Ayse U. Akarca

8.6k total citations
34 papers, 773 citations indexed

About

Ayse U. Akarca is a scholar working on Oncology, Immunology and Pathology and Forensic Medicine. According to data from OpenAlex, Ayse U. Akarca has authored 34 papers receiving a total of 773 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Oncology, 12 papers in Immunology and 11 papers in Pathology and Forensic Medicine. Recurrent topics in Ayse U. Akarca's work include Cancer Immunotherapy and Biomarkers (10 papers), Lymphoma Diagnosis and Treatment (10 papers) and CAR-T cell therapy research (6 papers). Ayse U. Akarca is often cited by papers focused on Cancer Immunotherapy and Biomarkers (10 papers), Lymphoma Diagnosis and Treatment (10 papers) and CAR-T cell therapy research (6 papers). Ayse U. Akarca collaborates with scholars based in United Kingdom, Italy and United States. Ayse U. Akarca's co-authors include Teresa Marafioti, David C. Linch, Giuseppe Gritti, Karl S. Peggs, Brian Philip, Patrycja Wawrzyniecka, Mateusz Legut, Joan Somja, David K. Cole and Miguel Á. Piris and has published in prestigious journals such as Nature Medicine, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Ayse U. Akarca

33 papers receiving 758 citations

Peers

Ayse U. Akarca
Ayse U. Akarca
Citations per year, relative to Ayse U. Akarca Ayse U. Akarca (= 1×) peers Katharina Balschun

Countries citing papers authored by Ayse U. Akarca

Since Specialization
Citations

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

Fields of papers citing papers by Ayse U. Akarca

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ayse U. Akarca. 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 Ayse U. Akarca. The network helps show where Ayse U. Akarca may publish in the future.

Co-authorship network of co-authors of Ayse U. Akarca

This figure shows the co-authorship network connecting the top 25 collaborators of Ayse U. Akarca. A scholar is included among the top collaborators of Ayse U. Akarca 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 Ayse U. Akarca. Ayse U. Akarca 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.
Dey, Aditi, Ayse U. Akarca, David Auty, et al.. (2024). Mitochondrial dsRNA from B-ALL cells stimulates mesenchymal stromal cells to become cancer-associated fibroblasts. Blood Advances. 8(21). 5696–5709. 2 indexed citations
2.
Zhang, Hanyun, Khalid AbdulJabbar, Ayse U. Akarca, et al.. (2023). Self-supervised deep learning for highly efficient spatial immunophenotyping. EBioMedicine. 95. 104769–104769. 4 indexed citations
3.
Zhang, Hanyun, Khalid AbdulJabbar, David A. Moore, et al.. (2023). Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma. Cancer Research. 83(9). 1410–1425. 22 indexed citations
4.
Pearce, David R., Ayse U. Akarca, Roel P. H. De Maeyer, et al.. (2023). Phenotyping of lymphoproliferative tumours generated in xenografts of non-small cell lung cancer. Frontiers in Oncology. 13. 1156743–1156743. 5 indexed citations
5.
Wong, Yien Ning Sophia, Ayse U. Akarca, Pawan Dhami, et al.. (2022). Systematic Evaluation of the Immune Environment of Small Intestinal Neuroendocrine Tumors. Clinical Cancer Research. 28(12). 2657–2668. 7 indexed citations
6.
Hynds, Robert E., Ariana Huebner, Daniel J. Pearce, et al.. (2022). 3MO Genomic evolution of non-small cell lung cancer during the establishment and propagation of patient-derived xenograft models. Annals of Oncology. 33. S1384–S1384. 2 indexed citations
7.
Akarca, Ayse U., Riccardo L. Rossi, Sabine Pomplun, et al.. (2022). High inter‐follicular spatial co‐localization of CD8+FOXP3+ with CD4+CD8+ cells predicts favorable outcome in follicular lymphoma. Hematological Oncology. 40(4). 541–553. 8 indexed citations
8.
Pollara, Gabriele, Carolin T. Turner, Joshua Rosenheim, et al.. (2021). Exaggerated IL-17A activity in human in vivo recall responses discriminates active tuberculosis from latent infection and cured disease. Science Translational Medicine. 13(592). 31 indexed citations
9.
Natoli, Marina, John Gallon, Haonan Lu, et al.. (2021). Transcriptional analysis of multiple ovarian cancer cohorts reveals prognostic and immunomodulatory consequences of ERV expression. Journal for ImmunoTherapy of Cancer. 9(1). e001519–e001519. 16 indexed citations
10.
Pinato, David J., Joanne Evans, Hua Zhang, et al.. (2020). Programmed Cell Death Ligand Expression Drives Immune Tolerogenesis across the Diverse Subtypes of Neuroendocrine Tumours. Neuroendocrinology. 111(5). 465–474. 14 indexed citations
11.
Granai, Massimo, Lucia Mundo, Ayse U. Akarca, et al.. (2020). Immune landscape in Burkitt lymphoma reveals M2-macrophage polarization and correlation between PD-L1 expression and non-canonical EBV latency program. Infectious Agents and Cancer. 15(1). 28–28. 29 indexed citations
12.
Pinato, David J., Francesco Mauri, Paolo Spina, et al.. (2019). Clinical implications of heterogeneity in PD-L1 immunohistochemical detection in hepatocellular carcinoma: the Blueprint-HCC study. British Journal of Cancer. 120(11). 1033–1036. 71 indexed citations
13.
Rizzo, Francesca, Teresa Marafioti, Mohid S. Khan, et al.. (2019). Circulating tumour cells and their association with bone metastases in patients with neuroendocrine tumours. British Journal of Cancer. 120(3). 294–300. 29 indexed citations
14.
Agostinelli, Claudio, Ayse U. Akarca, Alan G. Ramsay, et al.. (2019). Novel markers in pediatric-type follicular lymphoma. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 475(6). 771–779. 28 indexed citations
15.
Granai, Massimo, Maria Raffaella Ambrosio, Ayse U. Akarca, et al.. (2019). Role of Epstein-Barr virus in transformation of follicular lymphoma to diffuse large B-cell lymphoma: a case report and review of the literature. Haematologica. 104(6). e269–e273. 10 indexed citations
16.
Bello, Giuseppe, Ayse U. Akarca, Maria Raffaella Ambrosio, et al.. (2018). Granulysin, a novel marker for extranodal NK/T cell lymphoma, nasal type. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 473(6). 749–757. 4 indexed citations
17.
Taraborrelli, Lucia, Nieves Peltzer, Antonella Montinaro, et al.. (2018). LUBAC prevents lethal dermatitis by inhibiting cell death induced by TNF, TRAIL and CD95L. Nature Communications. 9(1). 3910–3910. 80 indexed citations
18.
Maciocia, Paul, Patrycja Wawrzyniecka, Brian Philip, et al.. (2017). Targeting the T cell receptor β-chain constant region for immunotherapy of T cell malignancies. Nature Medicine. 23(12). 1416–1423. 192 indexed citations
19.
Flynn, Michael, Francesca Zammarchi, Peter Tyrer, et al.. (2016). ADCT-301, a Pyrrolobenzodiazepine (PBD) Dimer–Containing Antibody–Drug Conjugate (ADC) Targeting CD25-Expressing Hematological Malignancies. Molecular Cancer Therapeutics. 15(11). 2709–2721. 85 indexed citations
20.
Agostinelli, Claudio, Hasan Rizvi, Jennifer C. Paterson, et al.. (2014). Intracellular TCR-signaling Pathway. The American Journal of Surgical Pathology. 38(10). 1349–1359. 16 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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