Gary Kohanbash

6.1k total citations · 1 hit paper
76 papers, 3.9k citations indexed

About

Gary Kohanbash is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, Gary Kohanbash has authored 76 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Immunology, 28 papers in Molecular Biology and 27 papers in Oncology. Recurrent topics in Gary Kohanbash's work include Glioma Diagnosis and Treatment (23 papers), Immunotherapy and Immune Responses (20 papers) and Immune cells in cancer (17 papers). Gary Kohanbash is often cited by papers focused on Glioma Diagnosis and Treatment (23 papers), Immunotherapy and Immune Responses (20 papers) and Immune cells in cancer (17 papers). Gary Kohanbash collaborates with scholars based in United States, China and Israel. Gary Kohanbash's co-authors include Hideho Okada, Mitsugu Fujita, Ryo Ueda, Ronald L. Hamilton, John R. Ohlfest, Manish K. Aghi, Heather A. McDonald, Edward R. Kastenhuber, Wendy Fellows-Mayle and Xinmei Zhu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Investigation and Journal of Clinical Oncology.

In The Last Decade

Gary Kohanbash

69 papers receiving 3.9k citations

Hit Papers

Single-cell profiling of ... 2017 2026 2020 2023 2017 100 200 300 400

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Gary Kohanbash 2.1k 1.5k 1.4k 1.2k 812 76 3.9k
Jun Wei 2.1k 1.0× 1.8k 1.2× 1.4k 1.0× 1.4k 1.1× 952 1.2× 57 4.2k
Florian Klemm 1.5k 0.7× 1.3k 0.8× 1.6k 1.2× 754 0.6× 696 0.9× 31 3.7k
Mahua Dey 1.5k 0.7× 1.5k 1.0× 1.0k 0.7× 1.3k 1.1× 430 0.5× 76 3.6k
Leila Akkari 3.3k 1.5× 1.8k 1.2× 1.9k 1.4× 1.0k 0.9× 930 1.1× 45 5.6k
Yoko Oei 1.9k 0.9× 1.1k 0.7× 1.4k 1.0× 541 0.4× 338 0.4× 17 3.6k
Manu Setty 2.7k 1.3× 1.7k 1.2× 3.2k 2.3× 555 0.5× 1.1k 1.4× 31 6.0k
Yi‐Fang Ping 929 0.4× 1.4k 0.9× 1.9k 1.4× 621 0.5× 1.2k 1.4× 70 3.7k
Juan Manuel Sepúlveda-Sánchez 844 0.4× 1.5k 1.0× 1.2k 0.9× 1.7k 1.4× 637 0.8× 181 3.9k
Friedemann Kiefer 1.7k 0.8× 2.0k 1.3× 2.8k 2.0× 246 0.2× 582 0.7× 98 5.9k
Yu Yao 670 0.3× 893 0.6× 1.1k 0.8× 1.1k 0.9× 772 1.0× 112 3.0k

