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Our institute is well embedded into the local, national and international research landscapes. All over, our ambition is to convert high throughput big data into meaningful biological knowledge for improving patient outcome. Researchers with multidisciplinary backgrounds, from biology and bioinformatics to physics and mathematics, are working together to achieve this goal.

In Freiburg, we are working closely with university clinic where the Freiburg’s Molecular Tumor Board (MTB) was established. We built a “personalized decision making” pipeline, based on multi-omics approach including Whole Exome Sequencing, RNA sequencing and Methylome, to counsel clinicians on how to choose the most appropriate treatment for each patient. Together with the Institute for Transfusion Medicine and Gene Therapy, we are developing bio-informatics pipelines to quantify the off-target activity of nucleases such like CRISPR-Cas or TALEN.

To get a better understanding of complex mechanisms like the ones driving cancer proliferation and invasion, or those behind immune-mediated pathology, we are participating and analyzing data from several national and international consortia such like the SFB/CRC 850, SFB1160 IMPATH, COMPASS for pancreatic cancer, DeCaRe for zebrafish heart regeneration.

In-silico methods

We tackle biological questions using a bunch of up-to-date high-throughput omics technologies. In particular, Whole Genome and Exome Sequencing as well as panel gene sequencing are utilized to determine the mutational pattern characterizing diverse diseases like pancreatic ductal adenocarcinoma, colon adenocarcinoma or acute myeloid leukemia among many others. Bulk and single cell RNA sequencing, proteomics, epigenomic (methylome, ChIP- and ATAC-sequencing) allow us to quantify the regulation and alteration of crucial patterns leading to impaired cell behavior. Identified targets can be seen as signaling pathways or even a whole protein-protein interaction network to retrieve a higher level of organization. These different biological layers are highly intertwined and therefore also require integrative approaches. How mutational landscape reshapes transcriptome and proteome of cancer cells?  What is the influence of epigenetic on cell fate? These are some of the questions we are addressing using qualitative and quantitative integration.

Overall, we cope with raw data processing (alignment, quantification, etc.), quality checking, results interpretation and validation. Machine learning approaches facilitate the detection of early stage diseases, as well as the prediction of treatment response and survival. Our outcomes are confronted to independent datasets or biological databases (GEO, TCGA, ICGC, etc.) for validation. Altogether, it eventually contributes to a better understanding of the initiation, progression and hopefully regression of diseases.

Project: Silencing of pancreatic ductal adenocarcinoma (PDAC) biomarker genes in PDAC cell lines by RNA interference

Due to frequent late detection, PDAC is a cancer type with a poor prognosis and low 5-year survival rate. Therefore, early detection at the pre-metastatic stage remains a major goal of translational research.1

By performing a meta-analysis on multiple publicly available independent PDAC cohorts, we have established a diagnostic and prognostic gene signature for PDAC containing 17 robustly regulated genes. This so-called 17-gene classifier not only discriminated PDAC from healthy tissue but also from early pancreatic intraepithelial neoplasia (PanIN) and pancreatitis.2 We experimentally validated the classifier in patient-derived samples on transcript and protein level; two secreted proteins were also detected in patient blood plasma.2

Currently, we are studying the function of individual classifier genes by inactivating their expression in established PDAC cell lines using short-hairpin RNA (shRNA); we are assessing the effects of the gene silencing in vitro, studying proliferative and migratory behavior of the altered cell lines, and in vivo, by injecting the altered cell lines and the respective controls into immunodeficient mice. 

  1. Chari S. T., et al. (2015). Early detection of sporadic pancreatic cancer. Pancreas 44 693–712. 10.1097/MPA.0000000000000368
  2. Klett et al., Front Genet. 2018; 9: 108. doi: 10.3389/fgene.2018.00108

Project: Resistance in Malignant Melanoma

Malignant melanoma is a rare but severe form of skin cancer accounting for about 1% of all skin cancer cases but the overwhelming majority of skin cancer deaths.1 Up to 50% of melanomas carry a mutation in the gene encoding the serine/threonine-kinase rapidly accelerated fibrosarcoma homolog B (BRAF). Therein, an amino acid substitution of valin (V) to glutamic acid (E) in codon 600 (BRAFV600E) is the most common mutation, resulting in BRAF’s constitutive activation and subsequent hyperactivity of the mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) pathway driving melanoma genesis. Multiple molecular subtypes of melanoma contribute to patient heterogeneity. For example, the loss of the tumor suppressor phosphatase and tensin homolog (PTEN), the antagonist of the phosphoinositide-3 kinase (PI3K), resulting in constitutive PI3K activity and subsequent enhanced cell survival and proliferation, is often found in BRAF-mutant melanoma.2

Late-stage melanoma is characterized by a high metastasis rate and intrinsic or acquired treatment resistance to small molecular inhibitors: while targeted inhibition of BRAFV600E is initially effective, most patients develop a resistance to small molecular inhibitors such as vemurafenib within one year.2 Therefore, it is of utmost importance to understand the underlying resistance mechanisms in each individual patient.

In a systems biology approach, we performed a transcriptome analysis with the objective to characterize the individual transcriptional signatures of four late-stage patient-derived melanoma cell lines, all carrying the BRAFV600E mutation. We were able to predict various functional differences in proliferation, cell survival, migration, and invasion, which we confirmed in a cell and molecular biology approach.

To dissect possible resistance mechanisms, we are currently studying the functional changes in proliferation, cell cycle, migration and invasion, as well as metabolic reprogramming of the melanoma cell lines under inhibition of the MEK/ERK or PI3K pathways. We are furthermore focusing on the role of the tumor microenvironment with respect to treatment response and on the analysis of communication of melanoma cells with surrounding fibroblasts as well as with cells of the immune system. 

  1. American Cancer Society. “Cancer Facts and Figures 2020”. Atlanta: American Cancer Society; 2020.
  2. Shtivelman, E. et al. Pathways and therapeutic targets in melanoma. Oncotarget 5, 1701–1752 (2014).