Computational cancer biology
Cancer is a genetic disease caused by mutations to the genome. International efforts such as the International Cancer Genome Consortium (ICGC) have charted the genomic lesions leading to cancer at unprecedented detail and in tens of thousands of patients. A revelation of these projects was an even greater genomic complexity of cancer genomes than previously anticipated: Despite having the same disease each patient harbours a unique constellation of mutations. The genetic complexity of cancer is a challenge and an opportunity at the same time. A challenge to understand the underlying mechanisms of cancer development - and an opportunity for finding an explanation for differences in therapy success and outcome.
Data-driven cancer research
The group uses statistical approaches to enhance the quantitative understanding of cancer. This is critical to extract meaningful signals from big molecular data sets, such as genomics and transcriptomics, as well as imaging and large longitudinal records for thousands to millions of patients.
Specific research questions address
- the molecular mechanism of mutations,
- the evolutionary dynamics driving cancer,
- translational applications to predict the future trajectory from pre-malignant to malignant disease, and
- prognostic and clinical decision support algorithms
To address these questions we develop and utilise statistical algorithms to discriminate signal from noise in large data sets using high-dimensional statistical learning theory, but also employ machine and deep learning methods. In addition to answering quantative research questions and developing algorithms we also build clinical decision support tools.
Selected publications
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S. González, N. Volkova, P. Beer, and M. Gerstung. (2017). Immuno-oncology from the perspective of somatic evolution. Semin Cancer Biol.
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M. Gerstung, E. Papaemmanuil, I. Martincorena, et al. (2017). Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet, 49:332-340.
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I. Martincorena, K. M. Raine, M. Gerstung, et al. (2017). Universal Patterns of Selection in Cancer and Somatic Tissues. Cell, 171:1029-1041.e21.
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E. Papaemmanuil, M. Gerstung, L. Bullinger, et al. (2016). Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med, 374:2209-21.
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Gerstung M, Pellagatti A, Malcovati L, et al. (2015) Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nature Communications, 6:5901
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Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F (2015) Cancer evolution: mathematical models and computational inference. Systematic Biology, 64:e1-e25
RECOMB Computational Cancer Biology
We are co-organising the RECOMB-CCB Workshop in Paris, Apr 19-20. Call for papers, abstracts and posters is currently open.