Kaiming Sun

6.0k total citations · 1 hit paper
20 papers, 1.1k citations indexed

About

Kaiming Sun is a scholar working on Molecular Biology, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Kaiming Sun has authored 20 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 11 papers in Oncology and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Kaiming Sun's work include PARP inhibition in cancer therapy (11 papers), DNA Repair Mechanisms (3 papers) and Protein Degradation and Inhibitors (3 papers). Kaiming Sun is often cited by papers focused on PARP inhibition in cancer therapy (11 papers), DNA Repair Mechanisms (3 papers) and Protein Degradation and Inhibitors (3 papers). Kaiming Sun collaborates with scholars based in United States, United Kingdom and Canada. Kaiming Sun's co-authors include Keith Mikule, Sridhar Ramaswamy, Jeffrey H. Hanke, Zebin Wang, Ian Jackson, Richard A. Young, Michael J. Townsend, Graham M. Lord, Richard G. Jenner and Russell D. Bouwman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and Genes & Development.

In The Last Decade

Kaiming Sun

20 papers receiving 1.0k 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.

Niraparib monotherapy for late-line treatment of ovarian cancer (QUADRA): a multicentre, open-label, single-arm, phase 2 trial

2019 377 citations Kathleen N. Moore, Angeles Alvarez Secord et al. The Lancet Oncology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kaiming Sun United States	9	685	448	272	268	105	20	1.1k
Mei‐Kuang Chen United States	12	912 1.3×	527 1.2×	105 0.4×	348 1.3×	191 1.8×	24	1.2k
Denarda Dangaj Laniti Switzerland	15	703 1.0×	367 0.8×	94 0.3×	544 2.0×	78 0.7×	31	1.1k
Elizabeth L. Christie Australia	13	403 0.6×	498 1.1×	214 0.8×	120 0.4×	87 0.8×	24	883
Yang Peng United States	12	789 1.2×	906 2.0×	141 0.5×	287 1.1×	146 1.4×	27	1.4k
Katarzyna Kluzek Poland	8	819 1.2×	811 1.8×	119 0.4×	90 0.3×	143 1.4×	14	1.1k
Johanne I. Weberpals Canada	20	619 0.9×	687 1.5×	378 1.4×	101 0.4×	133 1.3×	56	1.3k
Ana M. Mendes‐Pereira United Kingdom	8	559 0.8×	737 1.6×	141 0.5×	47 0.2×	101 1.0×	10	973
Ioannis Assiotis United Kingdom	12	699 1.0×	1.0k 2.2×	110 0.4×	85 0.3×	198 1.9×	14	1.4k
Anita K. Mehta United States	12	457 0.7×	442 1.0×	36 0.1×	345 1.3×	118 1.1×	20	970
Anna L Olsen United Kingdom	7	535 0.8×	727 1.6×	36 0.1×	213 0.8×	50 0.5×	9	964

Countries citing papers authored by Kaiming Sun

Since Specialization

Citations

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

Fields of papers citing papers by Kaiming Sun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaiming Sun

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Lakhani, Nehal J., Martin Gutierrez, Linda Duska, et al.. (2021). Phase 1/2 first-in-human (FIH) study of CPI-0209, a novel small molecule inhibitor of enhancer of zeste homolog 2 (EZH2) in patients with advanced tumors.. Journal of Clinical Oncology. 39(15_suppl). 3104–3104. 10 indexed citations

Zhao, Feng, Patricia J. Keller, Jennifer A. Mertz, et al.. (2021). Abstract 2126: Therapeutic potential of CPI-0209. Cancer Research. 81(13_Supplement). 2126–2126. 1 indexed citations

Wang, Zebin, Kaiming Sun, Yonghong Xiao, et al.. (2019). Niraparib activates interferon signaling and potentiates anti-PD-1 antibody efficacy in tumor models. Scientific Reports. 9(1). 1853–1853. 178 indexed citations

Mirza, Mansoor Raza, Bin Feng, Ming Shan, et al.. (2019). Elucidation of PARP inhibitor activity in BRCAwt recurrent ovarian cancer by hrr mutational gene profile analysis.. Journal of Clinical Oncology. 37(15_suppl). 5568–5568. 2 indexed citations

Moore, Kathleen N., Angeles Alvarez Secord, Melissa A. Geller, et al.. (2019). Niraparib monotherapy for late-line treatment of ovarian cancer (QUADRA): a multicentre, open-label, single-arm, phase 2 trial. The Lancet Oncology. 20(5). 636–648. 377 indexed citations breakdown →

Rose, Rachel H., et al.. (2019). Abstract 2952: Predicting concentration of PARP inhibitors in human tumor tissue using PBPK modeling. 2952–2952. 1 indexed citations

