Marion Subklewe

816 total citations
36 papers, 276 citations indexed

About

Marion Subklewe is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Marion Subklewe has authored 36 papers receiving a total of 276 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Oncology, 15 papers in Molecular Biology and 12 papers in Immunology. Recurrent topics in Marion Subklewe's work include CAR-T cell therapy research (23 papers), Acute Myeloid Leukemia Research (10 papers) and Immunotherapy and Immune Responses (6 papers). Marion Subklewe is often cited by papers focused on CAR-T cell therapy research (23 papers), Acute Myeloid Leukemia Research (10 papers) and Immunotherapy and Immune Responses (6 papers). Marion Subklewe collaborates with scholars based in Germany, United States and France. Marion Subklewe's co-authors include Anthony S. Stein, Sophia K. Khaldoyanidi, Dirk Nagorsen, G. J. Ossenkoppele, Hagop M. Kantarjian, Farhad Ravandi, Gert J. Ossenkoppele, Mojca Jongen‐Lavrencic, Roland B. Walter and Antreas Hindoyan and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Cancer Cell.

In The Last Decade

Marion Subklewe

32 papers receiving 276 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marion Subklewe Germany 6 180 114 95 86 36 36 276
Sanbin Wang China 9 137 0.8× 102 0.9× 52 0.5× 74 0.9× 27 0.8× 31 259
Daniel Egan United States 8 123 0.7× 127 1.1× 49 0.5× 79 0.9× 28 0.8× 22 212
Kathleen Ruehle United States 8 212 1.2× 121 1.1× 62 0.7× 78 0.9× 30 0.8× 24 291
Haven R. Garber United States 9 162 0.9× 45 0.4× 103 1.1× 82 1.0× 42 1.2× 20 245
Torben Altmann Germany 5 190 1.1× 78 0.7× 131 1.4× 56 0.7× 17 0.5× 8 253
Samuel John United States 7 159 0.9× 75 0.7× 231 2.4× 99 1.2× 38 1.1× 22 379
Jiazhen Cui China 11 312 1.7× 74 0.6× 118 1.2× 139 1.6× 76 2.1× 39 423
Sibgha Gull Chaudhary United States 8 162 0.9× 100 0.9× 81 0.9× 70 0.8× 15 0.4× 38 302
Tiina Hannunen Finland 4 139 0.8× 50 0.4× 125 1.3× 100 1.2× 46 1.3× 4 285
Anna Truppel-Hartmann United States 10 223 1.2× 130 1.1× 65 0.7× 141 1.6× 26 0.7× 22 289

Countries citing papers authored by Marion Subklewe

Since Specialization
Citations

This map shows the geographic impact of Marion Subklewe'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 Marion Subklewe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marion Subklewe more than expected).

Fields of papers citing papers by Marion Subklewe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marion Subklewe. 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 Marion Subklewe. The network helps show where Marion Subklewe may publish in the future.

