Projects
HAMdetector: A Bayesian regression model that integrates information to detect HLA-associated mutations
A key process in anti-viral adaptive immunity is that the human leukocyte antigen (HLA) system presents epitopes as major histocompatibility complex I (MHC I) protein–peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the hosts MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses.
We present a new Bayesian regression model for the detection of HAMs that integrates a sparsity-inducing prior, epitope predictions and phylogenetic bias assessment, and that yields easily interpretable quantitative information on HAM candidates. The model predicts experimentally confirmed HAMs as having high posterior probabilities, and it performs well in comparison to state-of-the-art models for several datasets from individuals infected with HBV, HDV and HIV.
Dissecting drivers of immune activation in chronic HIV-1 infection
This collaboration project with virologist Prof. Hendrik Streeck focused on the characterization of immune parameters and inflammation in chronic HIV infection.
AFRICOS is currently the largest study of immune activation in chronic HIV infection and measures various immune parameters in HIV-infected individuals from different countries and regions in Africa.
Our results show that chronic immune activation in HIV-1 infection is influenced by HIV viral load, sex, age, region, and use of anti-retroviral drugs.
Clinical and molecular characteristics associated with response to therapeutic PD-1/PD-L1 inhibition in advanced Merkel cell carcinoma
Merkel cell carcinoma is a rare and aggressive form of skin cancer with a mortality rate of approximately 33%. New antibody-based therapies are very effective in some patients but not in others. Because there are few and incomplete data on the relationship between patient and tumor characteristics and treatment success, identifying risk factors is particularly challenging.
Using a Bayesian approach, we were able to incorporate partially missing data points into the analysis in a collaborative project and thus learn significantly more from the available data while accounting for uncertainty.