Computational Precision Oncology via Patient-Specific Tumor Biomarkers
Computational Precision Oncology via Patient-Specific Tumor Biomarkers
Our research aims to identify patient specific tumour biomarkers for applying precision medicine to cancer patients. The search for biomarkers focuses mainly on alterations in alternative splicing, mutations in the non-coding regions of the genome and the creation of conformotypic biomarkers in the proteome. For the investigation, we integrate NGS based whole-genome and RNA sequencing data, mass-spectrometry based proteomics data, experimental X-ray protein structure data, interaction network data and patient records.
The interaction between the immune system and cancer cells is highly complex, involving various cellular factors of the innate and adaptive immune system. The major immune factor in shaping tumour evolution is cytotoxic CD8+ T-lymphocyte, which under normal condition recognises and destroys nascent tumours. The recognition is driven by the interaction between T-cell receptor and HLA-1 molecules. The latter protein presents peptides on the cell surface, which in case of neoantigen can harbour mutations that can be recognised by T-cells as non-self. Nascent tumours can escape the destruction by over-expressing various immune-checkpoint proteins, such as CTLA-4 or PD-L1. Immune-checkpoint inhibitors work against this mode of action and reactivate T-cells by binding and inhibiting CTLA-4, PD-1 or PD-L1. Once reactivated, the question arises whether and which neoantigens are recognize by CD8+ T-cells in order to induce the destruction of the tumour cells. It is of paramount importance to understand this relationship for stratifying patients prior to immunotherapy.
Aberrant alternative splicing is omnipresent in cancer . The study of its genetic causes has been facilitated by new advancements in DNA and RNA sequencing and the possibility to associate genome-wide non-coding mutations with differential isoform expression . But which of the non-coding mutations and differentially used spliced variants impact cancer development and progression? To find answers to these urgent questions, I am developing computational methods and databases within the PCAWG project ICGC for estimating the degree of network rewiring of non-coding cancer mutations and aberrant alternative splice isoforms.
Protein-protein interaction networks integrated with structural information of proteins have emerged as a powerful tool for studying the enrichment and functional impact of cancer driver mutations [3,4]. The power of such networks will certainly increase given that the number of structural templates for predicting physical protein-protein interactions based on homology is nearing completion . I have therefore started to work on a set of new interaction networks that will fully exploit structural and genomics data for the identification of coding and non-coding driver mutations in various cancers.
Genomic variations in cancer have large impacts on gene expression, the abundances of proteins, their posttranslational modifications, and their metabolic efficacy. While next generation sequencing and proteomics approaches are being used to investigate the differential abundances and modifications of genes and proteins in cancerous cells, there has not been a technology to probe the conformational changes of the proteome on a globular scale [6,7]. The conformation however is key to the function of the protein and its ability to form functional complexes. Knowing which proteins undergo conformational changes in cancer therefore will give insight into the activity of single proteins and whole cellular signaling pathway. Together with Prof. Paola Picotti from the ETH Zurich, I am developing a new structural proteomics technology based on the combination of Limited Proteolysis and targeted mass spectrometry (LiP-SRM) to probe the conformational changes of cancer proteins on a global scale.
While proteins execute their biochemical function often via binary physical interactions, their cellular functions are mainly accomplished via the formation of protein complexes. Cancer cells have many deregulated protein complexes . Traditionally, these multimeric protein complexes have been studied by X-ray crystallography and cryo-Electron-Microscopy (cryo-EM). Recently, structural proteomics techniques like chemical Cross-linking Mass Spectrometry (CX-MS) and LiP-SRM have emerged as powerful complementary techniques with lower resolution but less stringent requirements for sample purity and concentration . To improve the structure determination of cancer related protein complexes using hybrid modeling, I am working on a new integrated molecular modeling driven chemical cross-linking method that will intertwine computational modeling within the chemical cross-linking protocol.
