Michael Hudecek

13.0k total citations · 4 hit papers
112 papers, 6.4k citations indexed

About

Michael Hudecek is a scholar working on Oncology, Immunology and Molecular Biology. According to data from OpenAlex, Michael Hudecek has authored 112 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 102 papers in Oncology, 50 papers in Immunology and 43 papers in Molecular Biology. Recurrent topics in Michael Hudecek's work include CAR-T cell therapy research (96 papers), Virus-based gene therapy research (30 papers) and Immune Cell Function and Interaction (23 papers). Michael Hudecek is often cited by papers focused on CAR-T cell therapy research (96 papers), Virus-based gene therapy research (30 papers) and Immune Cell Function and Interaction (23 papers). Michael Hudecek collaborates with scholars based in Germany, United States and Spain. Michael Hudecek's co-authors include Stanley R. Riddell, Daniel Sommermeyer, Hermann Einsele, Paula L. Kosasih, Cameron J. Turtle, Michael C. Jensen, David G. Maloney, Christoph Rader, Tea Gogishvili and Thomas Nerreter and has published in prestigious journals such as Nature, Angewandte Chemie International Edition and Journal of Clinical Investigation.

In The Last Decade

Michael Hudecek

104 papers receiving 6.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Immunotherapy of non-Hodgkin’s lymphoma with a defined ratio of CD8 ⁺ and CD4 ⁺ CD19-specific chimeric antigen receptor–modified T cells

2016 741 citations Cameron J. Turtle, Carolina Berger et al. profile →
Chimeric antigen receptor-modified T cells derived from defined CD8+ and CD4+ subsets confer superior antitumor reactivity in vivo

2015 639 citations Daniel Sommermeyer, Michael Hudecek et al. Leukemia profile →
The Nonsignaling Extracellular Spacer Domain of Chimeric Antigen Receptors Is Decisive for In Vivo Antitumor Activity

2014 406 citations Michael Hudecek, Daniel Sommermeyer et al. Cancer Immunology Research profile →
The tyrosine kinase inhibitor dasatinib acts as a pharmacologic on/off switch for CAR T cells

2019 379 citations Katrin Mestermann, Justus Weber et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Hudecek Germany	39	5.4k	2.4k	2.0k	1.9k	1.6k	112	6.4k
Zhaohui Zheng United States	17	5.6k 1.0×	1.8k 0.7×	2.1k 1.0×	1.5k 0.8×	1.7k 1.0×	32	6.5k
Vanessa Gonzalez United States	8	5.0k 0.9×	1.5k 0.6×	1.8k 0.9×	1.4k 0.7×	1.5k 0.9×	20	5.6k
Enli Liu United States	19	4.5k 0.8×	1.8k 0.8×	2.1k 1.0×	1.3k 0.7×	1.7k 1.0×	31	5.6k
Anne Chew United States	19	8.4k 1.5×	2.5k 1.1×	3.3k 1.6×	2.3k 1.2×	2.6k 1.6×	34	9.6k
Carlos A. Ramos United States	35	4.3k 0.8×	2.1k 0.9×	2.0k 1.0×	1.0k 0.6×	1.5k 0.9×	107	6.2k
Mohamad Hamieh United States	14	3.8k 0.7×	1.7k 0.7×	1.5k 0.7×	1.3k 0.7×	1.2k 0.8×	26	4.6k
Marco Ruella United States	29	3.2k 0.6×	1.3k 0.5×	1.3k 0.6×	809 0.4×	1.1k 0.7×	124	4.0k
Daniel J. Powell United States	49	6.8k 1.3×	2.0k 0.8×	5.3k 2.6×	1.4k 0.8×	1.5k 0.9×	118	8.8k
Joseph A. Fraietta United States	25	3.6k 0.7×	1.7k 0.7×	2.0k 1.0×	1.0k 0.6×	924 0.6×	80	4.9k
Bambi Grilley United States	21	3.7k 0.7×	1.2k 0.5×	1.5k 0.7×	920 0.5×	1.5k 0.9×	69	4.4k

Countries citing papers authored by Michael Hudecek

Since Specialization

Citations

This map shows the geographic impact of Michael Hudecek's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Hudecek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Hudecek more than expected).

Fields of papers citing papers by Michael Hudecek

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Hudecek. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Hudecek. The network helps show where Michael Hudecek may publish in the future.

Co-authorship network of co-authors of Michael Hudecek

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Hudecek. A scholar is included among the top collaborators of Michael Hudecek based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Hudecek. Michael Hudecek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Nietzer, Sarah, Lorenz Lechner, Lisa Bauer, et al.. (2025). A fully human In-vitro model for bone metastasis: potential of an electrospun scaffold as alternative to biological materials for osteoblast/osteoclast differentiation. Materialia. 42. 102440–102440.

Ho, Patrick, Céline Grégoire, AnneMarie W. Block, et al.. (2025). Fine tuning towards the next generation of engineered T cells. Nature Biomedical Engineering. 9(10). 1610–1631.

