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Samuel Schäfer

Working to better understand how OMICs data can be used for disease identification, classification, and therapeutic predictions for individual patients.

Individualized treatment for patients with immune-mediated inflammatory disease

Immune-mediated inflammatory disease (IMID) includes many common diseases such as inflammatory bowel disease, rheumatoid arthritis, and psoriatic arthritis, affects millions of people worldwide and can cause chronic pain, disability, and reduced quality of life. It also entails a substantial socioeconomic cost. A major problem is that many patients do not respond to medication. One explanation is the enormous complexity of the diseases, where altered gene expression of thousands of genes can change the biological processes in many different cell types and tissues. Gene expression can also vary between patients with the same diagnosis.

Despite the cellular and molecular differences between individuals, patients with the same diagnosis often receive the same treatment in conventional health care. However, this approach ignores the fact that patient’s disease differs on a cellular and molecular level.

In my studies, I am investigating whether it is possible to improve the treatment outcomes of patients by using complex individual gene expression changes in drug selection through the construction of digital twins.

Digital twins are high-resolution models of an individual patient's disease that can be produced in unlimited copies to simulate treatment with thousands of drugs, aiming to find the optimal treatment for that particular patient. The digital twins are constructed through network and AI-based analyses of relevant OMICs data.

An illustration of the digital twin strategy for personalised medicine.
The digital twin strategy for personalised medicine. A) An individual patient with a local sign of disease (red). B) A digital twin of the patient is constructed in unlimited copies, based on high-performance computational integration of thousands of disease-relevant molecular and clinical variables. C) Each twin is computationally treated with one or more of thousands of drugs. This results in digital cure of one twin (green). D) The drug that cured the twin is selected for treatment of the patient. Figure reference: B Björnsson et al., Genome Medicine 12 (1), 4 (2019).



Samuel Schäfer, Martin Smelik, Oleg Sysoev, Yelin Zhao, Desiré Eklund, Sandra Lilja, Mika Gustafsson, Holger Heyn, Antonio Julia, Istvan A. Kovacs, Joseph Loscalzo, Sara Marsal, Huan Zhang, Xinxiu Li, Danuta Gawel, Hui Wang, Mikael Benson (2024) scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases Genome Medicine, Vol. 16, Article 42 Continue to DOI


Xinxiu Li, Eun Jung Jung Lee, Sandra Lilja, Joseph Loscalzo, Samuel Schäfer, Martin Smelik, Maria Regina Strobl, Oleg Sysoev, Hui Wang, Huan Zhang, Yelin Zhao, Danuta Gawel, Barbara Bohle, Mikael Benson (2022) A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets Genome Medicine, Vol. 14, Article 48 Continue to DOI


Samuel Schäfer, Kevin Wang, Felicia Sundling, Jean Yang, Anthony Liu, Ralph Nanan (2021) Modelling maternal and perinatal risk factors to predict poorly controlled childhood asthma PLOS ONE, Vol. 16, Article e0252215 Continue to DOI
Samuel Schäfer, Felicia Sundling, Anthony Liu, David Raubenheimer, Ralph Nanan (2021) Firstborn sex defines early childhood growth of subsequent siblings Proceedings of the Royal Society of London. Biological Sciences, Vol. 288, Article 20202329 Continue to DOI


Eun Jung Jung Lee, Danuta Gawel, Sandra Lilja, Xinxiu Li, Samuel Schäfer, Oleg Sysoev, Huan Zhang, Mikael Benson (2020) Analysis of expression profiling data suggests explanation for difficulties in finding biomarkers for nasal polyps Rhinology, Vol. 58, p. 360-367 Continue to DOI