Publications by authors named "M Jeanmougin"

In this study, we propose an alternative approach for stratifying genome-scale DNA methylation profiles of gastrointestinal (GI) adenocarcinomas based on a robust analytical framework. A set of 978 GI adenocarcinomas and 120 adjacent normal tissues from public repositories was quality controlled and analyzed. Hierarchical consensus clustering of the tumors, based on differential epigenetic variability between malignant and normal samples, identified six distinct subtypes defined either by a pan-GI or a lower GI-specific phenotype.

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Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers.

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Cystoscopy is the gold standard for surveillance of non-muscle invasive bladder cancer (NMIBC), but the procedure is invasive and has suboptimal accuracy. The aim of this study was to investigate the potential of analyzing urine samples for the presence of urine tumor DNA (utDNA) to replace cystoscopy for surveillance of bladder cancer recurrence. In this longitudinal, prospective, and observational study, 47 patients were followed for recurrence for 2 years, involving analysis of utDNA using the BladMetrix DNA methylation biomarker test at each cystoscopy.

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Background: DNA methylation biomarkers in circulating cell-free DNA (cfDNA) have great clinical potential for cancer management. Most methods for DNA methylation analysis require bisulfite conversion, causing DNA degradation and loss. This is particularly challenging for cfDNA, which is naturally fragmented and normally present in low amounts.

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Motivation: Droplet digital PCR (ddPCR) holds great promises for investigating DNA methylation with high sensitivity. Yet, the lack of methods for analyzing ddPCR DNA methylation data has resulted in users processing the data manually at the expense of standardization.

Results: PoDCall is an R package performing automated calling of positive droplets, quantification and normalization of methylation levels in ddPCR experiments.

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