Tamara Laskowski

1.3k total citations · 1 hit paper
8 papers, 645 citations indexed

About

Tamara Laskowski is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Tamara Laskowski has authored 8 papers receiving a total of 645 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Oncology, 3 papers in Molecular Biology and 3 papers in Immunology. Recurrent topics in Tamara Laskowski's work include CAR-T cell therapy research (5 papers), Immune Cell Function and Interaction (3 papers) and Virus-based gene therapy research (1 paper). Tamara Laskowski is often cited by papers focused on CAR-T cell therapy research (5 papers), Immune Cell Function and Interaction (3 papers) and Virus-based gene therapy research (1 paper). Tamara Laskowski collaborates with scholars based in United States, Switzerland and Belgium. Tamara Laskowski's co-authors include Katayoun Rezvani, Alexander Biederstädt, Harjeet Singh, Drew C. Deniger, Laurence J.N. Cooper, Simon Olivares, Radhika Thokala, Helen Huls, Hiroki Torikai and Richard E. Champlin and has published in prestigious journals such as The Journal of Experimental Medicine, Nature reviews. Cancer and PLoS ONE.

In The Last Decade

Tamara Laskowski

8 papers receiving 636 citations

Hit Papers

Natural killer cells in antitumour adoptive cell immunoth... 2022 2026 2023 2024 2022 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tamara Laskowski United States 7 403 395 201 90 76 8 645
Stephanie Silveria United States 5 472 1.2× 328 0.8× 223 1.1× 51 0.6× 84 1.1× 6 723
Marine Cazaux France 10 281 0.7× 370 0.9× 185 0.9× 152 1.7× 71 0.9× 14 575
Alexander Biederstädt Germany 5 402 1.0× 375 0.9× 155 0.8× 81 0.9× 45 0.6× 9 604
James Dongjoo Ham United States 6 550 1.4× 643 1.6× 210 1.0× 132 1.5× 96 1.3× 9 832
Ranjan Upadhyay United States 7 392 1.0× 358 0.9× 151 0.8× 84 0.9× 52 0.7× 12 582
Jonathan Fisher United Kingdom 14 597 1.5× 648 1.6× 150 0.7× 119 1.3× 129 1.7× 30 961
Kevin M. Friedman United States 8 283 0.7× 405 1.0× 193 1.0× 57 0.6× 55 0.7× 15 530
Anna Capsomidis United Kingdom 5 397 1.0× 381 1.0× 129 0.6× 62 0.7× 85 1.1× 7 622
Tiziano Ingegnere Italy 11 391 1.0× 280 0.7× 161 0.8× 40 0.4× 37 0.5× 15 604
Sabine Heitzeneder United States 5 243 0.6× 416 1.1× 170 0.8× 159 1.8× 113 1.5× 13 599

Countries citing papers authored by Tamara Laskowski

Since Specialization
Citations

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

Fields of papers citing papers by Tamara Laskowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tamara Laskowski

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

All Works

8 of 8 papers shown
1.
Meo, Francesco Di, Rujin Cheng, Christina Y. Yu, et al.. (2023). A target discovery pipeline identified ILT3 as a target for immunotherapy of multiple myeloma. Cell Reports Medicine. 4(7). 101110–101110. 17 indexed citations
2.
Laskowski, Tamara, Alexander Biederstädt, & Katayoun Rezvani. (2022). Natural killer cells in antitumour adoptive cell immunotherapy. Nature reviews. Cancer. 22(10). 557–575. 469 indexed citations breakdown →
3.
Meo, Francesco Di, Rujin Cheng, Christina Y. Yu, et al.. (2022). A Target Discovery Pipeline Identified ILT3 as a Target for Immunotherapy of Multiple Myeloma. SSRN Electronic Journal. 5 indexed citations
4.
Laskowski, Tamara & Katayoun Rezvani. (2020). Adoptive cell therapy: Living drugs against cancer. The Journal of Experimental Medicine. 217(12). 32 indexed citations
5.
Silva, Thiago Aparecido da, Paul Hauser, Irfan Bandey, et al.. (2020). Glucuronoxylomannan in the Cryptococcus species capsule as a target for Chimeric Antigen Receptor T-cell therapy. Cytotherapy. 23(2). 119–130. 22 indexed citations
6.
Laskowski, Tamara, et al.. (2019). Rigor and Reproducibility of Cytometry Practices for Immuno‐Oncology: A multifaceted challenge. Cytometry Part A. 97(2). 116–125. 13 indexed citations
7.
Laskowski, Tamara, Rasoul Pourebrahim, Chao Ma, et al.. (2016). Gene Correction of iPSCs from a Wiskott-Aldrich Syndrome Patient Normalizes the Lymphoid Developmental and Functional Defects. Stem Cell Reports. 7(2). 139–148. 36 indexed citations
8.
Thokala, Radhika, Simon Olivares, Tiejuan Mi, et al.. (2016). Redirecting Specificity of T cells Using the Sleeping Beauty System to Express Chimeric Antigen Receptors by Mix-and-Matching of VL and VH Domains Targeting CD123+ Tumors. PLoS ONE. 11(8). e0159477–e0159477. 51 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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