Rose, Rachel H., et al.. (2019). Abstract 2952: Predicting concentration of PARP inhibitors in human tumor tissue using PBPK modeling. Cancer Research. 79(13_Supplement). 2952–2952. 1 indexed citations

Sambade, Maria J., Amanda E.D. Van Swearingen, Marni B. McClure, et al.. (2019). Efficacy and pharmacodynamics of niraparib in BRCA-mutant and wild-type intracranial triple-negative breast cancer murine models. Neuro-Oncology Advances. 1(1). vdz005–vdz005. 12 indexed citations

Moore, Kathleen N., Angeles Alvarez Secord, Melissa A. Geller, et al.. (2019). Abstract NT-101: QUADRA: A PHASE 2, OPEN-LABEL, SINGLE-ARM STUDY TO EVALUATE SINGLE-AGENT NIRAPARIB TREATMENT IN PATIENTS WITH RELAPSED OVARIAN CANCER (ROC) WHO HAVE RECEIVED ≥3 PRIOR CHEMOTHERAPY REGIMENS. Clinical Cancer Research. 25(22_Supplement). NT–101. 1 indexed citations

10.

Sun, Kaiming, Yonghong Xiao, Bin Feng, et al.. (2018). Abstract 1724: Evaluation of niraparib in combination with anti-PD1/anti-PD-L1 in preclinical models. Cancer Research. 78(13_Supplement). 1724–1724. 6 indexed citations

11.

Sun, Kaiming, Keith Mikule, Zebin Wang, et al.. (2018). A comparative pharmacokinetic study of PARP inhibitors demonstrates favorable properties for niraparib efficacy in preclinical tumor models. Oncotarget. 9(98). 37080–37096. 115 indexed citations

12.

Sun, Kaiming, Grace K. Poon, Sarah Wang, et al.. (2018). Abstract A102: A comparative pharmacokinetic-pharmacodynamic-treatment study of PARP inhibitors demonstrates favorable properties for niraparib activity in preclinical tumor models. Molecular Cancer Therapeutics. 17(1_Supplement). A102–A102. 3 indexed citations

13.

Sambade, Maria J., et al.. (2018). Abstract 2813: Efficacy and pharmacokinetics of niraparib in BRCA-mutant and wild-type intracranial triple negative breast cancer murine models. Cancer Research. 78(13_Supplement). 2813–2813. 1 indexed citations

14.

Sun, Kaiming, Ruzanna Atoyan, Mylissa Borek, et al.. (2016). Dual HDAC and PI3K Inhibitor CUDC-907 Downregulates MYC and Suppresses Growth of MYC-dependent Cancers. Molecular Cancer Therapeutics. 16(2). 285–299. 109 indexed citations

15.

Sun, Kaiming, Ruzanna Atoyan, Mylissa Borek, et al.. (2016). The Combination of Venetoclax and CUDC-907 Exhibits Synergistic Activity in Venetoclax-Refractory DLBCL. Blood. 128(22). 4184–4184. 4 indexed citations

16.

Younes, Anas, Jesús G. Berdeja, Manish R. Patel, et al.. (2015). Phase 1 Trial Testing Single Agent CUDC-907, a Novel, Oral Dual Inhibitor of Histone Deacetylase (HDAC) and PI3K: Initial Assessment of Patients with Relapsed or Refractory (RR) Diffuse Large B-Cell Lymphoma (DLBCL), Including Double Expressor (DE) Lymphoma. Blood. 126(23). 257–257. 4 indexed citations

17.

Jenner, Richard G., Michael J. Townsend, Ian Jackson, et al.. (2009). The transcription factors T-bet and GATA-3 control alternative pathways of T-cell differentiation through a shared set of target genes. Proceedings of the National Academy of Sciences. 106(42). 17876–17881. 186 indexed citations

18.

Coı̈c, Eric, Kaiming Sun, Çherry Wu, & James E. Haber. (2006). Cell Cycle-Dependent Regulation of Saccharomyces cerevisiae Donor Preference during Mating-Type Switching by SBF (Swi4/Swi6) and Fkh1. Molecular and Cellular Biology. 26(14). 5470–5480. 19 indexed citations

19.

Sun, Kaiming, et al.. (2002). Saccharomyces forkhead protein Fkh1 regulates donor preference during mating-type switching through the recombination enhancer. Genes & Development. 16(16). 2085–2096. 38 indexed citations

20.

Sun, Kaiming, et al.. (2001). A Saccharomyces servazzii clone homologous to Saccharomyces cerevisiae chromosome III spanning KAR4, ARS 304 and SPB1 lacks the recombination enhancer but contains an unknown ORF. Yeast. 18(9). 789–795. 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