Carcinogenesis is accompanied by widespread DNA methylation changes within the cell. These changes are characterized by a globally hypomethylated genome with focal hypermethylation of numerous 5’-cytosine-phosphate-guanine-3’ (CpG) islands, often spanning gene promoters and first exons. Many of these epigenetic changes occur early in tumorigenesis and are highly pervasive across a tumor type. This allows DNA methylation cancer biomarkers to be suitable for early detection and also to have utility across a range of areas relevant to cancer detection and treatment. Such tests are also simple in construction, as only one or a few loci need to be targeted for good test coverage. These properties make cancer-associated DNA methylation changes very attractive for development of cancer biomarker tests with substantive clinical utility. Across the patient journey from initial detection, to treatment and then monitoring, there are several points where DNA methylation assays can inform clinical practice. Assays on surgically removed tumor tissue are useful to determine indicators of treatment resistance, prognostication of outcome, or to molecularly characterize, classify, and determine the tissue of origin of a tumor. Cancer-associated DNA methylation changes can also be detected with accuracy in the cell-free DNA present in blood, stool, urine, and other biosamples. Such tests hold great promise for the development of simple, economical, and highly specific cancer detection tests suitable for population-wide screening, with several successfully translated examples already. The ability of circulating tumor DNA liquid biopsy assays to monitor cancer in situ also allows for the ability to monitor response to therapy, to detect minimal residual disease and as an early biomarker for cancer recurrence. This review will summarize existing DNA methylation cancer biomarkers used in clinical practice across the application domains above, discuss what makes a suitable DNA methylation cancer biomarker, and identify barriers to translation. We discuss technical factors such as the analytical performance and product-market fit, factors that contribute to successful downstream investment, including geography, and how this impacts intellectual property, regulatory hurdles, and the future of the marketplace and healthcare system.

BackgroundThe development of colorectal cancer (CRC) is accompanied by extensive epigenetic changes, including frequent regional hypermethylation particularly of gene promoter regions. Specific genes, including SEPT9, VIM1 and TMEFF2 become methylated in a high fraction of cancers and diagnostic assays for detection of cancer-derived methylated DNA sequences in blood and/or fecal samples are being developed. There is considerable potential for the development of new DNA methylation biomarkers or panels to improve the sensitivity and specificity of current cancer detection tests.MethodsCombined epigenomic methods – activation of gene expression in CRC cell lines following DNA demethylating treatment, and two novel methods of genome-wide methylation assessment – were used to identify candidate genes methylated in a high fraction of CRCs. Multiplexed amplicon sequencing of PCR products from bisulfite-treated DNA of matched CRC and non-neoplastic tissue as well as healthy donor peripheral blood was performed using Roche 454 sequencing. Levels of DNA methylation in colorectal tissues and blood were determined by quantitative methylation specific PCR (qMSP).ResultsCombined analyses identified 42 candidate genes for evaluation as DNA methylation biomarkers. DNA methylation profiles of 24 of these genes were characterised by multiplexed bisulfite-sequencing in ten matched tumor/normal tissue samples; differential methylation in CRC was confirmed for 23 of these genes. qMSP assays were developed for 32 genes, including 15 of the sequenced genes, and used to quantify methylation in tumor, adenoma and non-neoplastic colorectal tissue and from healthy donor peripheral blood. 24 of the 32 genes were methylated in >50% of neoplastic samples, including 11 genes that were methylated in 80% or more CRCs and a similar fraction of adenomas.ConclusionsThis study has characterised a panel of 23 genes that show elevated DNA methylation in >50% of CRC tissue relative to non-neoplastic tissue. Six of these genes (SOX21, SLC6A15, NPY, GRASP, ST8SIA1 and ZSCAN18) show very low methylation in non-neoplastic colorectal tissue and are candidate biomarkers for stool-based assays, while 11 genes (BCAT1, COL4A2, DLX5, FGF5, FOXF1, FOXI2, GRASP, IKZF1, IRF4, SDC2 and SOX21) have very low methylation in peripheral blood DNA and are suitable for further evaluation as blood-based diagnostic markers.

