Deepu Madduri

12.3k total citations
77 papers, 1.6k citations indexed

About

Deepu Madduri is a scholar working on Hematology, Oncology and Molecular Biology. According to data from OpenAlex, Deepu Madduri has authored 77 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Hematology, 49 papers in Oncology and 30 papers in Molecular Biology. Recurrent topics in Deepu Madduri's work include Multiple Myeloma Research and Treatments (52 papers), CAR-T cell therapy research (38 papers) and Protein Degradation and Inhibitors (24 papers). Deepu Madduri is often cited by papers focused on Multiple Myeloma Research and Treatments (52 papers), CAR-T cell therapy research (38 papers) and Protein Degradation and Inhibitors (24 papers). Deepu Madduri collaborates with scholars based in United States, Belgium and Germany. Deepu Madduri's co-authors include Sundar Jagannath, Samir Parekh, Ajai Chari, Joshua Richter, Hearn Jay Cho, Shambavi Richard, Jesús G. Berdeja, Saad Z. Usmani, Alessandro Laganà and Enrique Zudaire and has published in prestigious journals such as Circulation, Nature Medicine and Journal of Clinical Oncology.

In The Last Decade

Deepu Madduri

76 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Deepu Madduri United States 23 1.2k 867 748 313 250 77 1.6k
Joshua Richter United States 22 863 0.7× 897 1.0× 660 0.9× 357 1.1× 261 1.0× 139 1.6k
Neha Korde United States 26 772 0.7× 1.4k 1.7× 1.1k 1.4× 260 0.8× 130 0.5× 124 2.1k
Todd DeVries United States 16 666 0.6× 230 0.3× 257 0.3× 590 1.9× 151 0.6× 58 1.3k
Sherilyn A. Tuazon United States 10 494 0.4× 517 0.6× 476 0.6× 128 0.4× 118 0.5× 27 941
Caitlin Costello United States 17 519 0.4× 455 0.5× 463 0.6× 157 0.5× 140 0.6× 64 910
Shambavi Richard United States 15 394 0.3× 353 0.4× 268 0.4× 114 0.4× 99 0.4× 64 708
Olalekan O. Oluwole United States 25 2.1k 1.8× 185 0.2× 604 0.8× 508 1.6× 70 0.3× 154 2.4k
Barry J. Kappel United States 16 336 0.3× 606 0.7× 330 0.4× 898 2.9× 255 1.0× 28 1.7k
Kasey J. Leger United States 17 1.2k 1.1× 171 0.2× 363 0.5× 200 0.6× 61 0.2× 44 1.7k
Julia Stieglmaier Germany 15 938 0.8× 413 0.5× 237 0.3× 364 1.2× 301 1.2× 24 1.4k

Countries citing papers authored by Deepu Madduri

Since Specialization
Citations

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

Fields of papers citing papers by Deepu Madduri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deepu Madduri

