David L. Rimm

69.9k total citations · 20 hit papers
547 papers, 41.4k citations indexed

About

David L. Rimm is a scholar working on Oncology, Molecular Biology and Cancer Research. According to data from OpenAlex, David L. Rimm has authored 547 papers receiving a total of 41.4k indexed citations (citations by other indexed papers that have themselves been cited), including 331 papers in Oncology, 250 papers in Molecular Biology and 116 papers in Cancer Research. Recurrent topics in David L. Rimm's work include Cancer Immunotherapy and Biomarkers (138 papers), HER2/EGFR in Cancer Research (100 papers) and Immunotherapy and Immune Responses (61 papers). David L. Rimm is often cited by papers focused on Cancer Immunotherapy and Biomarkers (138 papers), HER2/EGFR in Cancer Research (100 papers) and Immunotherapy and Immune Responses (61 papers). David L. Rimm collaborates with scholars based in United States, Greece and Canada. David L. Rimm's co-authors include Robert L. Camp, Marisa Dolled‐Filhart, Kurt A. Schalper, Vamsidhar Velcheti, Harriet M. Kluger, Roy S. Herbst, Jon S. Morrow, Gina G. Chung, Lori A. Charette and Aaron J. Berger and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

David L. Rimm

537 papers receiving 40.7k citations

Hit Papers

X-Tile 1994 2026 2004 2015 2004 2005 2021 1994 2019 500 1000 1.5k 2.0k 2.5k
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. X-Tile
    2004 2.9k citations Robert L. Camp, Marisa Dolled‐Filhart et al. Clinical Cancer Research profile →
  2. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma
    2005 1.1k citations Aaron J. Berger, David L. Rimm et al. profile →
  3. PD-L1 as a biomarker of response to immune-checkpoint inhibitors
    2021 980 citations David L. Rimm et al. profile →
  4. Adhesion between epithelial cells and T lymphocytes mediated by E-cadherin and the αEβ7 integrin
    1994 946 citations David L. Rimm et al. profile →
  5. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
    2019 935 citations Kurt A. Schalper, David L. Rimm et al. profile →
  6. Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update
    2020 851 citations David L. Rimm et al. profile →
  7. Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non–Small-Cell Lung Cancer
    2015 679 citations Joseph McLaughlin, Kurt A. Schalper et al. profile →
  8. Validation of Tissue Microarray Technology in Breast Carcinoma
    2000 655 citations Robert L. Camp, David L. Rimm et al. profile →
  9. Programmed death ligand-1 expression in non-small cell lung cancer
    2013 652 citations Vamsidhar Velcheti, Kurt A. Schalper et al. profile →
  10. Automated subcellular localization and quantification of protein expression in tissue microarrays
    2002 645 citations Robert L. Camp, David L. Rimm et al. profile →
  11. Alpha 1(E)-catenin is an actin-binding and -bundling protein mediating the attachment of F-actin to the membrane adhesion complex.
    1995 629 citations David L. Rimm et al. profile →
  12. Molecular Classification Identifies a Subset of Human Papillomavirus–Associated Oropharyngeal Cancers With Favorable Prognosis
    2006 618 citations Malini Harigopal, Robert L. Camp et al. profile →
  13. A Prospective, Multi-institutional, Pathologist-Based Assessment of 4 Immunohistochemistry Assays for PD-L1 Expression in Non–Small Cell Lung Cancer
    2017 576 citations David L. Rimm, Robert Homer et al. profile →
  14. Siglec-15 as an immune suppressor and potential target for normalization cancer immunotherapy
    2019 488 citations Maria Toki, Roy S. Herbst et al. profile →
  15. Immunotherapy in Non–Small Cell Lung Cancer: Facts and Hopes
    2019 470 citations Miguel F. Sanmamed, David L. Rimm et al. Clinical Cancer Research profile →
  16. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade
    2019 405 citations David L. Rimm, Kurt A. Schalper et al. profile →
  17. In Situ Tumor PD-L1 mRNA Expression Is Associated with Increased TILs and Better Outcome in Breast Carcinomas
    2014 367 citations Kurt A. Schalper, Vamsidhar Velcheti et al. Clinical Cancer Research profile →
  18. A proposal for validation of antibodies
    2016 357 citations David L. Rimm et al. profile →
  19. PD-L1 Expression in Lung Cancer
    2016 336 citations David L. Rimm et al. profile →
  20. Biological and clinical significance of tumour-infiltrating lymphocytes in the era of immunotherapy: a multidimensional approach
    2025 21 citations Miguel F. Sanmamed, Lieping Chen et al. profile →

Peers

David L. Rimm
Kenneth J. Pienta United States
Roger E. McLendon United States
Ralph R. Weichselbaum United States
Daniel J. Hicklin United States
Ignacio I. Wistuba United States
Robert S. Kerbel Canada
Guido Sauter Germany
Charles Swanton United Kingdom
Ming‐Sound Tsao Canada
Isaiah J. Fidler United States
Kenneth J. Pienta United States
David L. Rimm
Citations per year, relative to David L. Rimm David L. Rimm (= 1×) peers Kenneth J. Pienta

Countries citing papers authored by David L. Rimm

Since Specialization
Citations

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

Fields of papers citing papers by David L. Rimm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David L. Rimm. 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 David L. Rimm. The network helps show where David L. Rimm may publish in the future.