Energy homeostasis plays a significant role in food consumption and body weight regulation with fat intake being an area of particular interest due to its palatability and high energy density. Increasing evidence from humans and animal studies indicate the existence of a taste modality responsive to fat via its breakdown product fatty acids. These studies implicate multiple candidate receptors and ion channels for fatty acid taste detection, indicating a complex peripheral physiology that is currently not well understood. Additionally, a limited number of studies suggest a reduced ability to detect fatty acids is associated with obesity and a diet high in fat reduces an individual's ability to detect fatty acids. To support this, genetic variants within candidate fatty acid receptors are also associated with obesity reduced ability to detect fatty acids. Understanding oral peripheral fatty acid transduction mechanisms and the association with fat consumption may provide the basis of novel approaches to control development of obesity.

BackgroundBatch effects are a persistent and pervasive form of measurement noise which undermine the scientific utility of high-throughput genomic datasets. At their most benign, they reduce the power of statistical tests resulting in actual effects going unidentified. At their worst, they constitute confounds and render datasets useless. Attempting to remove batch effects will result in some of the biologically meaningful component of the measurement (i.e. signal) being lost. We present and benchmark a novel technique, called Harman. Harman maximises the removal of batch noise with the constraint that the risk of also losing biologically meaningful component of the measurement is kept to a fraction which is set by the user.ResultsAnalyses of three independent publically available datasets reveal that Harman removes more batch noise and preserves more signal at the same time, than the current leading technique. Results also show that Harman is able to identify and remove batch effects no matter what their relative size compared to other sources of variation in the dataset. Of particular advantage for meta-analyses and data integration is Harman’s superior consistency in achieving comparable noise suppression - signal preservation trade-offs across multiple datasets, with differing number of treatments, replicates and processing batches.ConclusionHarman’s ability to better remove batch noise, and better preserve biologically meaningful signal simultaneously within a single study, and maintain the user-set trade-off between batch noise rejection and signal preservation across different studies makes it an effective alternative method to deal with batch effects in high-throughput genomic datasets. Harman is flexible in terms of the data types it can process. It is available publically as an R package (https://bioconductor.org/packages/release/bioc/html/Harman.html), as well as a compiled Matlab package (http://www.bioinformatics.csiro.au/harman/) which does not require a Matlab license to run.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1212-5) contains supplementary material, which is available to authorized users.

The code for Blue and its related tools are available from http://www.bioinformatics.csiro.au/Blue. These programs are written in C# and run natively under Windows and under Mono on Linux.

Evidence suggests individuals less sensitive to fat taste (high fat taste thresholds (FTT)) may be overweight or obese and consume greater amounts of dietary fat than more sensitive individuals. The aims of this study were to assess associations between FTT, anthropometric measurements, fat intake, and liking of fatty foods. FTT was assessed in 69 Australian females (mean age 41.3 (15.6) (SD) years and mean body mass index 26.3 (5.7) kg/m2) by a 3-alternate forced choice methodology and transformed to an ordinal scale (FT rank). Food liking was assessed by hedonic ratings of high-fat and reduced-fat foods, and a 24-h food recall and food frequency questionnaire was completed. Linear mixed regression models were fitted. FT rank was associated with dietary % energy from fat (sans-serifβ^ = 0.110 [95% CI: 0.003, 0.216]), % energy from carbohydrate (sans-serifβ^ = −0.112 [−0.188, −0.035]), and frequency of consumption of foods per day from food groups: high-fat dairy (sans-serifβ^ = 1.091 [0.106, 2.242]), meat & meat alternatives (sans-serifβ^ = 0.669 [0.168, 1.170]), and grain & cereals (sans-serifβ^ = 0.771 [0.212, 1.329]) (adjusted for energy and age). There were no associations between FT rank and anthropometric measurements or hedonic ratings. Therefore, fat taste sensitivity appears to be associated with short-term fat intake, but not body size in this group of females.

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