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Magnus von Knebel Doeberitz

Hospital Sírio-Libanês · DE
Area of research
Pathology and Forensic Medicine · Epidemiology
Research interest
Research interests include Genetic factors in colorectal cancer, Cervical Cancer and HPV Research, Cancer Genomics and Diagnostics, and Colorectal Cancer Screening and Detection.
h-index
86
citations
25,993
works
537
NIH funding
primary concept
Medicine
email

Recent publications

Lower Degree of Microsatellite Instability in Colorectal Carcinomas From MSH6-Associated Lynch Syndrome Patients
Modern Pathology 2025cited by 10position: middledoi
Lower degree of microsatellite instability in colorectal carcinomas from <i>MSH6</i> -associated Lynch syndrome patients
bioRxiv (Cold Spring Harbor Laboratory) 2024cited by 2position: middledoi
Obituary: Nobel Laureate Harald zur Hausen (1936–2023) - Who dedicated his life to unraveling the role of infectious agents in the pathogenesis of human cancers
European Journal of Cancer 2024cited by 1position: lastdoi
Mortality by age, gene and gender in carriers of pathogenic mismatch repair gene variants receiving surveillance for early cancer diagnosis and treatment: a report from the prospective Lynch syndrome database
EClinicalMedicine 2023cited by 103position: middledoi
Lynch syndrome cancer vaccines: A roadmap for the development of precision immunoprevention strategies
Frontiers in Oncology 2023cited by 30position: middledoi
A “Two-in-One Hit” Model of Shortcut Carcinogenesis in MLH1 Lynch Syndrome Carriers
Gastroenterology 2023cited by 28position: middledoi
Senescent Tumor Cells Are Frequently Present at the Invasion Front: Implications for Improving Disease Control in Patients with Locally Advanced Prostate Cancer
Pathobiology 2023cited by 10position: middledoi
Senescent Tumor Cells Are Frequently Present at the Invasion Front: Implications for Improving Disease Control in Patients with Locally Advanced Prostate Cancer
Pathobiology 2023cited by 10position: contributordoi
Is HLA type a possible cancer risk modifier in Lynch syndrome?
2023cited by 8position: contributordoi
Predictors for Survival of Patients with Squamous Cell Carcinoma of Unknown Primary in the Head and Neck Region
Cancers 2023cited by 6position: middledoi
Table S5 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Table S5 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Supplementary Data 2 from A Frameshift Peptide Neoantigen-Based Vaccine for Mismatch Repair-Deficient Cancers: A Phase I/IIa Clinical Trial
2023cited by 0position: contributordoi
Data from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Table S2 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Table S4 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Supplementary Data 1 from A Frameshift Peptide Neoantigen-Based Vaccine for Mismatch Repair-Deficient Cancers: A Phase I/IIa Clinical Trial
2023cited by 0position: contributordoi
Table S1 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Supplementary Data 2 from A Frameshift Peptide Neoantigen-Based Vaccine for Mismatch Repair-Deficient Cancers: A Phase I/IIa Clinical Trial
2023cited by 0position: contributordoi
Data from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Table S3 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Supplementary Figures from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Supplementary Figures from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Table S2 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Supplementary Data 3 from A Frameshift Peptide Neoantigen-Based Vaccine for Mismatch Repair-Deficient Cancers: A Phase I/IIa Clinical Trial
2023cited by 0position: contributordoi
Supplementary Data 3 from A Frameshift Peptide Neoantigen-Based Vaccine for Mismatch Repair-Deficient Cancers: A Phase I/IIa Clinical Trial
2023cited by 0position: contributordoi
Supplementary Data 1 from A Frameshift Peptide Neoantigen-Based Vaccine for Mismatch Repair-Deficient Cancers: A Phase I/IIa Clinical Trial
2023cited by 0position: contributordoi
Data from A Frameshift Peptide Neoantigen-Based Vaccine for Mismatch Repair-Deficient Cancers: A Phase I/IIa Clinical Trial
2023cited by 0position: contributordoi
Table S1 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi
Table S4 from Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&amp;E Stains
2023cited by 0position: contributordoi

Grants

No grants ingested yet.

Frequent collaborators

Matthias Kloor · European Molecular Biology Organization43 papers (2012–2025) · 37 papers (2019–2023)Miriam Reuschenbach · MSD (Germany)28 papers (2012–2021)Jens Peter Klußmann · Düsseldorf University Hospital27 papers (2014–2023)Elena‐Sophie Prigge · German Cancer Research Center26 papers (2013–2023)Aysel Ahadova · DKFZ-ZMBH Alliance20 papers (2013–2025) · 17 papers (2020–2023) · 17 papers (2020–2023) · 17 papers (2020–2023)Hans Christian Reinhardt · Düsseldorf University Hospital16 papers (2020–2023) · 15 papers (2021–2023)Christine Langer · Justus-Liebig-Universität Gießen15 papers (2021–2023)Hendrik Bläker · University Medical Center13 papers (2012–2023)Steffen Wagner · Justus-Liebig-Universität Gießen13 papers (2014–2023)Claus Wittekindt · Witten/Herdecke University11 papers (2014–2023)Johannes Gebert · Medical University of Vienna9 papers (2015–2023)Axel Benner · Cancer Research And Biostatistics8 papers (2012–2021)Alexej Ballhausen · Humboldt-Universität zu Berlin8 papers (2018–2022)Lena Bohaumilitzky · Heidelberg University8 papers (2020–2025)Toni T. Seppälä · Johns Hopkins Hospital7 papers (2019–2023)