Co-authorship network of co-authors of Marion Subklewe

This figure shows the co-authorship network connecting the top 25 collaborators of Marion Subklewe. A scholar is included among the top collaborators of Marion Subklewe 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 Marion Subklewe. Marion Subklewe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Stock, Sophia, Adrian Gottschlich, Janina Dörr, et al.. (2024). Comparative performance of scFv-based anti-BCMA CAR formats for improved T cell therapy in multiple myeloma. Cancer Immunology Immunotherapy. 73(6). 100–100. 2 indexed citations
3.
Xu, Tao, Alexander Beck, Katharina Müller, et al.. (2024). PD-1 blockade does not improve efficacy of EpCAM-directed CAR T-cell in lung cancer brain metastasis. Cancer Immunology Immunotherapy. 73(12). 255–255. 1 indexed citations
4.
5.
Gottschlich, Adrian, Johannes Sam, Lisa Rohrbacher, et al.. (2024). CSF1R Targeting T Cell Engaging Bispecific Antibodies Enable Safe and Efficient Immunotherapies in Acute Myeloid Leukemia. Blood. 144(Supplement 1). 918–918.
6.
Heitmann, Jonas S., Juliane S. Walz, Helmut R. Salih, et al.. (2024). A Phase 1, Open-Label, Dose Escalation and Dose Expansion Study of Cln-049 for the Treatment of Acute Myeloid Leukemia Patients with Measurable Residual Disease. Blood. 144(Supplement 1). 2883.1–2883.1. 1 indexed citations
7.
Ravandi, Farhad, Marion Subklewe, Roland B. Walter, et al.. (2024). Safety and tolerability of AMG 330 in adults with relapsed/refractory AML: a phase 1a dose-escalation study. Leukemia & lymphoma. 65(9). 1281–1291. 12 indexed citations
8.
Rohrbacher, Lisa, Daniel Nixdorf, Bettina Brauchle, et al.. (2023). Two Players, One Goal: BiTE ® Vs CART Targeting FLT3 in AML. Blood. 142(Supplement 1). 3444–3444. 1 indexed citations
9.
Winkelmann, Michael, Viktoria Blumenberg, Kai Rejeski, et al.. (2023). Predictive value of pre-infusion tumor growth rate for the occurrence and severity of CRS and ICANS in lymphoma under CAR T-cell therapy. Annals of Hematology. 103(1). 259–268. 4 indexed citations
10.
Haubner, Sascha, Jorge Mansilla‐Soto, Sarah Nataraj, et al.. (2023). Cooperative CAR targeting to selectively eliminate AML and minimize escape. Cancer Cell. 41(11). 1871–1891.e6. 58 indexed citations
11.
Khaldoyanidi, Sophia K., Antreas Hindoyan, Anthony S. Stein, & Marion Subklewe. (2022). Leukemic stem cells as a target for eliminating acute myeloid leukemia: Gaps in translational research. Critical Reviews in Oncology/Hematology. 175. 103710–103710. 23 indexed citations
12.
Rohrbacher, Lisa, Daniel Nixdorf, Bettina Brauchle, et al.. (2022). The Race Is on: BiTE Vs CART As FLT3-Directed Immunotherapies in AML. Blood. 140(Supplement 1). 10233–10234. 1 indexed citations
13.
Rejeski, Kai, David M. Cordas dos Santos, Lian Liu, et al.. (2022). Body Composition and Immunonutritional Status Impact Survival Outcomes after CD19 CAR T-Cell Therapy. Blood. 140(Supplement 1). 10405–10406. 2 indexed citations
14.
Hänel, Gerulf, Vesna Pulko, Christina Claus, et al.. (2021). Augmenting Efficacy of T-Cell Bispecific Antibodies in AML through a Tumor Stroma-Targeted 4-1BB Agonist. Blood. 138(Supplement 1). 1178–1178. 1 indexed citations
15.
Zito, Laura, Anetta Marcinek, Bettina Brauchle, et al.. (2019). Exploiting an Anti-CD3/CD33 Bispecific Antibody to Redirect Donor T Cells Against HLA Loss Leukemia Relapses. Blood. 134(Supplement_1). 513–513. 3 indexed citations
16.
Bücklein, Veit, et al.. (2019). Predictors of Efficacy for Blinatumomab in BCP-ALL Patients: Non-Responders Show Impaired CD19-BiTE®-Mediated Cytotoxicity in Vitro. Blood. 134(Supplement_1). 2632–2632. 2 indexed citations
17.
Subklewe, Marion. (2018). Current status of immunotherapy in acute myeloid leukemia. HemaSphere. 2(S2). 15–18. 1 indexed citations
18.
Krupka, Christina, Thomas Köhnke, Felix S. Lichtenegger, et al.. (2016). Steroids Abrogate BiTE® Antibody Construct-Mediated Cytotoxicity in Primary AML Cells. Blood. 128(22). 3940–3940. 1 indexed citations
19.
Köhnke, Thomas, Veit Bücklein, Kristina R. Pfannes, et al.. (2015). Improved Detection of Minimal Residual Disease By Flow Cytometry in AML By Combining Manual Gating and Visne Clustering. Blood. 126(23). 2593–2593. 1 indexed citations
20.
Henschler, Reinhard, et al.. (2015). ITOC2 – 022. Vaccination with next-generation dendritic cells for AML postremission therapy induces antigen-specific T cell responses. European Journal of Cancer. 51. S8–S8. 2 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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