary metabolite concentrations for BBzP (r = 0.71), di-isobutyl phthalate (r = 0.44), and diethyl phthalate (DEP; r = 0.39). In women sampled late in pregnancy, specific gravity appeared to be more effective than creatinine in adjusting for urine dilution.ConclusionsUrinary concentrations of DEP and DEHP metabolites in pregnant women showed lower reproducibility than metabolites for di-n-butyl phthalate and BBzP. A single indoor air sample may be sufficient to characterize phthalate exposure in the home, whereas urinary phthalate biomarkers should be sampled longitudinally during pregnancy to minimize exposure misclassification.

L1 retrotransposons comprise 17% of the human genome and are its only autonomous mobile elements. Although L1-induced insertional mutagenesis causes Mendelian disease, their mutagenic load in cancer has been elusive. Using L1-targeted resequencing of 16 colorectal tumor and matched normal DNAs, we found that certain cancers were excessively mutagenized by human-specific L1s, while no verifiable insertions were present in normal tissues. We confirmed de novo L1 insertions in malignancy by both validating and sequencing 69/107 tumor-specific insertions and retrieving both 5′ and 3′ junctions for 35. In contrast to germline polymorphic L1s, all insertions were severely 5′ truncated. Validated insertion numbers varied from up to 17 in some tumors to none in three others, and correlated with the age of the patients. Numerous genes with a role in tumorigenesis were targeted, including ODZ3, ROBO2, PTPRM, PCM1, and CDH11. Thus, somatic retrotransposition may play an etiologic role in colorectal cancer.