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Research

Computational Oncology

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Computational Oncology Overview

Berlin

Our groups interest lies in understanding the basis of biological heterogeneity using computational approaches applied to ‘omics data in a disease context. We employ artificial intelligence (AI) and data science (DS) methods, not only to address the underlying causes of this heterogeneity, but also the interactions between different molecular ‘omics layers. We have a strong specialisation in the application area of oncology, but are also very interested in immunology and neurodegenerative diseases.

We believe that by better understanding global and local patterns in biological data, we can better differentiate the mechanistics behind biological and molecular processes from technical noise. As such, we rely on establishing close ties with experimental collaborators who are able to provide us with large scale and high dimensional datasets profiled using state of the art and exotic profiling techniques. We aim to take advantage of recent technological advances that are able to resolve single cell, spatial, temporal and genetic heterogeneity.

Topics

Cancer multiomics and precision oncology

The group has a strong core expertise in analysis and interpretation of cancer multiomics data, with exposure to a wide number of omics layers and tumour entities. We are in particular very interested in understanding the heterogeneous patient response to drugs and are interested in accurate classification of tumour entities, patient specific molecular drivers, tumour microenvironment and TILs, tumour evolution and heterogeneity, and germline predisposing factors.
topic1_cancer_multiomics_colorectal_cancer_pathways_redux
Apoptosis signalling model in colorectal cancer. Together with Prof Heike Algayer we investigated molecular drivers of colorectal cancer using whole genome sequencing. We show that my investigating patterns of gene mutations (shown in the oncoprint, left), we can reconstruct a model of apoptosis signalling (cartoon model, right). Adapted from Ishaque N et al, Nature Commun 2018.

Immunology and allergies
Regulation of innate and adaptive immunity is the primary factor distinguishing healthy and diseased states. Attenuation leaves one susceptible to infections and over activity can lead to autoimmune responses. We have a strong history of collaborating with the group of Irina Lehmann who heads the Molecular Epidemiology department at BIH, and Tobias Boettler and Maike Hoffmann heading the Liver Immunology Lab at Freiburg University Clinic.
topic2_immunity_HCV_CD4Tcells_reduced_v2
Emergence of a Tfh signature on HCV-specific CD4+ T cells after antigen removal. In a collaborative project with Dr Tobias Boettler we investigated expression changes in CD4+ T-cells that were able to recognise HCV, which shifted from a Th1 like phenotype to a Tfh like phenotype. Top row shows the tSNE embedding of HCV-specific T-cells based on FACS marker data, second row shows embedding of various T-cell lineages. Adapted from Smits M et al, JCI 2019.

Spatially resolved transcriptomics
Advances in single cell sequencing has led to better understanding of heterogeneous cell types and cell states. Even more recent advances in in situ transcriptomics allows one to resolve not only cell type and state, but also spatial location. With this information we can, for example, identify the location of different tumour infiltration immune cells in relation to the tumour mass. In the framework of the HCA and with help from the spaceTX consortium, we have recently published a tool called SSAM - the first computational framework for segmentation free cell-type and tissue domain analysis of in situ transcriptomics data.
topic3_spatial_transcriptomics_SSAM_osmFISH_SSp_redux
Reconstruction of the mouse somatosensory cortex (SSp) cell types and tissue structure using SSAM. We applied our SSAM tool to spatially resolved transcriptomics data generated by osmFISH (Codeluppi S et al, 2019). The reconstruction of the cell-type map reveals the cell-type organisation in the tissue (left), and the tissue domain map of cell-type composition motifs identifies cortical layers with the mouse SSp (right). Adapted from Park J et al, bioRxiv 2019.

Community engagement
We are active participants and contributors to various ELIXIR networks, and heavily involved the German ELIXIR node, de.NBI. Committed to the spirit of academic collaboration and transparency, we organise numerous conferences and workshops.
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NaveedIshaque_sq
Naveed obtained a M.Sc. in Bioinformatics and Systems Biology at Imperial College London and undertook his Ph.D. under Jonathan Jones at Sainsbury Laboratory in Norwich, focusing on NGS applications determining host-pathogen interactions. He joined Roland Eils’s lab at DKFZ in Heidelberg in 2013. In 2018, he moved with Roland Eils to Berlin where he leads the Computational Oncology research group.