Co-authorship network of co-authors of David L. Rimm

This figure shows the co-authorship network connecting the top 25 collaborators of David L. Rimm. A scholar is included among the top collaborators of David L. Rimm 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 David L. Rimm. David L. Rimm 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.
Aung, Thazin Nwe, Jonathan Warrell, Sandra Martínez-Morilla, et al.. (2024). Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma. Clinical Cancer Research. 30(16). 3520–3532. 6 indexed citations
2.
Bai, Yalai, Kimberly Cole, Sandra Martínez-Morilla, et al.. (2021). An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer. Clinical Cancer Research. 27(20). 5557–5565. 38 indexed citations
3.
Muthusamy, Viswanathan, et al.. (2021). Targeting Pyruvate Kinase M2 Phosphorylation Reverses Aggressive Cancer Phenotypes. Cancer Research. 81(16). 4346–4359. 41 indexed citations
4.
Ahmed, Fahad Shabbir, Patricia Gaule, John J. McGuire, et al.. (2020). PD-L1 Protein Expression on Both Tumor Cells and Macrophages are Associated with Response to Neoadjuvant Durvalumab with Chemotherapy in Triple-negative Breast Cancer. Clinical Cancer Research. 26(20). 5456–5461. 67 indexed citations
5.
Liu, Yuting, Jon Zugazagoitia, Fahad Shabbir Ahmed, et al.. (2019). Immune Cell PD-L1 Colocalizes with Macrophages and Is Associated with Outcome in PD-1 Pathway Blockade Therapy. Clinical Cancer Research. 26(4). 970–977. 225 indexed citations
6.
Toki, Maria, Christopher R. B. Merritt, Pok Fai Wong, et al.. (2019). High-Plex Predictive Marker Discovery for Melanoma Immunotherapy–Treated Patients Using Digital Spatial Profiling. Clinical Cancer Research. 25(18). 5503–5512. 111 indexed citations
7.
Wong, Pok Fai, Wei Wei, James W. Smithy, et al.. (2019). Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma. Clinical Cancer Research. 25(8). 2442–2449. 103 indexed citations
8.
Carvajal‐Hausdorf, Daniel, Kelly Stanton, Franz Villarroel‐Espíndola, et al.. (2019). Multiplexed (18-Plex) Measurement of Signaling Targets and Cytotoxic T Cells in Trastuzumab-Treated Patients using Imaging Mass Cytometry. Clinical Cancer Research. 25(10). 3054–3062. 40 indexed citations
9.
Corredor, Germán, Xiangxue Wang, Yu Zhou, et al.. (2018). Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer. Clinical Cancer Research. 25(5). 1526–1534. 162 indexed citations
10.
Bellone, Stefania, Natália Buza, Jungmin Choi, et al.. (2018). Exceptional Response to Pembrolizumab in a Metastatic, Chemotherapy/Radiation-Resistant Ovarian Cancer Patient Harboring a PD-L1-Genetic Rearrangement. Clinical Cancer Research. 24(14). 3282–3291. 35 indexed citations
11.
Kluger, Harriet M., Christopher R. Zito, Gabriela Turcu, et al.. (2017). PD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors. Clinical Cancer Research. 23(15). 4270–4279. 111 indexed citations
12.
Schalper, Kurt A., Daniel Carvajal‐Hausdorf, Joseph McLaughlin, et al.. (2016). Differential Expression and Significance of PD-L1, IDO-1, and B7-H4 in Human Lung Cancer. Clinical Cancer Research. 23(2). 370–378. 160 indexed citations
13.
Wilson, Melissa, Fengmin Zhao, Sanika Khare, et al.. (2015). Copy Number Changes Are Associated with Response to Treatment with Carboplatin, Paclitaxel, and Sorafenib in Melanoma. Clinical Cancer Research. 22(2). 374–382. 31 indexed citations
14.
Jilaveanu, Lucia B., Fabio Parisi, Meaghan L. Barr, et al.. (2014). PLEKHA5 as a Biomarker and Potential Mediator of Melanoma Brain Metastasis. Clinical Cancer Research. 21(9). 2138–2147. 57 indexed citations
15.
Brown, Jason R., Hallie Wimberly, Donald R. Lannin, et al.. (2014). Multiplexed Quantitative Analysis of CD3, CD8, and CD20 Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer. Clinical Cancer Research. 20(23). 5995–6005. 129 indexed citations
16.
Aziz, Saadia A., Lucia B. Jilaveanu, Christopher R. Zito, et al.. (2010). Vertical Targeting of the Phosphatidylinositol-3 Kinase Pathway as a Strategy for Treating Melanoma. Clinical Cancer Research. 16(24). 6029–6039. 69 indexed citations
17.
Anagnostou, Valsamo, Gerold Bepler, Konstantinos N. Syrigos, et al.. (2009). High Expression of Mammalian Target of Rapamycin Is Associated with Better Outcome for Patients with Early Stage Lung Adenocarcinoma. Clinical Cancer Research. 15(12). 4157–4164. 29 indexed citations
18.
Liu, Jiang, Murad Ghanim, Lei Xue, et al.. (2009). Analysis of Drosophila Segmentation Network Identifies a JNK Pathway Factor Overexpressed in Kidney Cancer. Science. 323(5918). 1218–1222. 98 indexed citations
19.
Dolled‐Filhart, Marisa, Lisa Rydén, Melissa A. Cregger, et al.. (2006). Classification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays. Clinical Cancer Research. 12(21). 6459–6468. 90 indexed citations
20.
Hoek, Keith S., David L. Rimm, Kenneth R. Williams, et al.. (2004). Expression Profiling Reveals Novel Pathways in the Transformation of Melanocytes to Melanomas. Cancer Research. 64(15). 5270–5282. 371 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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