Aaron N. Hata

14.5k total citations · 3 hit papers
85 papers, 3.4k citations indexed

About

Aaron N. Hata is a scholar working on Oncology, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Aaron N. Hata has authored 85 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Oncology, 46 papers in Molecular Biology and 44 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Aaron N. Hata's work include Lung Cancer Treatments and Mutations (42 papers), Lung Cancer Research Studies (17 papers) and Cancer Genomics and Diagnostics (16 papers). Aaron N. Hata is often cited by papers focused on Lung Cancer Treatments and Mutations (42 papers), Lung Cancer Research Studies (17 papers) and Cancer Genomics and Diagnostics (16 papers). Aaron N. Hata collaborates with scholars based in United States, Japan and China. Aaron N. Hata's co-authors include Richard Breyer, Jeffrey A. Engelman, Anthony C. Faber, Justin F. Gainor, Heidie Frisco Cabanos, Mari Mino–Kenudson, Ibiayi Dagogo‐Jack, W. Marston Linehan, Catherine B. Meador and Satoshi Yoda and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Aaron N. Hata

79 papers receiving 3.3k citations

Hit Papers

Pharmacology and signaling of prostaglandin receptors: Mu... 2004 2026 2011 2018 2004 2015 2021 200 400 600

Peers

Aaron N. Hata
Daniel C. Chan United States
Christopher L. Morton United States
Shan Zha United States
Taebo Sim South Korea
Ker Yu United States
Min H. Kang United States
Pixu Liu China
Peter L. Toogood United States
Daniel C. Chan United States
Aaron N. Hata
Citations per year, relative to Aaron N. Hata Aaron N. Hata (= 1×) peers Daniel C. Chan

Countries citing papers authored by Aaron N. Hata

Since Specialization
Citations

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

Fields of papers citing papers by Aaron N. Hata

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aaron N. Hata. 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 Aaron N. Hata. The network helps show where Aaron N. Hata may publish in the future.