Petri, Karl, Elvira D’Ippolito, Annette Künkele, et al.. (2025). Next-generation T cell immunotherapies engineered with CRISPR base and prime editing: challenges and opportunities. Nature Reviews Clinical Oncology. 22(12). 902–923.

Peters, Timo, Benjamin Salzer, Janett Göhring, et al.. (2024). TCR/CD3-based synthetic antigen receptors (TCC) convey superior antigen sensitivity combined with high fidelity of activation. Science Advances. 10(36). eadj4632–eadj4632. 1 indexed citations

Sanges, Carmen, et al.. (2024). Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors. 11. e47031–e47031. 40 indexed citations

Müller, R, Daria Briukhovetska, Justus Weber, et al.. (2024). The Spectrum of CAR Cellular Effectors: Modes of Action in Anti-Tumor Immunity. Cancers. 16(14). 2608–2608. 6 indexed citations

Egri, Péter, Carmen Sanges, Sophia Danhof, et al.. (2024). Reinforcement Learning Based Resource Management for CAR T-Cell Therapies. Procedia CIRP. 125. 154–159. 1 indexed citations

Friedrich, Maik, Maximilian Merz, Vladan Vučinić, et al.. (2023). Teclistamab impairs detection of BCMA CAR-T cells. Blood Advances. 7(15). 3842–3845. 4 indexed citations

Bexte, Tobias, Lacramioara Botezatu, Csaba Miskey, et al.. (2021). Non-Viral Sleeping Beauty Transposon Engineered CD19-CAR-NK Cells Show a Safe Genomic Integration Profile and High Antileukemic Efficiency. Blood. 138(Supplement 1). 2797–2797. 11 indexed citations

10.

Frenz, Silke, María‐Belén Vídriales, Marcos González, et al.. (2021). Siglec-6 is a novel target for CAR T-cell therapy in acute myeloid leukemia. Blood. 138(19). 1830–1842. 74 indexed citations

11.

Chabannon, Christian, Ulrike Koehl, Francesco Lanza, et al.. (2019). Development of adaptive immune effector therapies in solid tumors. Annals of Oncology. 30(11). 1740–1750. 38 indexed citations

12.

Querques, Irma, Andreas Mades, Cecilia Zuliani, et al.. (2019). A highly soluble Sleeping Beauty transposase improves control of gene insertion. Nature Biotechnology. 37(12). 1502–1512. 65 indexed citations

13.

Paszkiewicz, Paulina J., Simon P. Fräßle, Shivani Srivastava, et al.. (2016). Targeted antibody-mediated depletion of murine CD19 CAR T cells permanently reverses B cell aplasia. Journal of Clinical Investigation. 126(11). 4262–4272. 222 indexed citations

14.

Sommermeyer, Daniel, Michael Hudecek, Paula L. Kosasih, et al.. (2015). Chimeric antigen receptor-modified T cells derived from defined CD8+ and CD4+ subsets confer superior antitumor reactivity in vivo. Leukemia. 30(2). 492–500. 639 indexed citations breakdown →

15.

Berger, Carolina, Daniel Sommermeyer, Michael Hudecek, et al.. (2014). Safety of Targeting ROR1 in Primates with Chimeric Antigen Receptor–Modified T Cells. Cancer Immunology Research. 3(2). 206–216. 139 indexed citations

16.

Hudecek, Michael, Daniel Sommermeyer, Paula L. Kosasih, et al.. (2014). The Nonsignaling Extracellular Spacer Domain of Chimeric Antigen Receptors Is Decisive for In Vivo Antitumor Activity. Cancer Immunology Research. 3(2). 125–135. 406 indexed citations breakdown →

17.

Hudecek, Michael, Maria Teresa Lupo Stanghellini, Paula L. Kosasih, et al.. (2013). Receptor Affinity and Extracellular Domain Modifications Affect Tumor Recognition by ROR1-Specific Chimeric Antigen Receptor T Cells. Clinical Cancer Research. 19(12). 3153–3164. 424 indexed citations

18.

Bleakley, Marie, Brith Otterud, Michael Hudecek, et al.. (2010). Leukemia-associated minor histocompatibility antigen discovery using T-cell clones isolated by in vitro stimulation of naive CD8+ T cells. Blood. 115(23). 4923–4933. 79 indexed citations

19.

Brown, Christine E., Renate Starr, Catalina Martínez, et al.. (2009). Recognition and Killing of Brain Tumor Stem-Like Initiating Cells by CD8+ Cytolytic T Cells. Cancer Research. 69(23). 8886–8893. 113 indexed citations

20.

Nishida, Tetsuya, Michael Hudecek, Ana Kostić, et al.. (2009). Development of Tumor-Reactive T Cells After Nonmyeloablative Allogeneic Hematopoietic Stem Cell Transplant for Chronic Lymphocytic Leukemia. Clinical Cancer Research. 15(14). 4759–4768. 31 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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