Born in the German city Wetzlar, I did my undergraduate study in bioinformatics at the University of Applied Sciences Giessen. After receiving my diploma degree, I took the opportunity to do my Ph.D. at the University of Cambridge and the European Bioinformatics Insitute under the supervision of Prof Dame Janet M Thornton. With the successful defense of my Ph.D. thesis, I moved to the Institute of Molecular Systems Biology to do a PostDoc with Prof. Ruedi Aebersold at the ETH Zurich and later to the lab of Prof. Paola Picotti for a short research scientist position. Lastly, I was a senior PostDoc with Prof. Christian von Mering at the Institute of Molecular Life Sciences at the University of Zurich.
26. Reyna, M.A., Haan, D., Paczkowska, M., Verbeke, L.P.C., Vazquez, M., Kahraman A. et al. (2018), Pathway and network analysis of more than 2,500 whole cancer genomes. bioRxiv 385294.
25. Rheinbay, E. et al. (2018) Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes. bioRxiv 237313.
24. Schopper, S., Kahraman, A., Leuenberger, P., Feng, Y., Piazza, I., Müller, O., et al. (2017). Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry. Nature Protocols, 12(11), 2391–2410.
23. Wang X., Cimermancic P., Yu C., Schweitzer A., Chopra N., Engel J.L., Greenberg C.H., Huzzah A.S., Beck F., Sakata E., Yang Y., Novitsky E.J., Leitner A., Nanni P., Kahraman A., Guo X., Dixon J.E., Rychnovsky S.D., Aebersold R., Baumeister W., Sali A., Huang L. (2017). Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome. Molecular & Cellular Proteomics : MCP, 16(5), 840–854.
22. Leuenberger, P., Ganscha, S., Kahraman, A., Cappelletti, V., Boersema, P.J., von Mering, C., Claassen, M., Picotti, P. (2017). Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability. Science 355, 812, eaai7825.
21. SIB Swiss Institute of Bioinformatics Members. (2016) The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases. Nucleic Acids Res 44, D27–D37.
20. Grimm, M., Zimniak, T., Kahraman, A., Herzog, F. (2015). xVis: a webserver for the schematic visualization and interpretation of crosslink-derived spatial restraints, Nucleic Acids Res. 43, W362–9.
19. Valleliana, F., Garcia-Rubiod, I., Pugliaa, M., Kahraman, A., Deuel, J.W., Engelsberger, W.R., Mason, R.P., Buehlerg, P.W., Schaer, D.J. (2015). Spin trapping combined with quantitative mass spectrometry defines free radical redistribution within the oxidized hemoglobin:haptoglobin complex. Free Radic. Biol. Med. 85, 259–268.
18. Boersema, P., Kahraman, A., Picotti, P. (2015). Proteomics beyond large-scale protein expression analysis. Current Opinion in Biotechnology 34, 162-170.
17. Robinson, M.D., Kahraman, A. , Law, C.W., Lindsay, H., Nowicka, M., Weber, L.M., Zhou, X., (2014). Statistical methods for detecting differentially methylated loci and regions. Front Genet. (5) 324.
16. Feng, Y.*, De Franceschi, G.*, Kahraman, A.*, Soste, M., Melnik, A., Boersema, P., Polverino de Laureto, P., Nikoaev, Y., Oliveira, A.P., Picotti, P. (2014). Global analysis of protein structural changes in complex proteomes. Nature Biotech 32, 1036–1044.
15. Merkley, E.D., Rysavy, S., Kahraman, A., Hafen, R.P., Daggett, V. and Adkins, J.N. (2014). Distance restraints from cross-linking mass spectrometry: Mining a molecular dynamics simulation database to evaluate lysine-lysine distances Protein Science 23 (6), 747-759.
14. Kahraman, A.*, Herzog, F.*, Leitner, A., Rosenberger, G., Aebersold, R., Malmström, L. (2013). Cross-Link Guided Molecular Modeling with ROSETTA. PLoS ONE 8(9): e73411.