Co-authorship network of co-authors of Aaron N. Hata

This figure shows the co-authorship network connecting the top 25 collaborators of Aaron N. Hata. A scholar is included among the top collaborators of Aaron N. Hata 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 Aaron N. Hata. Aaron N. Hata 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.
Takamori, Shinkichi, Hideko Isozaki, Keisuke Shigeta, et al.. (2025). Activation of APOBEC3 cytidine deaminases and endogenous retroviruses is integrated by MUC1-C in NSCLC cells. Cell Death Discovery. 11(1). 372–372.
2.
Takamori, Shinkichi, Atrayee Bhattacharya, Chie Kikutake, et al.. (2025). MUCIN 1 confers inflammatory memory of tyrosine kinase inhibitor resistance in non-small cell lung cancer. Signal Transduction and Targeted Therapy. 10(1). 389–389.
3.
Egan, Regina K., Jianli Ma, Jong Kung, et al.. (2025). Targeting Monounsaturated Fatty Acid Metabolism for Radiosensitization of KRAS Mutant 3D Lung Cancer Models. Molecular Cancer Therapeutics. 24(6). 920–930. 1 indexed citations
4.
Sun, Xiaoxiao, Lani F. Wu, Steven J. Altschuler, & Aaron N. Hata. (2024). Targeting therapy-persistent residual disease. Nature Cancer. 5(9). 1298–1304. 7 indexed citations
5.
Whitfield, Troy W., Asaf Maoz, Dian Yang, et al.. (2024). Targeted therapies prime oncogene-driven lung cancers for macrophage-mediated destruction. Journal of Clinical Investigation. 134(9). 8 indexed citations
6.
Waliany, Sarah, Andrew Do, Aimin Liu, et al.. (2024). P1.12B.02 Mechanisms of Resistance to First-Line vs Later-Line Alectinib in ALK Fusion-Positive Non-Small Cell Lung Cancer. Journal of Thoracic Oncology. 19(10). S199–S200. 2 indexed citations
7.
Nardi, Francesca, Naiara Perurena, Amy E. Schade, et al.. (2023). Cotargeting a MYC/eIF4A-survival axis improves the efficacy of KRAS inhibitors in lung cancer. Journal of Clinical Investigation. 133(16). 12 indexed citations
8.
Haratake, Naoki, Hiroki Ozawa, Yoshihiro Morimoto, et al.. (2023). Abstract B089: MUC1-C is a common driver of acquired osimertinb resistance in NSCLC. Molecular Cancer Therapeutics. 22(12_Supplement). B089–B089. 2 indexed citations
9.
Heather, James, Matthew Spindler, David G. Millar, et al.. (2022). Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information. Nucleic Acids Research. 50(12). e68–e68. 18 indexed citations
10.
Ali, Moiez, Min Lu, Ryan S. Soderquist, et al.. (2022). Small-molecule targeted therapies induce dependence on DNA double-strand break repair in residual tumor cells. Science Translational Medicine. 14(638). eabc7480–eabc7480. 29 indexed citations
12.
Tanaka, Noritaka, W. Marston Linehan, Chendi Li, et al.. (2021). Clinical Acquired Resistance to KRASG12C Inhibition through a Novel KRAS Switch-II Pocket Mutation and Polyclonal Alterations Converging on RAS–MAPK Reactivation. Cancer Discovery. 11(8). 1913–1922. 302 indexed citations breakdown →
13.
Cabanos, Heidie Frisco & Aaron N. Hata. (2021). Emerging Insights into Targeted Therapy-Tolerant Persister Cells in Cancer. Cancers. 13(11). 2666–2666. 122 indexed citations
14.
Davoudi, Farideh, Satoshi Yoda, Ellen Murchie, et al.. (2021). Alginate-based 3D cancer cell culture for therapeutic response modeling. STAR Protocols. 2(2). 100391–100391. 4 indexed citations
15.
Dagogo‐Jack, Ibiayi, Philicia Moonsamy, Justin F. Gainor, et al.. (2021). A Phase 2 Study of Capmatinib in Patients With MET-Altered Lung Cancer Previously Treated With a MET Inhibitor. Journal of Thoracic Oncology. 16(5). 850–859. 40 indexed citations
16.
McClatchy, David M., Henning Willers, Aaron N. Hata, et al.. (2020). Modeling Resistance and Recurrence Patterns of Combined Targeted–Chemoradiotherapy Predicts Benefit of Shorter Induction Period. Cancer Research. 80(22). 5121–5133. 8 indexed citations
17.
Grassberger, Clemens, David M. McClatchy, Changran Geng, et al.. (2019). Patient-Specific Tumor Growth Trajectories Determine Persistent and Resistant Cancer Cell Populations during Treatment with Targeted Therapies. Cancer Research. 79(14). 3776–3788. 24 indexed citations
18.
Dagogo‐Jack, Ibiayi, Marguerite Rooney, W. Marston Linehan, et al.. (2019). Treatment with Next-Generation ALK Inhibitors Fuels Plasma ALK Mutation Diversity. Clinical Cancer Research. 25(22). 6662–6670. 112 indexed citations
19.
Hata, Aaron N., Alan T. Yeo, Anthony C. Faber, et al.. (2014). Failure to Induce Apoptosis via BCL-2 Family Proteins Underlies Lack of Efficacy of Combined MEK and PI3K Inhibitors for KRAS-Mutant Lung Cancers. Cancer Research. 74(11). 3146–3156. 60 indexed citations
20.
Corcoran, Ryan B., S. Michael Rothenberg, Aaron N. Hata, et al.. (2013). TORC1 Suppression Predicts Responsiveness to RAF and MEK Inhibition in BRAF- Mutant Melanoma. Science Translational Medicine. 5(196). 196ra98–196ra98. 110 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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