The common objective of developing and applying bioinformatics tools to better understand cancer biology fosters close interaction between the groups inside the Division of Applied Bioinformatics and cooperation with the Computational Oncology group in the Division of Theoretical Bioinformatics.
Comparative Cancer Genomics
The Comparative Cancer Genomics team lead by Dr. Lars Feuerbach focuses on integrating sequencing and array data across tumor subtypes and patient cohorts. In these Pan-Cancer studies the similarities and differences in the interplay of epigenome, genome and transcriptome during carciogenesis are investigated in context of three main interests:
Focus 1 - Replicative Immortality and telomere biology in cancer
Focus 2 - Identification of regulatory cancer driver mutations for precision oncology applications
Focus 3 - Software tool development for the analysis of cancer cohorts
Furthermore, following up on the ICGC Early-onset Prostate Cancer consortium, we study prostate cancer as part of the Pan Prostate Cancer group of the ICGC ARGO project.
Our methodological expertise for computational cancer genome analysis comprises algorithm development, specialized datastructures for multi-omics data integration, data mining by machine learning, compact visualization of complex information, and statistical modeling.
Computational Oncoimmunology
The Computational Oncoimmunology team lead by Charles Imbusch focuses on questions regarding the immune system in general and more specifically under pathogenic conditions such as cancer. To address these questions NGS, as well as array technologies, are routinely utilized.
With the advent of multiple single cell technologies it is now possible to differentiate subpopulations captured in an assay, allowing to address cell heterogeneity and the discovery of rare cell populations. We focus on the downstream analysis of scRNA-seq and scTCR to robustly identify and describe cell populations, study dynamic cross-talk between tumor and immune cells while keeping up to date with most recent algorithmic developments.
Alternatively to single cell assays we apply and develop methods to deconvolute from bulk data cell types, integrating epigenetic and transcriptomic data to describe the tumor microenvironment.
Tumor Heterogeneity and Evolution
The Tumor Heterogeneity and Evolution team lead by Dr. Sadaf Mughal focuses on the application of computational methods to analyze omics data from patient cohorts to understand cancer heterogeneity, tumor evolution and to decipher the immune crosstalk in cancer progression and metastatic spread. We are particularly interested in learning drug resistance mechanisms tumor employ during evolution. For this we are investigating longitudinal samples to predict the evolutionary trajectories of aggressive metastatic subclones. Furthermore, we are studying the tumor-immune coevolution to address inter- and intra-tumoural heterogeneity, dictating progression of disease.