Leah V. Sibener

1.9k total citations · 1 hit paper
15 papers, 1.4k citations indexed

About

Leah V. Sibener is a scholar working on Immunology, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Leah V. Sibener has authored 15 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Immunology, 8 papers in Oncology and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Leah V. Sibener's work include Immune Cell Function and Interaction (9 papers), CAR-T cell therapy research (8 papers) and T-cell and B-cell Immunology (7 papers). Leah V. Sibener is often cited by papers focused on Immune Cell Function and Interaction (9 papers), CAR-T cell therapy research (8 papers) and T-cell and B-cell Immunology (7 papers). Leah V. Sibener collaborates with scholars based in United States, Netherlands and India. Leah V. Sibener's co-authors include K. Christopher García, Marvin H. Gee, Kevin M. Jude, Michael E. Birnbaum, Xinbo Yang, Erin J. Adams, Jennifer E. Kung, Ricardo A. Fernandes, Mark M. Davis and Elizabeth Motunrayo Kolawole and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Leah V. Sibener

15 papers receiving 1.4k 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.

Selective targeting of engineered T cells using orthogonal IL-2 cytokine-receptor complexes

2018 275 citations Jonathan T. Sockolosky, Eleonora Trotta et al. Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Leah V. Sibener United States	12	1.0k	644	339	188	157	15	1.4k
Noor Momin United States	13	648 0.6×	632 1.0×	354 1.0×	155 0.8×	226 1.4×	24	1.1k
Naveen K. Mehta United States	12	616 0.6×	530 0.8×	380 1.1×	133 0.7×	215 1.4×	26	991
Jonathan T. Sockolosky United States	15	885 0.9×	583 0.9×	558 1.6×	258 1.4×	217 1.4×	21	1.6k
Lee Kim Swee Germany	17	787 0.8×	240 0.4×	488 1.4×	217 1.2×	73 0.5×	27	1.4k
Peter Ellmark Sweden	18	610 0.6×	598 0.9×	492 1.5×	342 1.8×	218 1.4×	62	1.3k
Gianfranco Picco United Kingdom	11	725 0.7×	448 0.7×	887 2.6×	219 1.2×	132 0.8×	13	1.3k
Barbara Schrörs Germany	12	1.2k 1.2×	1.0k 1.6×	783 2.3×	175 0.9×	163 1.0×	23	1.7k
Jason W. Pyrdol United States	19	1.9k 1.8×	742 1.2×	601 1.8×	300 1.6×	89 0.6×	22	2.4k
Mathias Vormehr Germany	13	1.2k 1.2×	990 1.5×	850 2.5×	143 0.8×	153 1.0×	23	1.8k

Countries citing papers authored by Leah V. Sibener

Since Specialization

Citations

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

Fields of papers citing papers by Leah V. Sibener

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leah V. Sibener. 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 Leah V. Sibener. The network helps show where Leah V. Sibener may publish in the future.

Co-authorship network of co-authors of Leah V. Sibener

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

All Works

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15 of 15 papers shown

Chan, Waipan, Xiang Zhao, Dongya Jia, et al.. (2023). TCR ligand potency differentially impacts PD-1 inhibitory effects on diverse signaling pathways. The Journal of Experimental Medicine. 220(12). 7 indexed citations

Zhao, Xiang, Elizabeth Motunrayo Kolawole, Waipan Chan, et al.. (2022). Tuning T cell receptor sensitivity through catch bond engineering. Science. 376(6589). eabl5282–eabl5282. 88 indexed citations

Fernandes, Ricardo A., Chaoran Li, Gang Wang, et al.. (2020). Discovery of surrogate agonists for visceral fat Treg cells that modulate metabolic indices in vivo. eLife. 9. 23 indexed citations

Gerber, Hans‐Peter, et al.. (2020). Identification of Antigenic Targets. Trends in cancer. 6(4). 299–318. 7 indexed citations

Gerber, Hans‐Peter, et al.. (2019). Intracellular targets as source for cleaner targets for the treatment of solid tumors. Biochemical Pharmacology. 168. 275–284. 11 indexed citations

Sockolosky, Jonathan T., Eleonora Trotta, Giulia Parisi, et al.. (2018). Selective targeting of engineered T cells using orthogonal IL-2 cytokine-receptor complexes. Science. 359(6379). 1037–1042. 275 indexed citations breakdown →

Gee, Marvin H., Leah V. Sibener, Michael E. Birnbaum, et al.. (2018). Stress-testing the relationship between T cell receptor/peptide-MHC affinity and cross-reactivity using peptide velcro. Proceedings of the National Academy of Sciences. 115(31). E7369–E7378. 18 indexed citations

Sibener, Leah V., Ricardo A. Fernandes, Elizabeth Motunrayo Kolawole, et al.. (2018). Isolation of a Structural Mechanism for Uncoupling T Cell Receptor Signaling from Peptide-MHC Binding. Cell. 174(3). 672–687.e27. 206 indexed citations

Gee, Marvin H., Arnold Han, Shane Lofgren, et al.. (2017). Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes. Cell. 172(3). 549–563.e16. 193 indexed citations

10.

Sharon, Eilon, Leah V. Sibener, Alexis Battle, et al.. (2016). Genetic variation in MHC proteins is associated with T cell receptor expression biases. Nature Genetics. 48(9). 995–1002. 102 indexed citations

11.

Adams, Jarrett, Samanthi Narayanan, Michael E. Birnbaum, et al.. (2015). Structural interplay between germline interactions and adaptive recognition determines the bandwidth of TCR-peptide-MHC cross-reactivity. Nature Immunology. 17(1). 87–94. 79 indexed citations

12.

Schiapparelli, Paula, et al.. (2014). CB-15 * NKCC1 REGULATES MIGRATION CAPACITY OF GLIOBLASTOMA TUMOR INITIATING CELLS BY MODULATING ACTIN CYTOSKELETON THROUGH SMALL RHO GTPASES. Neuro-Oncology. 16(suppl 5). v43–v44. 1 indexed citations

13.

Luoma, Adrienne, Caitlin D. Castro, Toufic Mayassi, et al.. (2013). Crystal Structure of Vδ1 T Cell Receptor in Complex with CD1d-Sulfatide Shows MHC-like Recognition of a Self-Lipid by Human γδ T Cells. Immunity. 39(6). 1032–1042. 200 indexed citations

14.

Perica, Karlo, Malarvizhi Durai, Yen‐Ling Chiu, et al.. (2013). Nanoscale artificial antigen presenting cells for T cell immunotherapy. Nanomedicine Nanotechnology Biology and Medicine. 10(1). 119–129. 119 indexed citations

15.

López‐Sagaseta, Jacinto, Leah V. Sibener, Jennifer E. Kung, Jenny E. Gumperz, & Erin J. Adams. (2012). Lysophospholipid presentation by CD1d and recognition by a human Natural Killer T‐cell receptor. The EMBO Journal. 31(8). 2047–2059. 54 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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