Computational Cancer Epigenomics
Overview
Recent advances in microarray and sequencing technologies made the genome-wide profiling of DNA methylation and histone modifications feasible and scalable to cohorts containing hundreds or even thousands of samples. Large international consortia, such as the Encylopedia of DNA Elements, The Cancer Genome Atlas Project, the International Cancer Genome Consortium and the International Human Epigenome Consortium have systematically generated comprehensive epigenome-wide datasets on tissue samples and cell lines in human. These vast cohorts open unprecedented opportunities to study the genetic regulation, intracellular heterogeneity and disease progression at the molecular level, but they also bring up enormous challenges in data processing and analysis.
Major Research Directions
- Computational methods for epigenome analysis: deconvolution, phylogenetic analysis and cancer cell-of-origin inference
- Epigenome-based biomarker discovery using machine learning methods
- Integrative analysis of multi-omics datasets in several cancer entities (early-onset prostate cancer, CLL, AML, T-ALL, cholangiocarcinoma, giant-cell tumor of bone)
- User-friendly software tools for processing and analysis of epigenomic data:
- Reproducible bioinformatics, containerized scientific workflows and accessible deployment interfaces
Selected Ongoing Projects
- BMBF Computational Life Sciences Project "BSmadeEZ - Standardizing and scaling bisulfite sequencing data processing using EpiCWL" (2019-2021)
- BMBF de.NBI Partner project de.NBI-Epi/DKFZ (with Prof. B. Brors, 2020-2021)
- Helmholtz AMPro, subproject Lutsik (2020)
- Deutsche Krebshilfe Project "CO-CLL: Epigenomes in the Cells of Origin of Chronic Lymphocytic Leukemia - Implications for disease progression and potential as biomarker" (with Prof. C. Plass, 2020-2023)
Selected Recent Publications
- Lutsik P, Baude A, Mancarella D, Öz S, Kühn A, Toth R, Hey J, Toprak U, Lim J, Nguyen V, Jiang C, Mayakonda A, Hartmann M, Rosemann F, Breuer K, Vonficht D, Grünschläger F, Lee S, Schuhmacher M, Kusevic D, Jauch A, Weichenhan D, Zustin J, Schlesner M, Haas S, Park J, Park Y, Oppermann U, Jeltsch A, Haller F, Fellenberg J, Lindroth A, Plass C. Globally altered epigenetic landscape and delayed osteogenic differentiation in H3.3-G34W-mutant giant cell tumor of bone. Nature Communications 11, 5414 (2020)
- Schönung M, Hess J, Bawidamann P, Stäble S, Hey J, Langstein J, Assenov Y, Weichenhan D, Lutsik P, Lipka DB. AmpliconDesign - an interactive web server for the design of high-throughput targeted DNA methylation assays. Epigenetics, Oct 24:1-7, 2020.
- Mayakonda A, Schönung M, Hey J, Batra RN, Feuerstein-Akgoz C, Köhler K, Lipka DB, Sotillo R, Plass C, Lutsik P, Toth R. Methrix: an R/bioconductor package for systematic aggregation and analysis of bisulfite sequencing data. Bioinformatics, Dec21:btaa1048, 2020
- Scherer M, Nazarov PV, Toth R, Sahay S, Kaoma T, Maurer V, Vedeneev N, Plass C, Lengauer T, Walter J, Lutsik P. Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz. Nature Protocols,15(10): 3240-3263, 2020
- Toth R, Schiffmann H, Hube-Magg C, Büscheck F, Höflmayer D, Weidemann S, Lebok P, Fraune C, Minner S, Schlomm T, Sauter G, Plass C, Assenov Y, Simon R, Meiners J, Gerhäuser C. Random forest-based modelling to detect biomarkers for prostate cancer progression. Clinical Epigenetics 11(1): 148, 2019
- Lutsik P, Slawski M, Gasparoni G, Vedeneev N, Hein M, Walter J. MeDeCom: discovery and quantification of latent components of heterogeneous methylomes. Genome Biology 18(1): 55, 2017
- Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C. Comprehensive analysis of DNA methylation data with RnBeads. Nature Methods, 11(11):1138-1140, 2014
The full list of publications is available as a custom Pubmed query.
Tthe publication lists of individual members can be seen on their personal pages (see Members).