Rachel Perret

846 total citations
22 papers, 659 citations indexed

About

Rachel Perret is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Rachel Perret has authored 22 papers receiving a total of 659 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Immunology, 12 papers in Oncology and 6 papers in Molecular Biology. Recurrent topics in Rachel Perret's work include Immune Cell Function and Interaction (13 papers), Immunotherapy and Immune Responses (12 papers) and CAR-T cell therapy research (11 papers). Rachel Perret is often cited by papers focused on Immune Cell Function and Interaction (13 papers), Immunotherapy and Immune Responses (12 papers) and CAR-T cell therapy research (11 papers). Rachel Perret collaborates with scholars based in New Zealand, Switzerland and United States. Rachel Perret's co-authors include Franca Ronchese, Pedro Romero, Alena Donda, Sophie Sierro, Stéphanie Corgnac, Robert Weinkove, Yasmin Nouri, Bruno Salaun, Yun Ji and Thelma M. Escobar and has published in prestigious journals such as Blood, Immunity and The Journal of Immunology.

In The Last Decade

Rachel Perret

21 papers receiving 649 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rachel Perret New Zealand 12 454 250 216 145 40 22 659
Giovanni Barillari Italy 12 330 0.7× 166 0.7× 201 0.9× 65 0.4× 52 1.3× 30 682
Céline Robert-Tissot United States 10 255 0.6× 191 0.8× 188 0.9× 48 0.3× 39 1.0× 15 430
Nadine Kasnitz Germany 9 450 1.0× 250 1.0× 177 0.8× 52 0.4× 24 0.6× 10 682
Kristian Hallermalm Sweden 12 274 0.6× 141 0.6× 228 1.1× 45 0.3× 40 1.0× 18 504
Brandon Hogstad United States 5 390 0.9× 128 0.5× 256 1.2× 41 0.3× 62 1.6× 5 710
Raquel P. Deering United States 9 222 0.5× 94 0.4× 305 1.4× 66 0.5× 55 1.4× 16 532
Camillia S. Azimi United States 7 729 1.6× 599 2.4× 211 1.0× 43 0.3× 26 0.7× 10 998
Meijie Tian United States 9 262 0.6× 142 0.6× 137 0.6× 46 0.3× 71 1.8× 13 526
Nana Haahr Overgaard Denmark 11 257 0.6× 202 0.8× 147 0.7× 42 0.3× 36 0.9× 14 562
Huijuan Zhong China 12 242 0.5× 109 0.4× 286 1.3× 168 1.2× 66 1.6× 29 624

