SummarySystematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

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Human aging cannot be fully understood in terms of the constrained genetic setting. Epigenetic drift is an alternative means of explaining age-associated alterations. To address this issue, we performed whole-genome bisulfite sequencing (WGBS) of newborn and centenarian genomes. The centenarian DNA had a lower DNA methylation content and a reduced correlation in the methylation status of neighboring cytosine-phosphate-guanine (CpGs) throughout the genome in comparison with the more homogeneously methylated newborn DNA. The more hypomethylated CpGs observed in the centenarian DNA compared with the neonate covered all genomic compartments, such as promoters, exonic, intronic, and intergenic regions. For regulatory regions, the most hypomethylated sequences in the centenarian DNA were present mainly at CpG-poor promoters and in tissue-specific genes, whereas a greater level of DNA methylation was observed in CpG island promoters. We extended the study to a larger cohort of newborn and nonagenarian samples using a 450,000 CpG-site DNA methylation microarray that reinforced the observation of more hypomethylated DNA sequences in the advanced age group. WGBS and 450,000 analyses of middle-age individuals demonstrated DNA methylomes in the crossroad between the newborn and the nonagenarian/centenarian groups. Our study constitutes a unique DNA methylation analysis of the extreme points of human life at a single-nucleotide resolution level.epigenomics | longevity D uring human aging, progressive impairment of organ and tissue functionality leads to an increasing probability of death. The molecular culprits behind this decline in physiological activities remain largely unknown. Studies of transcriptional and genomic associations in distinct tissues have identified several gene families and cellular pathways that might contribute to aging and alter lifespan. These families include the Sirtuins, DNA repair enzymes, insulin-signaling pathway/forkhead transcription factors, apolipoproteins, telomere biology, and oxidative damage/ mitochondrial metabolism (1, 2). Aging-associated mechanisms apparently involve many networks within a given cell. Considering that epigenetic regulation has emerged as a critical driver of cell fate and survival that targets many pathways (3, 4), that epigenetic drift can occur even in genetically identical humans (5, 6), and that DNA methylation patterns are disrupted in a wide range of common human diseases (7-11), we wondered whether individuals at the most extreme points of their lifespan had different DNA methylomes. To address this issue, we used whole-genome bisulfite sequencing (WGBS) (12-16) and a 450,000 CpG DNA methylation microarray to examine the DNA methylation profiles of newborn and nonagenarian/centenarian samples.Results and Discussion WGBS of Newborn and Centenarian DNA. The initial data were generated from the cord blood of a newborn (male Caucasian; NB) and from a centenarian (103-y-old male Caucasian; Y103) using DNA extracted from CD4 + T cells processed through an Illumina G...

Aim:DNA methylation is the best known epigenetic mark. Cancer and other pathologies show an altered DNA methylome. However, delivering complete DNA methylation maps is compromised by the price and labor-intensive interpretation of single nucleotide methods.Material & methods:Following the success of the HumanMethylation450 BeadChip (Infinium) methylation microarray (450K), we report the technical and biological validation of the newly developed MethylationEPIC BeadChip (Infinium) microarray that covers over 850,000 CpG methylation sites (850K). The 850K microarray contains >90% of the 450K sites, but adds 333,265 CpGs located in enhancer regions identified by the ENCODE and FANTOM5 projects.Results & conclusion:The 850K array demonstrates high reproducibility at the 450K CpG sites, is consistent among technical replicates, is reliable in the matched study of fresh frozen versus formalin-fixed paraffin-embeded samples and is also useful for 5-hydroxymethylcytosine. These results highlight the value of the MethylationEPIC BeadChip as a useful tool for the analysis of the DNA methylation profile of the human genome.

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DNA methylation patterns are important for establishing cell, tissue, and organism phenotypes, but little is known about their contribution to natural human variation. To determine their contribution to variability, we have generated genome-scale DNA methylation profiles of three human populations (Caucasian-American, African-American, and Han Chinese-American) and examined the differentially methylated CpG sites. The distinctly methylated genes identified suggest an influence of DNA methylation on phenotype differences, such as susceptibility to certain diseases and pathogens, and response to drugs and environmental agents. DNA methylation differences can be partially traced back to genetic variation, suggesting that differentially methylated CpG sites serve as evolutionarily established mediators between the genetic code and phenotypic variability. Notably, one-third of the DNA methylation differences were not associated with any genetic variation, suggesting that variation in population-specific sites takes place at the genetic and epigenetic levels, highlighting the contribution of epigenetic modification to natural human variation.

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

A B S T R A C T PurposeNon-small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard. Patients and MethodsA DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC. ResultsUnsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95% CI, 1.29 to 4.28; P ϭ .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high-and low-risk stage I NSCLC (HR, 3.24; 95% CI, 1.61 to 6.54; P ϭ .001). ConclusionThe DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging.