Computational cancer biology
Cancer is a genetic disease caused by mutations to the genome. When such mutations hit critical genetic elements, they perturb cellular signalling resulting in overly proliferative cells. The availability of cheap sequencing technologies has led to large international efforts such as the International Cancer Genome Consortium for charting the genomic lesions leading to cancer. 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.
We have developed statistical models for relating different layers of genomic, molecular and clinical data to extract the precise connections among variables to understand the connection of genotype and phenotype. Moreover we have been working on biostatistical models and informatics tools for predicting outcome based on comprehensive high-dimensional data sets.
Another area of our research are the evolutionary dynamics of cancer. The process of developing cancer is driven by mutation and selection; hence the language to quantify that process is that of evolutionary dynamics. Deep sequencing unmasks the clonal composition of a cancer, which sheds some light on its evolutionary history. Accurate detection of subclonal mutations and reconstruction of phylogenies requires, however, accurate bioinformatics tools that we are actively developing.
Future projects and goals
Starting at the EBI in August 2015, we envisage installing a research programme covering different aspects of computational cancer biology. Part of this research will be conducted in local collaboration, and also within national and international initiatives. Future research will involve developing and deploying tools to decipher mutational signatures based on data of comprehensive screens of genotoxins and genetic repair deficiencies. We will continue developing bioinformatical methods for reconstructing the evolutionary history of cancer using NGS data from individual and multiple samples generated as part of international efforts. Lastly, we will work on statistical methods for data-driven personalised outcome predictions.
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.
E. Papaemmanuil, M. Gerstung, L. Bullinger, et al. (2016). Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med, 374:2209-21.
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
Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F (2015) Cancer evolution: mathematical models and computational inference. Systematic Biology, 64:e1-e25
Gerstung M, Papaemmanuil E, Campbell PJ (2014) Subclonal variant calling with multiple samples and prior knowledge. Bioinformatics, 30:1198-1204