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Clear cell renal cell carcinoma (ccRCC) is characterised by significant genetic heterogeneity, which has diagnostic and prognostic implications. Very limited evidence is available regarding DNA methylation heterogeneity. We therefore generate sequence level DNA methylation data on 136 multi-region tumour and normal kidney tissue from 18 ccRCC patients, along with matched whole exome sequencing (85 samples) and gene expression (47 samples) data on a subset of samples. We perform a comprehensive systematic analysis of heterogeneity between patients, within a patient and within a sample. We demonstrate that bulk methylation data may be deconvoluted into cell-type-specific latent methylation components (LMCs), and that LMC1, which is likely to represent T cells, is associated with prognostic parameters. Differential epipolymorphism was noted between ccRCC and normal tissue in the promoter region of genes which are known to be associated with kidney cancer. This was externally validated in an independent cohort of 71 ccRCC and normal kidney tissues. Differential epipolymorphism in the gene promoter was a predictor of gene expression, after adjusting for average methylation. This represents the first evaluation of epipolymorphism in ccRCC and suggests that gains and losses in methylation disorder may have a functional relevance, gleaning important information on tumourigenesis., Competing Interests: Competing interests: GDS has received educational grants from Pfizer, AstraZeneca, and Intuitive. Surgical; consultancy fees from Pfizer, Merck, MSD, EUSA Pharma, and CMR Surgical; travel expenses from Pfizer; and speaker fees from MSD and Pfizer. GDS is Clinical lead (urology) National Kidney Cancer Audit and Topic Advisor for the NICE kidney cancer guideline. AWY is a member of the Clinical Reference Group for the National Kidney Cancer Audit. All other authors declare no competing interests. Ethics approval and consent to participate: Ethics approval was obtained (DIAMOND; Ethics REC ID 03/018, East of England—Cambridge Central Research Ethics Committee). Written informed consent was obtained from all participants involved in the study. All methods were performed in accordance with the relevant guidelines/regulations and are compliant with good scientific practice., (© 2025. The Author(s).)

Abstract: State-of-the-art response assessment of central nervous system lymphoma (CNSL) by magnetic resonance imaging is challenging and an insufficient predictor of treatment outcomes. Accordingly, the development of novel risk stratification strategies in CNSL is a high unmet medical need. We applied ultrasensitive circulating tumor DNA (ctDNA) sequencing to 146 plasma and cerebrospinal fluid (CSF) samples from 67 patients, aiming to develop an entirely noninvasive dynamic risk model considering clinical and molecular features of CNSL. Our ultrasensitive method allowed for the detection of CNSL-derived mutations in plasma ctDNA with high concordance to CSF and tumor tissue. Undetectable plasma ctDNA at baseline was associated with favorable outcomes. We tracked tumor-specific mutations in plasma-derived ctDNA over time and developed a novel CNSL biomarker based on this information: peripheral residual disease (PRD). Persistence of PRD after treatment was highly predictive of relapse. Integrating established baseline clinical risk factors with assessment of radiographic response and PRD during treatment resulted in the development and independent validation of a novel tool for risk stratification: molecular prognostic index for CNSL (MOP-C). MOP-C proved to be highly predictive of outcomes in patients with CNSL (failure-free survival hazard ratio per risk group of 6.60; 95% confidence interval, 3.12-13.97; P < .0001) and is publicly available at www.mop-c.com. Our results highlight the role of ctDNA sequencing in CNSL. MOP-C has the potential to improve the current standard of clinical risk stratification and radiographic response assessment in patients with CNSL, ultimately paving the way toward individualized treatment., (© 2024 American Society of Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

Accumulating metabolomics data is starting to become extremely useful in understanding the ageing process, by providing a snapshot into the metabolic state of tissues and organs, at different ages. Molecular studies of such metabolic variations during "normal" ageing can hence guide lifestyle changes and/or medical interventions aimed at improving healthspan and perhaps even lifespan. In this work, we present MetaboAge, a freely accessible database which hosts ageing-related metabolite changes, occurring in healthy individuals. Data is automatically filtered and then manually curated from scientific articles reporting statistically significant associations of human metabolite variations or correlations with ageing. Up to date, MetaboAge contains 408 metabolites annotated with their biological and chemical information, and more than 1515 ageing-related variations, graphically represented on the website grouped by validation methods, sex and age-groups. The MetaboAge database aims to continually structure the expanding information from the field of metabolomics in relation to ageing, thus making it more accessible for further research in gerontology.