Countries citing papers authored by Rachel Perret

Since Specialization
Citations

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

Fields of papers citing papers by Rachel Perret

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rachel Perret

This figure shows the co-authorship network connecting the top 25 collaborators of Rachel Perret. A scholar is included among the top collaborators of Rachel Perret 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 Rachel Perret. Rachel Perret 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.
Dasyam, Nathaniel, Claire Turner, Alfonso Schmidt, et al.. (2025). Jurkat T-cell lines exhibit marked genomic instability affecting karyotype, mutational profile, gene expression, immunophenotype and function. Scientific Reports. 15(1). 22426–22426.
2.
Perret, Rachel, et al.. (2024). Tuning CAR T-cell therapies for efficacy and reduced toxicity. Seminars in Hematology. 61(5). 333–344. 6 indexed citations
3.
Nouri, Yasmin, Robert Weinkove, & Rachel Perret. (2023). An In Vitro Model to Assess CRS Potential of CAR T Cells Using a Tumor Cell Line and Autologous Monocytes. Current Protocols. 3(8). e864–e864. 4 indexed citations
4.
5.
Perret, Rachel & Kenneth W. Olsen. (2022). Magnetic selection for consistent cellular starting material in autologous cell therapy manufacture. Cell and Gene Therapy Insights. 8(1). 97–112. 2 indexed citations
6.
Nouri, Yasmin, Robert Weinkove, & Rachel Perret. (2021). T-cell intrinsic Toll-like receptor signaling: implications for cancer immunotherapy and CAR T-cells. Journal for ImmunoTherapy of Cancer. 9(11). e003065–e003065. 40 indexed citations
7.
Corgnac, Stéphanie, Rachel Perret, Lianjun Zhang, et al.. (2014). iNKT/CD1d-antitumor immunotherapy significantly increases the efficacy of therapeutic CpG/peptide-based cancer vaccine. Journal for ImmunoTherapy of Cancer. 2(1). 39–39. 14 indexed citations
8.
Corgnac, Stéphanie, Rachel Perret, Lianjun Zhang, et al.. (2014). iNKT/CD1d-antitumor immunotherapy significantly increases the efficacy of therapeutic CpG/peptide-based cancer vaccine. Journal for ImmunoTherapy of Cancer. 2(1). 39–39. 1 indexed citations
9.
Perret, Rachel, Sophie Sierro, Natalia K. Botelho, et al.. (2014). Analysis of Tumor-infiltrating Lymphocytes Following CD45 Enrichment. BIO-PROTOCOL. 4(16). 1 indexed citations
10.
Perret, Rachel, Sophie Sierro, Natalia K. Botelho, et al.. (2013). Adjuvants That Improve the Ratio of Antigen-Specific Effector to Regulatory T Cells Enhance Tumor Immunity. Cancer Research. 73(22). 6597–6608. 72 indexed citations
11.
Dudda, Jan C., Bruno Salaun, Yun Ji, et al.. (2013). MicroRNA-155 Is Required for Effector CD8+ T Cell Responses to Virus Infection and Cancer. Immunity. 38(4). 742–753. 252 indexed citations
12.
Corgnac, Stéphanie, Rachel Perret, Laurent Derré, et al.. (2012). CD1d-antibody fusion proteins target iNKT cells to the tumor and trigger long-term therapeutic responses. Cancer Immunology Immunotherapy. 62(4). 747–760. 30 indexed citations
13.
Joel, Z., Jianping Yang, Jim Qin, et al.. (2012). Inefficient boosting of antitumor CD8+T cells by dendritic-cell vaccines is rescued by restricting T-cell cytotoxic functions. OncoImmunology. 1(9). 1507–1516. 4 indexed citations
14.
Salaun, Bruno, Rachel Perret, Sophie Sierro, et al.. (2011). Saponins from the Spanish saffron Crocus sativus are efficient adjuvants for protein-based vaccines. Vaccine. 30(2). 388–397. 15 indexed citations
15.
Sierro, Sophie, Alena Donda, Rachel Perret, et al.. (2011). Combination of lentivector immunization and low‐dose chemotherapy or PD‐1/PD‐L1 blocking primes self‐reactive T cells and induces anti‐tumor immunity. European Journal of Immunology. 41(8). 2217–2228. 62 indexed citations
16.
Peacey, Matthew, Sarah Wilson, Rachel Perret, et al.. (2008). Virus-like particles from rabbit hemorrhagic disease virus can induce an anti-tumor response. Vaccine. 26(42). 5334–5337. 33 indexed citations
17.
Perret, Rachel, et al.. (2008). Dendritic Cells Treated with Lipopolysaccharide Up-Regulate Serine Protease Inhibitor 6 and Remain Sensitive to Killing by Cytotoxic T Lymphocytes In Vivo. The Journal of Immunology. 181(12). 8356–8362. 19 indexed citations
18.
Perret, Rachel & Franca Ronchese. (2008). Effector CD8+ T cells activated in vitro confer immediate and long‐term tumor protection in vivo. European Journal of Immunology. 38(10). 2886–2895. 14 indexed citations
19.
Perret, Rachel & Franca Ronchese. (2008). Memory T cells in cancer immunotherapy: which CD8+ T‐cell population provides the best protection against tumours?. Tissue Antigens. 72(3). 187–194. 47 indexed citations
20.
Panhuys, Nicholas van, Rachel Perret, Marianne N. Prout, Franca Ronchese, & Graham Le Gros. (2005). Effector lymphoid tissue and its crucial role in protective immunity. Trends in Immunology. 26(5). 242–247. 36 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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