Berend Snijder

Last Name

Snijder

First Name

Berend

ORCID

0000-0003-3386-6583

Organisational unit

02287 - Personalized Health and Related Technol / Personalized Health and Related Technol

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Publications 1 - 10 of 39

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support
Irmisch, Anja; Bonilla, Ximena; Lehmann, Kjong-Van; et al. (2021)
Cancer Cell
The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.
The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support
Irmisch, Anja; Bonilla, Ximena; Chevrier, Stéphane; et al. (2020)
medRxiv
Recent technological advances allow profiling of tumor samples to an unparalleled level with respect to molecular and spatial composition as well as treatment response. We describe a prospective, observational clinical study performed within the Tumor Profiler (TuPro) Consortium that aims to show the extent to which such comprehensive information leads to advanced mechanistic insights of a patient’s tumor, enables prognostic and predictive biomarker discovery, and has the potential to support clinical decision making. For this study of melanoma, ovarian carcinoma, and acute myeloid leukemia tumors, in addition to the emerging standard diagnostic approaches of targeted NGS panel sequencing and digital pathology, we perform extensive characterization using the following exploratory technologies: single-cell genomics and transcriptomics, proteotyping, CyTOF, imaging CyTOF, pharmacoscopy, and 4i drug response profiling (4i DRP). In this work, we outline the aims of the TuPro study and present preliminary results on the feasibility of using these technologies in clinical practice showcasing the power of an integrative multi-modal and functional approach for understanding a tumor’s underlying biology and for clinical decision support.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialBASEC-Nr.2018-02050Funding StatementThe study described in this paper is the result of a jointly-funded effort between several academic institutions (The University of Zurich, The University of Zurich Hospital, The Swiss Federal Institute of Technology in Zurich, The University of Basel Hospital, and The University of Basel), as well as F. Hoffmann-La Roche AG.Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe manuscript details a prospetive outlook for a study that is currently underway. However, the data will be made available upon study completion and publication.
Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia
Wegmann, Rebekka; Bonilla Bustillo, Ximena; Casanova, Ruben; et al. (2024)
Nature Communications
Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like targeting increased proliferation with the PLK inhibitor volasertib. Additionally, high CD36 expression in VEN-resistant blasts associates with sensitivity to CD36-targeted antibody treatment ex vivo. This study demonstrates how single-cell multi-omic profiling can uncover drug resistance mechanisms and treatment vulnerabilities, providing a valuable resource for future AML research.
Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer
Dondi, Arthur; Lischetti, Ulrike; Jacob, Francis; et al. (2023)
Nature Communications
Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.
SCIM: Universal Single-Cell Matching with Unpaired Feature Sets
Stark, Stefan; Ficek, Joanna; Locatello, Francesco; et al. (2020)
bioRxiv
Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/scim
Large Scale RNAi Reveals the Requirement of Nuclear Envelope Breakdown for Nuclear Import of Human Papillomaviruses
Aydin, Inci; Weber, Susanne; Snijder, Berend; et al. (2014)
PLoS Pathogens
A two-step, high-throughput RNAi silencing screen was used to identify host cell factors required during human papillomavirus type 16 (HPV16) infection. Analysis of validated hits implicated a cluster of mitotic genes and revealed a previously undetermined mechanism for import of the viral DNA (vDNA) into the nucleus. In interphase cells, viruses were endocytosed, routed to the perinuclear area, and uncoated, but the vDNA failed to be imported into the nucleus. Upon nuclear envelope perforation in interphase cells HPV16 infection occured. During mitosis, the vDNA and L2 associated with host cell chromatin on the metaphase plate. Hence, we propose that HPV16 requires nuclear envelope breakdown during mitosis for access of the vDNA to the nucleoplasm. The results accentuate the value of genes found by RNAi screens for investigation of viral infections. The list of cell functions required during HPV16 infection will, moreover, provide a resource for future virus-host cell interaction studies.
Simultaneous analysis of large-scale RNAi screens for pathogen entry
Rämö, Pauli; Drewek, Anna; Arrieumerlou, Cécile; et al. (2014)
BMC Genomics
Background Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries. Results We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied. Conclusions Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.
RNAi Screening Reveals Proteasome- and Cullin3-Dependent Stages in Vaccinia Virus Infection
Mercer, Jason; Snijder, Berend; Sacher, Raphael; et al. (2012)
Cell Reports
A two-step, automated, high-throughput RNAi silencing screen was used to identify host cell factors required during vaccinia virus infection. Validation and analysis of clustered hits revealed previously unknown processes during virus entry, including a mechanism for genome uncoating. Viral core proteins were found to be already ubiquitinated during virus assembly. After entering the cytosol of an uninfected cell, the viral DNA was released from the core through the activity of the cell’s proteasomes. Next, a Cullin3-based ubiquitin ligase mediated a further round of ubiquitination and proteasome action. This was needed in order to initiate viral DNA replication. The results accentuate the value of large-scale RNAi screens in providing directions for detailed cell biological investigation of complex pathways. The list of cell functions required during poxvirus infection will, moreover, provide a resource for future virus-host cell interaction studies and for the discovery of antivirals.
Identification of intracellular signalling cascades mediating Salmonella invasion into epitheliall cells
Misselwitz, Benjamin; Dilling, Sabrina; Sacher, Raphael; et al. (2007)
International Journal of Medical Microbiology
Enhancing anti-EGFRvIII CAR T cell therapy against glioblastoma with a paracrine SIRPγ-derived CD47 blocker
Martins, Tomás A.; Kaymak, Deniz; Tatari, Nazanin; et al. (2024)
Nature Communications
A significant challenge for chimeric antigen receptor (CAR) T cell therapy against glioblastoma (GBM) is its immunosuppressive microenvironment, which is densely populated by protumoral glioma-associated microglia and macrophages (GAMs). Myeloid immune checkpoint therapy targeting the CD47-signal regulatory protein alpha (SIRP alpha) axis induces GAM phagocytic function, but CD47 blockade monotherapy is associated with toxicity and low bioavailability in solid tumors. In this work, we engineer a CAR T cell against epidermal growth factor receptor variant III (EGFRvIII), constitutively secreting a signal regulatory protein gamma-related protein (SGRP) with high affinity to CD47. Anti-EGFRvIII-SGRP CAR T cells eradicate orthotopic EGFRvIII-mosaic GBM in vivo, promoting GAM-mediated tumor cell phagocytosis. In a subcutaneous CD19+ lymphoma mouse model, anti-CD19-SGRP CAR T cell therapy is superior to conventional anti-CD19 CAR T. Thus, combination of CAR and SGRP eliminates bystander tumor cells in a manner that could overcome main mechanisms of CAR T cell therapy resistance, including immune suppression and antigen escape.

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Berend Snijder

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