13. Herzog, F.*, Kahraman, A.*, Bohringer, D.*, Mak, R., Bracher, A., Walzthoeni, T., Leitner, A., Beck, M., Hartl, F. U., Ban, N, Malmstroem, L., Aebersold, R. (2012). Structural probing of a protein phosphatase 2A network by chemical cross-linking and mass spectrometry. Science 337, 1348–1352.
12. Kahraman, A., Malmström, L., Aebersold, R. (2011). Xwalk: Computing and Visualizing Distances in Cross-linking Experiments. Bioinformatics 27, 2163-2164.
11. Leitner, A., Kahraman, A.*, , Walzthoeni, T.*, Herzog, F., Rinner, O., Beck, M. and Aebersold, R. (2010). Probing native protein structures by chemical cross-linking, mass spectrometry and bioinformatics. Mol Cell Proteomics 9, 1634-1649.
9. Kahraman, A., Morris, R. J., Laskowski, R.M., Favia, A.D. & Thornton, J. M. (2010). On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins. Proteins 78, 1120-36, PMID: 19927322.
8. Kahraman, A. (2009). The Geometry and Physicochemistry of Protein Binding Sites and Ligands and their Detection in Electron Density Maps. University of Cambridge. PhD Thesis.
7. Kahraman, A., Thornton, J. M. (2008). Methods for the analysis of enzyme binding site. Computational Structural Biology: Methods and Applications, Editors: Torsten Schwede, Manuel C. Peitsch. (Amazon).
6. Kahraman, A., Morris, R. J., Laskowski, R. A. & Thornton, J. M. (2007). Variation of geometrical and physicochemical properties in protein binding pockets and their ligands. BMC Bioinformatics 8, S1.
4. Morris, R. J., Kahraman, A., Funkhouser, T., Stockwell, G., Glaser, F., Laskowski, R. & Thornton, J. M. (2005). Binding pocket shape analysis for protein function prediction. Quantitative Biology, Shape Analysis, and Wavelets, Leeds University Press, Leeds, 91–94.
3. Morris, R. J., Kahraman, A. & Thornton, J. M. (2005). Binding Pocket Shape Analysis for Protein Function Prediction. Acta Crystallographica Section A 61, C156–157.
2. Morris, R. J., Najmanovich, R. J., Kahraman, A. & Thornton, J. M. (2005). Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons. Bioinformatics 21, 2347-55, PMID: 15728116.
1. Kahraman, A., Avramov, A., Nashev. L., Popov, D., Ternes, R., Pohlenz, H.D., and Weiss, B. (2005). PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics. Bioinformatics 21, 418-420, PMID: 15374875.
Xwalk: Prediction, Validation and Visualisation of Chemical Cross-Link Data.
Chemical cross-linking of proteins or protein complexes and the mass spectrometry based localization of the cross-linked amino acids is a powerful method for generating distance restraints on the substrate’s topology. Xwalk was written to predict and validate these cross-links on existing protein structures. Xwalk calculates and displays non-linear distances between chemically cross-linked amino acids on protein surfaces, while mimicking the flexibility and non-linearity of cross-linker molecules. It returns a Solvent Accessible Surface Distance, which corresponds to the length of the shortest path between two amino acids, where the path leads through solvent occupied space without penetrating the protein surface.
CleftXplorer: Geometrical and Physicochemical Analysis and Comparison of Protein Binding Pockets and Ligands.
Compare protein binding pockets with each other or small molecules using spherical harmonics. Analyse the electrostatic potential, hydrophobicity, hydrogen bond pattern and van der Waals forces in protein binding pockets and small molecules. Assess the complementarity between proteins and small molecules.
PhenomicDB: Multi-Organism Phenotype-Genotype Database.
PhenomicDB is a multi-organism phenotype-genotype database including human, mouse, fruit fly, C.elegans, and other model organisms.
The inclusion of gene indices (NCBI Gene) and orthologues (same gene in different organisms) from HomoloGene allows to compare phenotypes of a given gene over many organisms simultaneously.
University Hospital Zurich
Institute for Pathology and Molecular Pathology
Molecular Tumor Profiling lab
8952 Schlieren, Zurich