In a study recently published in Nature Communications, researchers from Berlin, Potsdam, and Jena present a new method for analyzing the epigenome. The machine-learning method identifies differentially methylated DNA regions without sample labels – a prerequisite for many existing algorithms. This makes it possible to identify previously hidden biological patterns as well as new subgroups of cells or diseases. Analyses of blood cells, pancreatic cancer, and brain tumors confirms known biological relationships and reveals new regulatory processes. The metilene3 method opens up new possibilities for better understanding disease mechanisms and identifying potential biomarkers. Quelle: IDW Informationsdienst Wissenschaft

A research team from the University of Cologne has identified a new approach for treating particularly aggressive pancreatic cancer. It makes use of a genetic mutation that allows the immune system to attack the cancer cells again / publication in ‘Nature Communications’ Quelle: IDW Informationsdienst Wissenschaft

Muhammad A Shah, Emily L Denton, Cheryl H Arrowsmith, Mathieu Lupien and Matthieu Schapira Abstract Background The notion that epigenetic mechanisms may be central to cancer initiation and progression is supported by recent next-generation sequencing efforts revealing that genes involved in chromatin-mediated signaling are recurrently mutated in cancer patients. Results Here, we analyze mutational and transcriptional profiles from TCGA and the ICGC across a collection 441 chromatin factors and histones. Chromatin factors essential for rapid replication are frequently overexpressed, and those that maintain genome stability frequently mutated. We identify novel mutation hotspots such as K36M in histone H3.1, and uncover…