Computational BiomedicineMenden Lab
The mission of our research group is to develop biostatistical and machine learning frameworks applied to biomedical data, to retrieve insights in the aetiology of complex diseases and identify novel intervention strategies. For this, we explore deep molecular characterised biomedical datasets, environmental factors, and tailor our models depending on disease specific knowledge gained through collaborations, literature and data driven analyses, thus empowering the next generation of drug target identification, drug repositioning and precision medicine.
Computational Cancer Pharmacogenomics
We have strong expertise is in computational method development for cancer pharmacogenomics including the analysis of monotherapy and drug combination high-throughput screens, and lately, CRISPR lethality and drug resistance screens, which is highlighted by our recent ERC Starting Grant. The Menden Lab customises machine learning and biostatistical methods to predict drug sensitivity and synergy, as well as derived genetic biomarkers of these responses. Our work focuses on clinical translatability and interaction with the microenvironment, and thus enables patient stratification based on deep molecular profiles, which is the key pillar of precision.
Translational Computational Pharmacogenomics
The foundation of our endeavour is our strong expertise in Computational Cancer Pharmacogenomics (ERC StG), which we envision to generalise to the Translational Computational Pharmacogenomics. In particular, we expand our research focus totuberculosis drug resistance (bayresq.net, UNITE4TB), inflammatory skin diseases(IGSSE), neurodegenerative diseases (MUDS) and diabetes (DZD). Common across all projects, we are experts in analysing deep molecular characterisations of patients and disease models to stratify samples into drug responders or phenotypes, which ultimately enables precision medicine. In essence, our research vision is to establish a Translational Computational Pharmacogenomics programme in cancer and beyond, in order to accelerate the delivery of urgently needed targeted therapies.
Research Group/Lab: Scientists at …
Dr. Michael MendenJunior Group Leader
Ginte KutkaitePhD candidate
Alexander Joschua OhnmachtPhD candidate
Ana GalhozPhD candidate
Christina HilligPhD candidate
Phong NguyenPhD candidate
Ines AssumPhD candidate
Dr. Diyuan LuPostDoc
Martin MeinelPhD candidate
Daniel GargerPhd candidate
Göksu AvarPhD candidate
Nikita MakarovPhD candidate
Clara MeijsPhD Candidate
Research Group: PublicationsSee all
Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.
Patients with seemingly the same tumour can respond very differently to treatment. There are strong, well-established effects of somatic mutations on drug efficacy, but there is at-most anecdotal evidence of a germline component to drug response. Here, we report a systematic survey of how inherited germline variants affect drug susceptibility in cancer cell lines. We develop a joint analysis approach that leverages both germline and somatic variants, before applying it to screening data from 993 cell lines and 265 drugs. Surprisingly, we find that the germline contribution to variation in drug susceptibility can be as large or larger than effects due to somatic mutations. Several of the associations identified have a direct relationship to the drug target. Finally, using 17-AAG response as an example, we show how germline effects in combination with transcriptomic data can be leveraged for improved patient stratification and to identify new markers for drug sensitivity.
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
2020 Cell Patterns