Publications by authors named "Clemens Messerschmidt"

11 Publications

  • Page 1 of 1

YAP and β-catenin cooperate to drive oncogenesis in basal breast cancer.

Cancer Res 2021 Feb 11. Epub 2021 Feb 11.

Cancer research program, Max Delbrueck Center for Molecular Medicine

Targeting cancer stem cells (CSC) can serve as an effective approach toward limiting resistance to therapies. While basal-like (triple-negative) breast cancers encompass cells with CSC features, rational therapies remain poorly established. We show here that the receptor tyrosine kinase Met promotes YAP activity in basal-like breast cancer and find enhanced YAP activity within the CSC population. Interfering with YAP activity delayed basal-like cancer formation, prevented luminal to basal trans-differentiation, and reduced CSC. YAP knockout mammary glands revealed a decrease in β-catenin target genes, suggesting that YAP is required for nuclear β-catenin activity. Mechanistically, nuclear YAP interacted with β-catenin and TEAD4 at gene regulatory elements. Proteomic patient data revealed an upregulation of the YAP signature in basal-like breast cancers. Our findings demonstrate that in basal-like breast cancers, β-catenin activity is dependent on YAP signalling and controls the CSC program. These findings suggest that targeting the YAP/TEAD4/β-catenin complex offers a potential therapeutic strategy for eradicating CSCs in basal-like breast cancers.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-2801DOI Listing
February 2021

Multiplexed Quantification of Four Neuroblastoma DNA Targets in a Single Droplet Digital PCR Reaction.

J Mol Diagn 2020 Aug 26. Epub 2020 Aug 26.

Department of Pediatric Hematology and Oncology, Charité-Universitätsmedizin Berlin, Berlin, Germany; Neuroblastoma Research Group, Experimental and Clinical Research Center, Berlin, Germany.

The detection and characterization of cell-free DNA (cfDNA) in peripheral blood from neuroblastoma patients may serve as a minimally invasive approach to liquid biopsy. Major challenges in the analysis of cfDNA purified from blood samples are small sample volumes and low cfDNA concentrations. Droplet digital PCR (ddPCR) is a technology suitable for analyzing low levels of cfDNA. Reported here are two quadruplexed ddPCR assay protocols that reliably quantify MYCN and ALK copy numbers in a single reaction together with the two reference genes, NAGK and AFF3, and accurately estimate ALK (exon 23 position 3522, C>A) and ALK (exon 25 position 3824, G>A) mutant allele fractions using cfDNA as input. The separation of positive and negative droplets was optimized for detecting two targets in each ddPCR fluorescence channel by the adjustment of the probe and primer concentrations of each target molecule. The quadruplexed assays were validated using a panel of 10 neuroblastoma cell lines and paired blood plasma and primary neuroblastoma samples from nine patients. Accuracy and sensitivity thresholds in quadruplexed assays corresponded well with those from the respective duplexed assays. Presented are two robust quadruplexed ddPCR protocols applicable in the routine clinical setting and that require only minimal plasma volumes for the assessment of MYCN and ALK oncogene status.
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http://dx.doi.org/10.1016/j.jmoldx.2020.07.006DOI Listing
August 2020

Distinct immune evasion in APOBEC-enriched, HPV-negative HNSCC.

Int J Cancer 2020 Oct 18;147(8):2293-2302. Epub 2020 Jun 18.

Department of Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Hindenburgdamm 30, Berlin, 12203, Germany.

Immune checkpoint inhibition leads to response in some patients with head and neck squamous cell carcinoma (HNSCC). Robust biomarkers are lacking to date. We analyzed viral status, gene expression signatures, mutational load and mutational signatures in whole exome and RNA-sequencing data of the HNSCC TCGA dataset (n = 496) and a validation set (DKTK MASTER cohort, n = 10). Public single-cell gene expression data from 17 HPV-negative HNSCC were separately reanalyzed. APOBEC3-associated TCW motif mutations but not total single nucleotide variant burden were significantly associated with inflammation. This association was restricted to HPV-negative HNSCC samples. An APOBEC-enriched, HPV-negative subgroup was identified, that showed higher T-cell inflammation and immune checkpoint expression, as well as expression of APOBEC3 genes. Mutations in immune-evasion pathways were also enriched in these tumors. Analysis of single-cell sequencing data identified expression of APOBEC3B and 3C genes in malignant cells. We identified an APOBEC-enriched subgroup of HPV-negative HNSCC with a distinct immunogenic phenotype, potentially mediating response to immunotherapy.
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http://dx.doi.org/10.1002/ijc.33123DOI Listing
October 2020

Acquired resistance to DZNep-mediated apoptosis is associated with copy number gains of AHCY in a B-cell lymphoma model.

