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  1. Book ; Thesis: Investigating mechanisms of deregulated EVI1 expression AML and solid tumors without 3q26/EVI1 rearrangements

    Kruse, Sabrina / Brors, Benedikt

    2023  

    Institution Universität Heidelberg
    Author's details presented by M.Sc. Sabrina Kruse ; referees: Prof. Dr. Benedikt Brors, Ph.D. Ana Oliveira
    Language English
    Size XVI, 146 Seiten, Illustrationen, Diagramme
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Ruprechts-Karls-University Heidelberg, 2023
    Note Text Englisch, Zusammenfassung in englischer und deutscher Sprache
    HBZ-ID HT030722436
    Database Catalogue ZB MED Medicine, Health

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    2024 E 571 order for loan Magazin

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  2. Book ; Thesis: Dissection of the tumor-reactive and bystander t-cell repertoires at the single-cell level in an orthotopic mouse model for pancreatic cancer

    Kehm, Hannes Jakob / Offringa, Rienk / Brors, Benedikt

    2023  

    Institution Universität Heidelberg
    Author's details presented by Hannes Jakob Kehm, M.Sc. ; referees: Prof. Dr. Rienk Offringa, Prof. Dr. Benedikt Brors
    Language English
    Size XVI, 145 Seiten, Illustrationen, Diagramme
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Ruprechts-Karls-University Heidelberg, 2023
    Note Text Englisch, Zusammenfassung in englischer und deutscher Sprache
    HBZ-ID HT030638085
    Database Catalogue ZB MED Medicine, Health

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    2024 E 284 order for loan Magazin

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  3. Book ; Thesis: Genomic analyses of mutational mechanisms and tumor heterogeneity in B-cell lymphomas

    Seufert, Julian / Brors, Benedikt

    2021  

    Institution Universität Heidelberg
    Author's details presented by Julian Benjamin Seufert, M. Sc. ; referees: Prof. Dr. Benedikt Brors, apl. Prof. Dr. Stefan Wiemann
    Language English
    Size xxi, 164 Seiten, Illustrationen, Diagramme
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Ruperto Carola University Heidelberg, 2021
    Note Zusammenfassung in englischer und deutscher Sprache ; Bibliography: Seite 143-164
    HBZ-ID HT021377988
    Database Catalogue ZB MED Medicine, Health

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    2022 E 838 order for loan Magazin

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  4. Book ; Thesis: The molecular landscape of tumor evolution unter therapy in Glioblastoma multiforme

    Knoll, Maximilian / Brors, Benedikt

    2021  

    Institution Universität Heidelberg
    Author's details presented by Maximilian Knoll ; referees: Prof. Dr. Benedikt Brors, Dr. Dr. Amir Abdollahi
    Language English
    Size 309 Seiten, Illustrationen, Diagramme
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Ruperto Carola University Heidelberg, 2021
    Note Text Englisch, Zusammenfassung in deutscher und englischer Sprache
    HBZ-ID HT021067321
    Database Catalogue ZB MED Medicine, Health

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    2021 E 1182 order for loan Magazin

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  5. Article ; Online: Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell-intrinsic Stemness or Mesenchymality Characteristics.

    Kreis, Julian / Aybey, Bogac / Geist, Felix / Brors, Benedikt / Staub, Eike

    Cancer research communications

    2024  Volume 4, Issue 2, Page(s) 516–529

    Abstract: Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or ... ...

    Abstract Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low cancer cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymal/EMT characteristics.
    Significance: Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.
    MeSH term(s) Humans ; Transcriptome/genetics ; Neoplasms/genetics ; Epithelial-Mesenchymal Transition/genetics ; Stromal Cells/pathology ; Tumor Microenvironment/genetics
    Language English
    Publishing date 2024-02-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2767-9764
    ISSN (online) 2767-9764
    DOI 10.1158/2767-9764.CRC-23-0383
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets.

    Aybey, Bogac / Zhao, Sheng / Brors, Benedikt / Staub, Eike

    Frontiers in immunology

    2023  Volume 14, Page(s) 1194745

    Abstract: Background: Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of ...

    Abstract Background: Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches.
    Results: We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment.
    Discussion and conclusion: We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.
    MeSH term(s) Random Forest ; Algorithms ; Benchmarking ; Machine Learning ; Gene Expression
    Language English
    Publishing date 2023-08-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606827-8
    ISSN 1664-3224 ; 1664-3224
    ISSN (online) 1664-3224
    ISSN 1664-3224
    DOI 10.3389/fimmu.2023.1194745
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  7. Book ; Online ; Thesis: Next-Generation Sequencing Analysis of Cell-Free DNA Identifies Actionable Alterations and Genomic Features in Pediatric Cancers

    Puranachot, Pitithat [Verfasser] / Brors, Benedikt [Akademischer Betreuer]

    2024  

    Author's details Pitithat Puranachot ; Betreuer: Benedikt Brors
    Keywords Naturwissenschaften ; Science
    Subject code sg500
    Language English
    Publisher Universitätsbibliothek Heidelberg
    Publishing place Heidelberg
    Document type Book ; Online ; Thesis
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  8. Book ; Online ; Thesis: Characterization of tumor subpopulations in glioblastoma with single cell transcriptomics

    Hai, Ling [Verfasser] / Brors, Benedikt [Akademischer Betreuer]

    2024  

    Author's details Ling Hai ; Betreuer: Benedikt Brors
    Keywords Naturwissenschaften ; Science
    Subject code sg500
    Language English
    Publisher Universitätsbibliothek Heidelberg
    Publishing place Heidelberg
    Document type Book ; Online ; Thesis
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  9. Book ; Online ; Thesis: ITCC-P4: Molecular characterization and multi-omics analysis of pediatric patient tumor and Patient-Derived Xenograft (PDX) models for preclinical model selection

    Gopisetty, Apurva [Verfasser] / Brors, Benedikt [Akademischer Betreuer]

    2024  

    Author's details Apurva Gopisetty ; Betreuer: Benedikt Brors
    Keywords Naturwissenschaften ; Science
    Subject code sg500
    Language English
    Publisher Universitätsbibliothek Heidelberg
    Publishing place Heidelberg
    Document type Book ; Online ; Thesis
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  10. Article: Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts.

    Münch, Julia M / Sobol, Morgan S / Brors, Benedikt / Kaster, Anne-Kristin

    Advances in applied microbiology

    2023  Volume 123, Page(s) 1–39

    Abstract: Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in ... ...

    Abstract Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.
    MeSH term(s) Transcriptome ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Data Analysis ; RNA, Messenger
    Chemical Substances RNA, Messenger
    Language English
    Publishing date 2023-05-18
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 160-0
    ISSN 0065-2164
    ISSN 0065-2164
    DOI 10.1016/bs.aambs.2023.04.002
    Database MEDical Literature Analysis and Retrieval System OnLINE

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