NFG027

Come and join the NFG027 Junior Research Group!

The project will start soon and we are currently actively recruiting PhD student researchers! The next deadline for applications for which you need to have a few documents ready, including a short research proposal is November 2nd, 2023. Thus, if you are interested, please contact us as soon as possible to discuss details about the project and on how to apply!

Project summary

Currently available therapies are mostly designed for the "average" patient and neglect patient-to-patient heterogeneity. However, these “one-size-fits-all” treatment approaches are not suitable for most complex diseases, such as cancers, as they do not take into account the molecular properties of an individual disease. The aim of the project is to use artificial intelligence approaches, which are already changing the professional world in many ways, to discover previously unknown molecular signatures and to unlock their biomarker potential for personalized medicine. By analyzing the signatures in terms of their occurrence within certain cancer types and their association with disease progression, we will develop predictive models that can support personalized diagnostics and clinical decision making in the future, which therefore can improve the lives of patients and clinical professionals lastingly.

Figure 1 | Project aims overview and integration. Aims 1 & 2 will uncover molecular signatures. Aim 3 will relate these signatures to clinical outcomes to uncover potent biomarkers and unlock the predictive potential of signatures towards improved clinical decision making in the future.

Project aims

Mutational signatures turn out to be of great value for cancer analytics. They are inferable from genome-scale mutations and can hint towards dysfunctional cellular mechanisms, such as DNA mismatch repair deficiency and the associated molecular cancer subtype, which enables to use them as stable biomarkers for clinical decision making. However, even though the set of identified signatures has grown since their first discovery, the number of molecular signatures that are currently considered of clinical relevance is relatively small. The aim of this project is to decipher genome-scale somatic alterations and gene activity changes observed in human cancers to unveil and characterize known as well as novel types of molecular signatures in order to make them available for precision analytics and clinical decision support in the future (Figure). To reach this overarching vision, we will work on the following three sub-goals, each of which will be tackled by an excellence research fellow:

• Aim 1: Unravelling novel mutational signatures for precision analytics
• Aim 2: Discovery and characterization of gene regulatory signatures
• Aim 3: Unlocking the signatures potential for precision analytics