Countries citing papers authored by Gary Kohanbash

Since Specialization
Citations

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

Fields of papers citing papers by Gary Kohanbash

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gary Kohanbash

This figure shows the co-authorship network connecting the top 25 collaborators of Gary Kohanbash. A scholar is included among the top collaborators of Gary Kohanbash 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 Gary Kohanbash. Gary Kohanbash 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.
Reinheimer, Jorge, Ahmed Habib, Baoli Hu, et al.. (2025). Transcript-targeted antigen mapping reveals the potential of POSTN splicing junction epitopes in glioblastoma immunotherapy. Genes and Immunity. 26(3). 190–199.
2.
Kohanbash, Gary, et al.. (2025). NK cells link immune-checkpoint blockade immunotherapy and response in melanoma brain metastases. Journal for ImmunoTherapy of Cancer. 13(3). e011581–e011581. 1 indexed citations
3.
Agnihotri, Sameer, Baoli Hu, Jan Drappatz, et al.. (2024). Assessment of anti-CD69 antibody therapy alone or in combination with anti-PD-1 in murine GBM. Expert Review of Clinical Immunology. 21(2). 239–247. 1 indexed citations
4.
Huq, Sakibul, et al.. (2024). Barriers to T Cell Functionality in the Glioblastoma Microenvironment. Cancers. 16(19). 3273–3273. 3 indexed citations
5.
Rosenblum, Lauren, et al.. (2023). Survival following complete resection of neuroblastoma in novel orthotopic rat xenograft model. Scientific Reports. 13(1). 20214–20214. 1 indexed citations
6.
Gilbert, Ryan J., et al.. (2022). Handheld PET Probe for Pediatric Cancer Surgery. Cancers. 14(9). 2221–2221. 7 indexed citations
7.
8.
Chuntova, Pavlina, Yafei Hou, Akane Yamamichi, et al.. (2021). Novel EGFRvIII-CAR transgenic mice for rigorous preclinical studies in syngeneic mice. Neuro-Oncology. 24(2). 259–272. 11 indexed citations
9.
Jackson, Joseph W., Bonnie L. Hall, Lisa Bailey, et al.. (2021). Treatment of glioblastoma with current oHSV variants reveals differences in efficacy and immune cell recruitment. Molecular Therapy — Oncolytics. 22. 444–453. 7 indexed citations
10.
Luo, Lanxin, Xiudong Guan, Gulnaz Begum, et al.. (2020). Blockade of Cell Volume Regulatory Protein NKCC1 Increases TMZ-Induced Glioma Apoptosis and Reduces Astrogliosis. Molecular Cancer Therapeutics. 19(7). 1550–1561. 19 indexed citations
11.
Wang, Lin, Husam Babikir, Sören Müller, et al.. (2019). The Phenotypes of Proliferating Glioblastoma Cells Reside on a Single Axis of Variation. Cancer Discovery. 9(12). 1708–1719. 194 indexed citations
12.
Müller, Sören, Sameer Agnihotri, Srilakshmi Chaparala, et al.. (2018). Peptide vaccine immunotherapy biomarkers and response patterns in pediatric gliomas. JCI Insight. 3(7). 24 indexed citations
14.
Okada, Hideho, Lisa H. Butterfield, Ronald L. Hamilton, et al.. (2014). Induction of Robust Type-I CD8+ T-cell Responses in WHO Grade 2 Low-Grade Glioma Patients Receiving Peptide-Based Vaccines in Combination with Poly-ICLC. Clinical Cancer Research. 21(2). 286–294. 83 indexed citations
15.
Kohanbash, Gary, Masashi Sakaki, Ryo Ueda, et al.. (2013). GM-CSF Promotes the Immunosuppressive Activity of Glioma-Infiltrating Myeloid Cells through Interleukin-4 Receptor-α. Cancer Research. 73(21). 6413–6423. 134 indexed citations
16.
Fujita, Mitsugu, Gary Kohanbash, Wendy Fellows-Mayle, et al.. (2011). COX-2 Blockade Suppresses Gliomagenesis by Inhibiting Myeloid-Derived Suppressor Cells. Cancer Research. 71(7). 2664–2674. 315 indexed citations
17.
Fujita, Mitsugu, Michael E. Scheurer, Stacy A. Decker, et al.. (2010). Role of Type 1 IFNs in Antiglioma Immunosurveillance—Using Mouse Studies to Guide Examination of Novel Prognostic Markers in Humans. Clinical Cancer Research. 16(13). 3409–3419. 76 indexed citations
18.
Ueda, Ryo, Mitsugu Fujita, Xinmei Zhu, et al.. (2009). Systemic Inhibition of Transforming Growth Factor-β in Glioma-Bearing Mice Improves the Therapeutic Efficacy of Glioma-Associated Antigen Peptide Vaccines. Clinical Cancer Research. 15(21). 6551–6559. 94 indexed citations
19.
Fujita, Mitsugu, Xinmei Zhu, Ryo Ueda, et al.. (2009). Effective Immunotherapy against Murine Gliomas Using Type 1 Polarizing Dendritic Cells—Significant Roles of CXCL10. Cancer Research. 69(4). 1587–1595. 84 indexed citations
20.
Ueda, Ryo, Gary Kohanbash, Kotaro Sasaki, et al.. (2008). Dicer-regulated micro RNAs 222 and 339 promote immune-escape of cancer cells through downregulation of ICAM-1. Cancer Research. 68. 2468–2468. 1 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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