BMC Cancer 2020 May 14;20(1):427. Epub 2020 May 14.

Department of Experimental Hematopathology, Institute of Pathology, Charité Medical University, Berlin, Charitéplatz 1, 10117, Berlin, Germany.

Background: Enhancer of zeste homolog 2 (EZH2) is considered an important driver of tumor development and progression by its histone modifying capabilities. Inhibition of EZH2 activity is thought to be a potent treatment option for eligible cancer patients with an aberrant EZH2 expression profile, thus the indirect EZH2 inhibitor 3-Deazaneplanocin A (DZNep) is currently under evaluation for its clinical utility. Although DZNep blocks proliferation and induces apoptosis in different tumor types including lymphomas, acquired resistance to DZNep may limit its clinical application.

Methods: To investigate possible mechanisms of acquired DZNep resistance in B-cell lymphomas, we generated a DZNep-resistant clone from a previously DZNep-sensitive B-cell lymphoma cell line by long-term treatment with increasing concentrations of DZNep (ranging from 200 to 2000 nM) and compared the molecular profiles of resistant and wild-type clones. This comparison was done using molecular techniques such as flow cytometry, copy number variation assay (OncoScan and TaqMan assays), fluorescence in situ hybridization, Western blot, immunohistochemistry and metabolomics analysis.

Results: Whole exome sequencing did not indicate the acquisition of biologically meaningful single nucleotide variants. Analysis of copy number alterations, however, demonstrated among other acquired imbalances an amplification (about 30 times) of the S-adenosyl-L-homocysteine hydrolase (AHCY) gene in the resistant clone. AHCY is a direct target of DZNep and is critically involved in the biological methylation process, where it catalyzes the reversible hydrolysis of S-adenosyl-L-homocysteine to L-homocysteine and adenosine. The amplification of the AHCY gene is paralleled by strong overexpression of AHCY at both the transcriptional and protein level, and persists upon culturing the resistant clone in a DZNep-free medium.

Conclusions: This study reveals one possible molecular mechanism how B-cell lymphomas can acquire resistance to DZNep, and proposes AHCY as a potential biomarker for investigation during the administration of EZH2-targeted therapy with DZNep.
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http://dx.doi.org/10.1186/s12885-020-06937-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7227222PMC
May 2020

SCelVis: exploratory single cell data analysis on the desktop and in the cloud.

PeerJ 2020 19;8:e8607. Epub 2020 Feb 19.

Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany.

Background: Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication.

Results: To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data.

Methods: SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.
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http://dx.doi.org/10.7717/peerj.8607DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035868PMC
February 2020

Support of a molecular tumour board by an evidence-based decision management system for precision oncology.

Eur J Cancer 2020 03 23;127:41-51. Epub 2020 Jan 23.

Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany; Department of Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany. Electronic address:

Background: Reliable and reproducible interpretation of molecular aberrations constitutes a bottleneck of precision medicine. Evidence-based decision management systems may improve rational therapy recommendations. To cope with an increasing amount of complex molecular data in the clinical care of patients with cancer, we established a workflow for the interpretation of molecular analyses.

Methods: A specialized physician screened results from molecular analyses for potential biomarkers, irrespective of the diagnostic modality. Best available evidence was retrieved and categorized through establishment of an in-house database and interrogation of publicly available databases. Annotated biomarkers were ranked using predefined evidence levels and subsequently discussed at a molecular tumour board (MTB), which generated treatment recommendations. Subsequent translation into patient treatment and clinical outcomes were followed up.

Results: One hundred patients were discussed in the MTB between January 2016 and May 2017. Molecular data were obtained for 70 of 100 patients (50 whole exome/RNA sequencing, 18 panel sequencing, 2 immunohistochemistry (IHC)/microsatellite instability analysis). The MTB generated a median of two treatment recommendations each for 63 patients. Thirty-nine patients were treated: 6 partial responses and 12 stable diseases were achieved as best responses. Genetic counselling for germline events was recommended for seven patients.

Conclusion: The development of an evidence-based workflow allowed for the clinical interpretation of complex molecular data and facilitated the translation of personalized treatment strategies into routine clinical care. The high number of treatment recommendations in patients with comprehensive genomic data and promising responses in patients treated with combination therapy warrant larger clinical studies.
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http://dx.doi.org/10.1016/j.ejca.2019.12.017DOI Listing
March 2020

Generation and characterization of a mitotane-resistant adrenocortical cell line.

Endocr Connect 2020 Feb;9(2):122-134

Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Nephrology and Medical Intensive Care, BCRT - Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany.