Naveed Ishaque, Ph.D.

Group leader at BIH Center for Digital Health

BIH Center for Digital Health
Kapelle-Ufer 2
10117 Berlin

Research Group

Dr. Sebastian Mackowiak
Senior Bioinformatician
BIH Center for Digital Health
Kapelle-Ufer 2
10117 Berlin
sebastian.mackowiak@charite.de
Olivia Debnath
Doctoral Student
BIH Center for Digital Health
Kapelle-Ufer 2
10117 Berlin
olivia.debnath@charite.de
Sebastian Tiesmeyer
Doctoral Student
BIH Center for Digital Health
Kapelle-Ufer 2
10117 Berlin
sebastian.tiesmeyer@charite.de
Shashwat Sahay
Doctoral Student
BIH Center for Digital Health
Kapelle-Ufer 2
10117 Berlin
shashwat.sahay@charite.de

Publications

Park, J.*, Choi, W.*, Tiesmeyer, S., Long, B., Borm, L.E., Garren, E., Nguyen, T.N., Codeluppi, S., Schlesner, M., Tasic, B., Eils, R.§ & Ishaque, N.§ (2019). Segmentation-free inference of cell types from in situ transcriptomics data. bioRxiv, doi: 10.1101/800748

 

Mallm, J.-P.*, Iskar, M.*, Ishaque, N.*, Klett, L.C., Kugler, S.J., Muino, J.M., Teif, V.B., Poos, A.M., Großmann, S., Erdel, F., Tavernari, D., Koser, S.D., Schumacher, S., Brors, B., König, R., Remondini, D., Vingron, M., Stilgenbauer, S., Lichter, P., Zapatka, M., Mertens, D.§ & Rippe, K.§ (2019). Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks. Molecular Systems Biology, 22;15(5):e8339. doi: 10.15252/msb.20188339

 

Smits, M., Zoldan, K., Ishaque, N., Gu, Z., Jechow, K., Wieland, D., Conrad, C., Eils, R., Fauvelle, C., Baumert, T.F., Emmerich, F., Bengsch, B., Neumann-Haefelin, C., Hofmann, M., Thimme, R. & Boettler, T.§ (2019). Follicular T helper cells shape the HCV-specific CD4 T cell repertoire after viral elimination. The Journal of Clinocal Investigation, 3;130(2):998-1009. doi: 10.1172/JCI129642

 

Ishaque, N.*, Abba, M.L.*, Hauser, C., Patil, N., Paramasivam, N., Huebschmann, D., Leupold, J.H., Balasubramanian, G.P., Kleinheinz, K., Toprak, U.H., Hutter, B., Benner, A., Shavinskaya, A., Zhou, C., Gu, Z., Kerssemakers, J., Marx, A., Moniuszko, M., Kozlowski, M., Reszec, J., Niklinski, J., Eils, J., Schlesner, M., Eils, R., Brors, B. & Allgayer, H.§ (2018). Whole genome sequencing puts forward hypotheses on metastasis evolution and therapy in colorectal cancer. Nature Communications, 9(1), 4782. doi: 10.1038/s41467-018-07041-z

 

Wegert, J.*, Ishaque, N.*, Vardapour, R., Geörg, C., Gu, Z., Bieg, M., Ziegler, B., Bausenwein, S., Nourkami, N., Ludwig, N., Keller, A., Grimm, C., Kneitz, S., Williams, R.D., Chagtai, T., Pritchard-Jones, K., van Sluis, P., Volckmann, R., Koster, J., Versteeg, R., Acha, T., O'Sullivan, M.J., Bode, P.K., Niggli, F., Tytgat, G.A., van Tinteren, H., van den Heuvel-Eibrink, M.M., Meese, E., Vokuhl, C., Leuschner, I., Graf, N., Eils, R., Pfister, S.M., Kool, M.§ & Gessler, M.§ (2015). Mutations in the SIX1/2 pathway and the DROSHA/DGCR8 miRNA microprocessor complex underlie high-risk blastemal type Wilms tumors. Cancer Cell, 9;27(2):298-311. doi: 10.1016/j.ccell.2015.01.002 

 

*these authors contributed equally

§corresponding author

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