Entirely noninvasive outcome prediction in central nervous system lymphomas using circulating tumor DNA.

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.

MetaboAge DB: a repository of known ageing-related changes in the human metabolome.

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.