This figure shows the co-authorship network connecting the top 25 collaborators of Deepu Madduri. A scholar is included among the top collaborators of Deepu Madduri 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 Deepu Madduri. Deepu Madduri 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.
Lin, Yi, Thomas Martin, Saad Z. Usmani, et al.. (2023). POSTER: MM-309 CARTITUDE-1 Final Results: Phase 1b/2 Study of Ciltacabtagene Autoleucel in Heavily Pretreated Patients With Relapsed/Refractory Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 23. S226–S226. 1 indexed citations
2.
Jagannath, Sundar, Saad Z. Usmani, Jesús G. Berdeja, et al.. (2023). P-275 CARTITUDE-1 final results: phase 1b/2 study of ciltacabtagene autoleucel in heavily pretreated patients with relapsed/refractory multiple myeloma. Clinical Lymphoma Myeloma & Leukemia. 23. S187–S187. 2 indexed citations
3.
Restrepo, Paula, Sherry Bhalla, Yogita Ghodke‐Puranik, et al.. (2022). A Three-Gene Signature Predicts Response to Selinexor in Multiple Myeloma. JCO Precision Oncology. 6(6). e2200147–e2200147. 14 indexed citations
4.
Martin, Thomas, Carolyn C. Jackson, Lida Pacaud, Deepu Madduri, & Sundar Jagannath. (2022). Recent Advances in the Use of Chimeric Antigen Receptor–Expressing T-Cell Therapies for Treatment of Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 23(1). 22–27. 6 indexed citations
5.
Lancman, Guido, Samantha E. Jacobs, John T. Doucette, et al.. (2021). Efficacy of Intravenous Immunoglobulin for Preventing Infections in Patients with Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 21(5). e470–e476. 18 indexed citations
6.
Oekelen, Oliver Van, Adolfo Aleman, Bhaskar Upadhyaya, et al.. (2021). Neurocognitive and hypokinetic movement disorder with features of parkinsonism after BCMA-targeting CAR-T cell therapy. Nature Medicine. 27(12). 2099–2103. 151 indexed citations
7.
Restrepo, Paula, Sherry Bhalla, Adolfo Aleman, et al.. (2021). Transcriptomic Correlates of Response to Selinexor in Multiple Myeloma Reveal a Predictive Signature. Blood. 138(Supplement 1). 457–457. 1 indexed citations
9.
Chari, Ajai, Erika Florendo, Deepu Madduri, et al.. (2021). Optimal Supportive Care With Selinexor Improves Outcomes in Patients With Relapsed/Refractory Multiple Myeloma. Clinical Lymphoma Myeloma & Leukemia. 21(12). e975–e984. 5 indexed citations
10.
Leng, Siyang, Erin Moshier, Douglas Tremblay, et al.. (2020). Timing of Autologous Stem Cell Transplantation for Multiple Myeloma in the Era of Current Therapies. Clinical Lymphoma Myeloma & Leukemia. 20(10). e734–e751. 2 indexed citations
12.
Perumal, Deepak, Naoko Imai, Alessandro Laganà, et al.. (2019). Mutation-derived Neoantigen-specific T-cell Responses in Multiple Myeloma. Clinical Cancer Research. 26(2). 450–464. 59 indexed citations
13.
Cooper, Dennis, Deepu Madduri, Suzanne Lentzsch, et al.. (2019). Safety and Preliminary Clinical Activity of REGN5458, an Anti-Bcma x Anti-CD3 Bispecific Antibody, in Patients with Relapsed/Refractory Multiple Myeloma. Blood. 134(Supplement_1). 3176–3176. 31 indexed citations
15.
Laganà, Alessandro, Dan Fu Ruan, David T. Melnekoff, et al.. (2018). Increased HLA-E Expression Correlates with Early Relapse in Multiple Myeloma. Blood. 132(Supplement 1). 59–59. 3 indexed citations
16.
Laganà, Alessandro, Violetta V. Leshchenko, Marsha Crochiere, et al.. (2018). E2F1 Is a Biomarker of Selinexor Resistance in Relapsed/Refractory Multiple Myeloma Patients. Blood. 132(Supplement 1). 3216–3216. 8 indexed citations
17.
Shah, Nina, Melissa Alsina, David S. Siegel, et al.. (2018). Initial Results from a Phase 1 Clinical Study of bb21217, a Next-Generation Anti Bcma CAR T Therapy. Blood. 132(Supplement 1). 488–488. 66 indexed citations
18.
Lancman, Guido, Suzanne Arinsburg, Jeffrey S. Jhang, et al.. (2018). Blood Transfusion Management for Patients Treated With Anti-CD38 Monoclonal Antibodies. Frontiers in Immunology. 9. 2616–2616. 44 indexed citations
19.
Melnekoff, David T., Alessandro Laganà, Wissam Hamou, et al.. (2017). Single-Cell RNA Sequencing Reveals Distinct Transcriptomic Profiles of Multiple Myeloma with Implications for Personalized Medicine. Blood. 130. 62–62. 2 indexed citations
20.
Laganà, Alessandro, David T. Melnekoff, Violetta V. Leshchenko, et al.. (2017). Clonal Evolution in Newly Diagnosed Multiple Myeloma Patients: A Follow-up Study from the Mmrf Commpass Genomics Project. Blood. 130. 325–325. 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026