Telomere Biology Disorders (TBDs) are characterized by mutations in telomere-related genes leading to short telomeres and premature aging but with no strict correlation between telomere length and ...disease severity. Epigenetic alterations are also markers of aging and we aimed to evaluate whether DNA methylation (DNAm) could be part of the pathogenesis of TBDs. In blood from 35 TBD cases, genome-wide DNAm were analyzed and the cases were grouped based on relative telomere length (RTL): short (S), with RTL close to normal controls, and extremely short (ES). TBD cases had increased epigenetic age and DNAm alterations were most prominent in the ES-RTL group. Thus, the differentially methylated (DM) CpG sites could be markers of short telomeres but could also be one of the mechanisms contributing to disease phenotype since DNAm alterations were observed in symptomatic, but not asymptomatic, cases with S-RTL. Furthermore, two or more DM-CpGs were identified in four genes previously linked to TBD or telomere length (PRDM8, SMC4, VARS, and WNT6) and in three genes that were novel in telomere biology (MAS1L, NAV2, and TM4FS1). The DM-CpGs in these genes could be markers of aging in hematological cells, but they could also be of relevance for the progression of TBD.
Large B-cell lymphoma (LBCL) is the most common lymphoma and is known to be a biologically heterogeneous disease regarding genetic, phenotypic, and clinical features. Although the prognosis is good, ...one-third has a primary refractory or relapsing disease which underscores the importance of developing predictive biological markers capable of identifying high- and low-risk patients. DNA methylation (DNAm) and telomere maintenance alterations are hallmarks of cancer and aging. Both these alterations may contribute to the heterogeneity of the disease, and potentially influence the prognosis of LBCL.
We studied the DNAm profiles (Infinium MethylationEPIC BeadChip) and relative telomere lengths (RTL) with qPCR of 93 LBCL cases: Diffuse large B-cell lymphoma not otherwise specified (DLBCL, n = 66), High-grade B-cell lymphoma (n = 7), Primary CNS lymphoma (n = 8), and transformation of indolent B-cell lymphoma (n = 12). There was a substantial methylation heterogeneity in DLBCL and other LBCL entities compared to normal cells and other B-cell neoplasms. LBCL cases had a particularly aberrant semimethylated pattern (0.15 ≤ β ≤ 0.8) with large intertumor variation and overall low hypermethylation (β > 0.8). DNAm patterns could not be used to distinguish between germinal center B-cell-like (GC) and non-GC DLBCL cases. In cases treated with R-CHOP-like regimens, a high percentage of global hypomethylation (β < 0.15) was in multivariable analysis associated with worse disease-specific survival (DSS) (HR 6.920, 95% CI 1.499-31.943) and progression-free survival (PFS) (HR 4.923, 95% CI 1.286-18.849) in DLBCL and with worse DSS (HR 5.147, 95% CI 1.239-21.388) in LBCL. These cases with a high percentage of global hypomethylation also had a higher degree of CpG island methylation, including islands in promoter-associated regions, than the cases with less hypomethylation. Additionally, telomere length was heterogenous in LBCL, with a subset of the DLBCL-GC cases accounting for the longest RTL. Short RTL was independently associated with worse DSS (HR 6.011, 95% CI 1.319-27.397) and PFS (HR 4.689, 95% CI 1.102-19.963) in LBCL treated with R-CHOP-like regimens.
We hypothesize that subclones with high global hypomethylation and hypermethylated CpG islands could have advantages in tumor progression, e.g. by inactivating tumor suppressor genes or promoting treatment resistance. Our findings suggest that cases with high global hypomethylation and thus poor prognosis could be candidates for alternative treatment regimens including hypomethylating drugs.
Prematurity in itself and exposure to neonatal intensive care triggers inflammatory processes and oxidative stress, leading to risk for disease later in life. The effects on cellular aging processes ...are incompletely understood.
Relative telomere length (RTL) was measured by qPCR in this longitudinal cohort study with blood samples taken at birth and at 2 years of age from 60 children (16 preterm and 44 term). Viral respiratory infections the first year were evaluated. Epigenetic biological DNA methylation (DNAm) age was predicted based on methylation array data in 23 children (11 preterm and 12 term). RTL change/year and DNAm age change/year was compared in preterm and term during the 2 first years of life.
Preterm infants had longer telomeres than term born at birth and at 2 years of age, but no difference in telomere attrition rate could be detected. Predicted epigenetic DNAm age was younger in preterm infants, but rate of DNAm aging was similar in both groups.
Despite early exposure to risk factors for accelerated cellular aging, children born preterm exhibited preserved telomeres. Stress during the neonatal intensive care period did not reflect accelerated epigenetic DNAm aging. Early-life aging was not explained by preterm birth.
Preterm birth is associated with elevated disease risk later in life. Preterm children often suffer from inflammation early in life. Stress-related telomere erosion during neonatal intensive care has been proposed. Inflammation-accelerated biological aging in preterm is unknown. We find no accelerated aging due to prematurity or infections during the first 2 years of life.
Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer and is associated with poor prognosis if metastasized. Up to one third of patients with local disease at ...diagnosis will develop metastasis after nephrectomy, and there is a need for new molecular markers to identify patients with high risk of tumor progression. In the present study, we performed genome-wide promoter DNA methylation analysis at diagnosis to identify DNA methylation profiles associated with risk for progress.
Diagnostic tissue samples from 115 ccRCC patients were analysed by Illumina HumanMethylation450K arrays and methylation status of 155,931 promoter associated CpGs were related to genetic aberrations, gene expression and clinicopathological parameters.
