Stanley Z. Lam

810 total citations · 1 hit paper
9 papers, 461 citations indexed

About

Stanley Z. Lam is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Stanley Z. Lam has authored 9 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Oncology, 6 papers in Molecular Biology and 3 papers in Immunology. Recurrent topics in Stanley Z. Lam's work include CAR-T cell therapy research (6 papers), CRISPR and Genetic Engineering (5 papers) and Immune Cell Function and Interaction (3 papers). Stanley Z. Lam is often cited by papers focused on CAR-T cell therapy research (6 papers), CRISPR and Genetic Engineering (5 papers) and Immune Cell Function and Interaction (3 papers). Stanley Z. Lam collaborates with scholars based in United States, China and Japan. Stanley Z. Lam's co-authors include Sidi Chen, Jonathan J. Park, Ryan D. Chow, Lupeng Ye, Yaying Du, Xiaoyun Dai, Matthew B. Dong, Guangchuan Wang, Youssef Errami and Paul Renauer and has published in prestigious journals such as Cell, Nature Genetics and Nature Biotechnology.

In The Last Decade

Stanley Z. Lam

9 papers receiving 452 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.

A genome-scale gain-of-function CRISPR screen in CD8 T cells identifies proline metabolism as a means to enhance CAR-T therapy

2022 143 citations Lupeng Ye, Jonathan J. Park et al. Cell Metabolism profile →

Peers

Stanley Z. Lam

ONCOL

MB

IMMUN

GENET

BE

Victoria Tobin United States

Luciana Rodrigues Carvalho Barros Brazil

Frederick Arce Vargas United Kingdom

Giorgia Rota Switzerland

Khalid Saeed Finland

Nicolas Acquavella United States

Jessica Michie Australia

Waipan Chan United States

Lemei Jia China

Martin G. Klatt United States

Victoria Tobin United States

Stanley Z. Lam

273 ×1.0

ONCOL

441 ×1.6

MB

186 ×1.0

IMMUN

131 ×1.6

GENET

48 ×1.0

BE

Citations per year, relative to Stanley Z. Lam Stanley Z. Lam (= 1×) peers Victoria Tobin

Countries citing papers authored by Stanley Z. Lam

Since Specialization

Citations

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

Fields of papers citing papers by Stanley Z. Lam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stanley Z. Lam. 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 Stanley Z. Lam. The network helps show where Stanley Z. Lam may publish in the future.

Co-authorship network of co-authors of Stanley Z. Lam

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

All Works

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9 of 9 papers shown

1.

Vos, Joris L., Stanley Z. Lam, Catherine Y. Han, et al.. (2025). Longitudinal and multisite sampling reveals mutational and copy number evolution in tumors during metastatic dissemination. Nature Genetics. 57(6). 1504–1511. 1 indexed citations

2.

Renauer, Paul, Giacomo Sferruzza, Luojia Yang, et al.. (2024). In vivo AAV–SB-CRISPR screens of tumor-infiltrating primary NK cells identify genetic checkpoints of CAR-NK therapy. Nature Biotechnology. 43(5). 752–761. 26 indexed citations

3.

Chin, Matthew G., et al.. (2024). A Culturally Specific Community Supported Agriculture (CSA) Program to Improve Diet in Immigrant Communities in Brooklyn, New York. Health Promotion Practice. 26(2). 243–248. 4 indexed citations

4.

Park, Jonathan J., et al.. (2023). Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity. Communications Biology. 6(1). 76–76. 19 indexed citations

5.

Ye, Lupeng, Stanley Z. Lam, Luojia Yang, et al.. (2023). AAV-mediated delivery of a Sleeping Beauty transposon and an mRNA-encoded transposase for the engineering of therapeutic immune cells. Nature Biomedical Engineering. 8(2). 132–148. 20 indexed citations

6.

Dai, Xiaoyun, Jonathan J. Park, Yaying Du, et al.. (2023). Massively parallel knock-in engineering of human T cells. Nature Biotechnology. 41(9). 1239–1255. 34 indexed citations

7.

Ye, Lupeng, Jonathan J. Park, Lei Peng, et al.. (2022). A genome-scale gain-of-function CRISPR screen in CD8 T cells identifies proline metabolism as a means to enhance CAR-T therapy. Cell Metabolism. 34(4). 595–614.e14. 143 indexed citations breakdown →

8.

Park, Jonathan J., et al.. (2022). Genome Engineering for Next-Generation Cellular Immunotherapies. Biochemistry. 62(24). 3455–3464. 1 indexed citations

9.

Dong, Matthew B., Guangchuan Wang, Ryan D. Chow, et al.. (2019). Systematic Immunotherapy Target Discovery Using Genome-Scale In Vivo CRISPR Screens in CD8 T Cells. Cell. 178(5). 1189–1204.e23. 213 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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