Mary A. Black

1.9k total citations · 1 hit paper
16 papers, 1.3k citations indexed

About

Mary A. Black is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Mary A. Black has authored 16 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Oncology, 8 papers in Molecular Biology and 8 papers in Immunology. Recurrent topics in Mary A. Black's work include CAR-T cell therapy research (15 papers), Virus-based gene therapy research (6 papers) and Immunotherapy and Immune Responses (5 papers). Mary A. Black is often cited by papers focused on CAR-T cell therapy research (15 papers), Virus-based gene therapy research (6 papers) and Immunotherapy and Immune Responses (5 papers). Mary A. Black collaborates with scholars based in United States, Malaysia and India. Mary A. Black's co-authors include Steven A. Feldman, Steven A. Rosenberg, Hui Xu, Richard A. Morgan, James N. Kochenderfer, Yangbing Zhao, Wyndham H. Wilson, Nikolaos Zacharakis, Zhili Zheng and Stephanie L. Goff and has published in prestigious journals such as Nature Medicine, Blood and Cancer Cell.

In The Last Decade

Mary A. Black

16 papers receiving 1.3k citations

Hit Papers

Immune recognition of somatic mutations leading to comple... 2018 2026 2020 2023 2018 100 200 300 400 500
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.

  1. Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer
    2018 544 citations Nikolaos Zacharakis, Harshini Chinnasamy et al. Nature Medicine profile →

Peers

Mary A. Black
Connie P.M. Duong United States
Giacomo Oliveira Italy
Zongliang Zhang China
Bruno L. Cadilha Germany
Linjie Guo United States
Lynsey M. Whilding United Kingdom
Elmer Santos United States
Sarah Tettamanti Italy
Peihua Lu China
Elizabeth L. Siegler United States
Connie P.M. Duong United States
Mary A. Black
Citations per year, relative to Mary A. Black Mary A. Black (= 1×) peers Connie P.M. Duong

Countries citing papers authored by Mary A. Black

Since Specialization
Citations

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

Fields of papers citing papers by Mary A. Black

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mary A. Black. 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 Mary A. Black. The network helps show where Mary A. Black may publish in the future.

Co-authorship network of co-authors of Mary A. Black

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

All Works

16 of 16 papers shown
1.
Song, Hannah, Michaela Procházková, Lipei Shao, et al.. (2024). CAR-T cell expansion platforms yield distinct T cell differentiation states. Cytotherapy. 26(7). 757–768. 28 indexed citations
2.
Gurusamy, Devikala, Amanda N. Henning, Tori N. Yamamoto, et al.. (2020). Multi-phenotype CRISPR-Cas9 Screen Identifies p38 Kinase as a Target for Adoptive Immunotherapies. Cancer Cell. 37(6). 818–833.e9. 116 indexed citations
3.
Arai, Yasuyuki, Uimook Choi, Sherry Koontz, et al.. (2018). Myeloid Conditioning with c-kit-Targeted CAR-T Cells Enables Donor Stem Cell Engraftment. Molecular Therapy. 26(5). 1181–1197. 37 indexed citations
4.
Xu, Hui, et al.. (2018). Continuous production process of retroviral vector for adoptive T- cell therapy. Biochemical Engineering Journal. 132. 145–151. 1 indexed citations
5.
Zacharakis, Nikolaos, Harshini Chinnasamy, Mary A. Black, et al.. (2018). Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nature Medicine. 24(6). 724–730. 544 indexed citations breakdown →
6.
Arai, Yasuyuki, Uimook Choi, Sherry Koontz, et al.. (2018). Enhanced Expression of CXCR4 Facilitates C-Kit-Targeted CAR-T Cell Trafficking to Bone Marrow and Enables Donor Stem Cell Engraftment. Biology of Blood and Marrow Transplantation. 24(3). S311–S311. 4 indexed citations
7.
Assadipour, Yasmine, Nikolaos Zacharakis, Jessica S. Crystal, et al.. (2017). Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer. Clinical Cancer Research. 23(15). 4347–4353. 28 indexed citations
8.
Klebanoff, Christopher A., Joseph G. Crompton, Anthony Leonardi, et al.. (2017). Inhibition of AKT signaling uncouples T cell differentiation from expansion for receptor-engineered adoptive immunotherapy. JCI Insight. 2(23). 160 indexed citations
9.
Yao, Xin, Yong‐Chen Lu, Linda L. Parker, et al.. (2016). Isolation and Characterization of an HLA-DPB1*04:01-restricted MAGE-A3 T-Cell Receptor for Cancer Immunotherapy. Journal of Immunotherapy. 39(5). 191–201. 27 indexed citations
10.
Feldman, Steven A., Hui Xu, Mary A. Black, et al.. (2014). Use of the piggyBac Transposon to Create Stable Packaging Cell Lines for the Production of Clinical-Grade Self-Inactivating γ-Retroviral Vectors. Human Gene Therapy Methods. 25(4). 253–260. 7 indexed citations
11.
Rosati, Shannon F., Maria R. Parkhurst, Young Ki Hong, et al.. (2014). A Novel Murine T-Cell Receptor Targeting NY-ESO-1. Journal of Immunotherapy. 37(3). 135–146. 23 indexed citations
12.
Yang, Shicheng, Stephanie L. Goff, Mary A. Black, et al.. (2012). A Simple and Effective Method to Generate Lentiviral Vectors for Ex Vivo Gene Delivery to Mature Human Peripheral Blood Lymphocytes. Human Gene Therapy Methods. 23(2). 73–83. 7 indexed citations
13.
Goff, Stephanie L., Laura A. Johnson, Mary A. Black, et al.. (2010). Enhanced receptor expression and in vitro effector function of a murine-human hybrid MART-1-reactive T cell receptor following a rapid expansion. Cancer Immunology Immunotherapy. 59(10). 1551–1560. 29 indexed citations
14.
Feldman, Steven A., Stephanie L. Goff, Hui Xu, et al.. (2010). Rapid Production of Clinical-Grade Gammaretroviral Vectors in Expanded Surface Roller Bottles Using a “Modified” Step-Filtration Process for Clearance of Packaging Cells. Human Gene Therapy. 22(1). 107–115. 13 indexed citations
15.
Kochenderfer, James N., Steven A. Feldman, Yangbing Zhao, et al.. (2009). Construction and Preclinical Evaluation of an Anti-CD19 Chimeric Antigen Receptor. Journal of Immunotherapy. 32(7). 689–702. 318 indexed citations
16.
Kochenderfer, James N., Steven A. Feldman, Yangbing Zhao, et al.. (2008). Construction and Pre-Clinical Evaluation of An Anti-CD19 Chimeric Antigen Receptor. Blood. 112(11). 4623–4623. 7 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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