Mitotane is the only drug approved for the therapy of adrenocortical carcinoma (ACC). Its clinical use is limited by the occurrence of relapse during therapy. To investigate the underlying mechanisms in vitro, we here generated mitotane-resistant cell lines. After long-term pulsed treatment of HAC-15 human adrenocortical carcinoma cells with 70 µM mitotane, we isolated monoclonal cell populations of treated cells and controls and assessed their respective mitotane sensitivities by MTT assay. We performed exome sequencing and electron microscopy, conducted gene expression microarray analysis and determined intracellular lipid concentrations in the presence and absence of mitotane. Clonal cell lines established after pulsed treatment were resistant to mitotane (IC50 of 102.2 ± 7.3 µM (n = 12) vs 39.4 ± 6.2 µM (n = 6) in controls (biological replicates, mean ± s.d., P = 0.0001)). Unlike nonresistant clones, resistant clones maintained normal mitochondrial and nucleolar morphology during mitotane treatment. Resistant clones largely shared structural and single nucleotide variants, suggesting a common cell of origin. Resistance depended, in part, on extracellular lipoproteins and was associated with alterations in intracellular lipid homeostasis, including levels of free cholesterol, as well as decreased steroid production. By gene expression analysis, resistant cells showed profound alterations in pathways including steroid metabolism and transport, apoptosis, cell growth and Wnt signaling. These studies establish an in vitro model of mitotane resistance in ACC and point to underlying molecular mechanisms. They may enable future studies to overcome resistance in vitro and improve ACC treatment in vivo.
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http://dx.doi.org/10.1530/EC-19-0510DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6993260PMC
February 2020

Identification and ranking of recurrent neo-epitopes in cancer.

BMC Med Genomics 2019 11 27;12(1):171. Epub 2019 Nov 27.

Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.

Background: Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system's capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority.

Methods: We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions.

Results: We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year.

Conclusion: While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.
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http://dx.doi.org/10.1186/s12920-019-0611-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6882202PMC
November 2019

SigsPack, a package for cancer mutational signatures.

BMC Bioinformatics 2019 Sep 2;20(1):450. Epub 2019 Sep 2.

Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.

Background: Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology.

Results: We present here SigsPack, a Bioconductor package to estimate a sample's exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example.

Conclusion: SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization.
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http://dx.doi.org/10.1186/s12859-019-3043-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720940PMC
September 2019

Reflection of neuroblastoma intratumor heterogeneity in the new OHC-NB1 disease model.

Int J Cancer 2020 02 1;146(4):1031-1041. Epub 2019 Aug 1.

Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Accurate modeling of intratumor heterogeneity presents a bottleneck against drug testing. Flexibility in a preclinical platform is also desirable to support assessment of different endpoints. We established the model system, OHC-NB1, from a bone marrow metastasis from a patient diagnosed with MYCN-amplified neuroblastoma and performed whole-exome sequencing on the source metastasis and the different models and passages during model development (monolayer cell line, 3D spheroid culture and subcutaneous xenograft tumors propagated in mice). OHC-NB1 harbors a MYCN amplification in double minutes, 1p deletion, 17q gain and diploid karyotype, which persisted in all models. A total of 80-540 single-nucleotide variants (SNVs) was detected in each sample, and comparisons between the source metastasis and models identified 34 of 80 somatic SNVs to be propagated in the models. Clonal reconstruction using the combined copy number and SNV data revealed marked clonal heterogeneity in the originating metastasis, with four clones being reflected in the model systems. The set of OHC-NB1 models represents 43% of somatic SNVs and 23% of the cellularity in the originating metastasis with varying clonal compositions, indicating that heterogeneity is partially preserved in our model system.
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http://dx.doi.org/10.1002/ijc.32572DOI Listing
February 2020

HLA-MA: simple yet powerful matching of samples using HLA typing results.

Bioinformatics 2017 Jul;33(14):2241-2242

Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany.

Summary: We propose the simple method HLA-MA for consistency checking in pipelines operating on human HTS data. The method is based on the HLA typing result of the state-of-the-art method OptiType. Provided that there is sufficient coverage of the HLA loci, comparing HLA types allows for simple, fast and robust matching of samples from whole genome, exome and RNA-seq data. Our approach uses information from small but genetically highly variable regions and thus complements approaches that rely on genome or exon-wide variant profiles.

Availability And Implementation: The software is implemented In Python 3 and freely available under the MIT license at https://github.com/bihealth/hlama and via Bioconda.

Contact: dieter.beule@bihealth.de.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btx132DOI Listing
July 2017