The ccRCC samples separated into two clusters (cluster A/B) based on genome-wide promoter methylation status. The samples in these clusters differed in tumor diameter (p < 0.001), TNM stage (p < 0.001), morphological grade (p < 0.001), and patients outcome (5 year cancer specific survival (pCSS
) p < 0.001 and cumulative incidence of progress (pCIP
) p < 0.001. An integrated genomic and epigenomic analysis in the ccRCCs, revealed significant correlations between the total number of genetic aberrations and total number of hypermethylated CpGs (R = 0.435, p < 0.001), and predicted mitotic age (R = 0.407, p < 0.001). We identified a promoter methylation classifier (PMC) panel consisting of 172 differently methylated CpGs accompanying progress of disease. Classifying non-metastatic patients using the PMC panel showed that PMC high tumors had a worse prognosis compared with the PMC low tumors (pCIP
38% vs. 8%, p = 0.001), which was confirmed in non-metastatic ccRCCs in the publically available TCGA-KIRC dataset (pCIP
39% vs. 16%, p < 0.001).
DNA methylation analysis at diagnosis in ccRCC has the potential to improve outcome-prediction in non-metastatic patients at diagnosis.
Leukocyte telomere length (LTL) has been shown to predict Alzheimer's disease (AD), albeit inconsistently. Failing to account for the competing risks between AD, other dementia types, and mortality, ...can be an explanation for the inconsistent findings in previous time-to-event analyses. Furthermore, previous studies indicate that the association between LTL and AD is non-linear and may differ depending on apolipoprotein E (APOE) ε4 allele carriage, the strongest genetic AD predictor.
We analyzed whether baseline LTL in interaction with APOE ε4 predicts AD, by following 1306 initially non-demented subjects for 25 years. Gender- and age-residualized LTL (rLTL) was categorized into tertiles of short, medium, and long rLTLs. Two complementary time-to-event models that account for competing risks were used; the Fine-Gray model to estimate the association between the rLTL tertiles and the cumulative incidence of AD, and the cause-specific hazard model to assess whether the cause-specific risk of AD differed between the rLTL groups. Vascular dementia and death were considered competing risk events. Models were adjusted for baseline lifestyle-related risk factors, gender, age, and non-proportional hazards.
After follow-up, 149 were diagnosed with AD, 96 were diagnosed with vascular dementia, 465 died without dementia, and 596 remained healthy. Baseline rLTL and other covariates were assessed on average 8 years before AD onset (range 1-24). APOE ε4-carriers had significantly increased incidence of AD, as well as increased cause-specific AD risk. A significant rLTL-APOE interaction indicated that short rLTL at baseline was significantly associated with an increased incidence of AD among non-APOE ε4-carriers (subdistribution hazard ratio = 3.24, CI 1.404-7.462, P = 0.005), as well as borderline associated with increased cause-specific risk of AD (cause-specific hazard ratio = 1.67, CI 0.947-2.964, P = 0.07). Among APOE ε4-carriers, short or long rLTLs were not significantly associated with AD incidence, nor with the cause-specific risk of AD.
Our findings from two complementary competing risk time-to-event models indicate that short rLTL may be a valuable predictor of the AD incidence in non-APOE ε4-carriers, on average 8 years before AD onset. More generally, the findings highlight the importance of accounting for competing risks, as well as the APOE status of participants in AD biomarker research.
Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic ...disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables.
A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression.
The "triple classifier" which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (
CIP
) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis.
The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.
Telomere length (TL) is regarded as a marker of cellular aging due to the gradual shortening by each cell division, but is influenced by a number of factors including oxidative stress and ...inflammation. Parkinson's disease and atypical forms of parkinsonism occur mainly in the elderly, with oxidative stress and inflammation in afflicted cells. In this study the relationship between blood TL and prognosis of 168 patients with idiopathic parkinsonism (136 Parkinson's disease PD, 17 Progressive Supranuclear Palsy PSP, and 15 Multiple System Atrophy MSA) and 30 controls was investigated. TL and motor and cognitive performance were assessed at baseline (diagnosis) and repeatedly up to three to five years follow up. No difference in TL between controls and patients was shown at baseline, nor any significant difference in TL stability or attrition during follow up. Interestingly, a significant relationship between TL at diagnosis and cognitive phenotype at follow up in PD and PSP patients was found, with longer mean TL at diagnosis in patients that developed dementia within three years.
Despite having common overlapping immunophenotypic and morphological features, T-cell lymphoblastic leukemia (T-ALL) and lymphoma (T-LBL) have distinct clinical manifestations, which may represent ...separate diseases. We investigated and compared the epigenetic and genetic landscape of adult and pediatric T-ALL (n = 77) and T-LBL (n = 15) patient samples by high-resolution genome-wide DNA methylation and Copy Number Variation (CNV) BeadChip arrays. DNA methylation profiling identified the presence of CpG island methylator phenotype (CIMP) subgroups within both pediatric and adult T-LBL and T-ALL. An epigenetic signature of 128 differentially methylated CpG sites was identified, that clustered T-LBL and T-ALL separately. The most significant differentially methylated gene loci included the SGCE/PEG10 shared promoter region, previously implicated in lymphoid malignancies. CNV analysis confirmed overlapping recurrent aberrations between T-ALL and T-LBL, including 9p21.3 (CDKN2A/CDKN2B) deletions. A significantly higher frequency of chromosome 13q14.2 deletions was identified in T-LBL samples (36% in T-LBL vs. 0% in T-ALL). This deletion, encompassing the RB1, MIR15A and MIR16-1 gene loci, has been reported as a recurrent deletion in B-cell malignancies. Our study reveals epigenetic and genetic markers that can distinguish between T-LBL and T-ALL, and deepen the understanding of the biology underlying the diverse disease localization.
Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals ...or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization.
We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions.
The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.