David S. Shames

19.3k total citations · 1 hit paper
110 papers, 6.1k citations indexed

About

David S. Shames is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, David S. Shames has authored 110 papers receiving a total of 6.1k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Oncology, 53 papers in Pulmonary and Respiratory Medicine and 40 papers in Cancer Research. Recurrent topics in David S. Shames's work include Lung Cancer Treatments and Mutations (44 papers), Cancer Immunotherapy and Biomarkers (37 papers) and Cancer Genomics and Diagnostics (36 papers). David S. Shames is often cited by papers focused on Lung Cancer Treatments and Mutations (44 papers), Cancer Immunotherapy and Biomarkers (37 papers) and Cancer Genomics and Diagnostics (36 papers). David S. Shames collaborates with scholars based in United States, China and Switzerland. David S. Shames's co-authors include John D. Minna, Adi F. Gazdar, Mitsuo Sato, Tony Mok, Ignacio I. Wistuba, Noriaki Sunaga, Luc Girard, Rosalyn Ram, David R. Corey and Bethany A. Janowski and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Nature Communications.

In The Last Decade

David S. Shames

106 papers receiving 6.0k 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.

Detection and Dynamic Changes of EGFR Mutations from Circulating Tumor DNA as a Predictor of Survival Outcomes in NSCLC Patients Treated with First-line Intercalated Erlotinib and Chemotherapy

2015 367 citations Tony Mok, Yi‐Long Wu et al. Clinical Cancer Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David S. Shames United States	45	3.0k	2.9k	2.4k	1.9k	522	110	6.1k
Fiamma Buttitta Italy	45	3.6k 1.2×	3.5k 1.2×	2.9k 1.2×	2.4k 1.3×	368 0.7×	102	7.1k
Paul K. Paik United States	31	2.2k 0.7×	3.0k 1.0×	3.5k 1.4×	1.1k 0.6×	471 0.9×	155	5.6k
Jianping Xiong China	36	2.1k 0.7×	2.0k 0.7×	1.6k 0.7×	1.3k 0.7×	486 0.9×	189	5.0k
Antonella De Luca Italy	38	3.6k 1.2×	3.4k 1.2×	1.7k 0.7×	1.4k 0.7×	509 1.0×	102	6.8k
Niels Reinmuth Germany	43	2.9k 1.0×	5.0k 1.7×	2.9k 1.2×	1.5k 0.8×	851 1.6×	196	7.9k
Viviana Bazan Italy	42	2.7k 0.9×	3.1k 1.1×	1.4k 0.6×	2.0k 1.0×	598 1.1×	191	6.3k
Daniele Generali Italy	42	2.5k 0.8×	3.1k 1.1×	1.7k 0.7×	2.1k 1.1×	475 0.9×	232	6.2k
Bijoyesh Mookerjee United States	30	4.1k 1.4×	3.7k 1.3×	2.2k 0.9×	2.3k 1.2×	687 1.3×	85	7.4k
De‐Chen Lin United States	35	4.3k 1.4×	2.1k 0.7×	2.2k 0.9×	2.7k 1.4×	824 1.6×	92	6.8k
Katsuya Tsuchihara Japan	38	2.9k 1.0×	2.1k 0.7×	1.3k 0.5×	1.6k 0.8×	319 0.6×	148	5.1k

Countries citing papers authored by David S. Shames

Since Specialization

Citations

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

Fields of papers citing papers by David S. Shames

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David S. Shames

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

All Works

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20 of 20 papers shown

Madison, Russell W., Zoe J. Assaf, Alexander D. Fine, et al.. (2025). Real-World Validity of Tissue-Agnostic Circulating Tumor DNA Response Monitoring in Lung Cancers Treated With Chemotherapy, Immunotherapy, or Targeted Agents. JTO Clinical and Research Reports. 6(9). 100829–100829. 1 indexed citations

Zou, Wei, Vinzent Rolny, Martin Reck, et al.. (2023). Combined use of CYFRA 21-1 and CA 125 predicts survival of patients with metastatic NSCLC and stable disease in IMpower150. Tumor Biology. 46(s1). S177–S190. 2 indexed citations

Pellini, Bruna, Russell W. Madison, Ole Gjoerup, et al.. (2023). Circulating Tumor DNA Monitoring on Chemo-immunotherapy for Risk Stratification in Advanced Non–Small Cell Lung Cancer. Clinical Cancer Research. 29(22). 4596–4605. 25 indexed citations

Friedman, Claire F., John D. Hainsworth, Razelle Kurzrock, et al.. (2021). Atezolizumab Treatment of Tumors with High Tumor Mutational Burden from MyPathway, a Multicenter, Open-Label, Phase IIa Multiple Basket Study. Cancer Discovery. 12(3). 654–669. 56 indexed citations

Zou, Wei, Stephanie J. Yaung, Marcus Ballinger, et al.. (2021). ctDNA Predicts Overall Survival in Patients With NSCLC Treated With PD-L1 Blockade or With Chemotherapy. JCO Precision Oncology. 5(5). 827–838. 35 indexed citations

Wu, Yi‐Long, Victor Lee, Chong Kin Liam, et al.. (2018). Clinical utility of a blood-based EGFR mutation test in patients receiving first-line erlotinib therapy in the ENSURE, FASTACT-2, and ASPIRATION studies. Lung Cancer. 126. 1–8. 35 indexed citations

Spigel, David R., Martin J. Edelman, Kenneth J. O’Byrne, et al.. (2017). Results From the Phase III Randomized Trial of Onartuzumab Plus Erlotinib Versus Erlotinib in Previously Treated Stage IIIB or IV Non–Small-Cell Lung Cancer: METLung. Journal of Clinical Oncology. 35(4). 412–420. 232 indexed citations

Spigel, David R., Martin J. Edelman, Kenneth J. O’Byrne, et al.. (2017). Results from the phase iii randomized trial of onartuzumab plus erlotinib versus erlotinib in previously treated stage iiib or iv non–small-cell lung cancer: METLung. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 1 indexed citations

Mok, Tony, Yi‐Long Wu, Jin Soo Lee, et al.. (2015). Detection and Dynamic Changes of EGFR Mutations from Circulating Tumor DNA as a Predictor of Survival Outcomes in NSCLC Patients Treated with First-line Intercalated Erlotinib and Chemotherapy. Clinical Cancer Research. 21(14). 3196–3203. 367 indexed citations breakdown →

10.

Sato, Mitsuo, Jill E. Larsen, Woochang Lee, et al.. (2013). Human Lung Epithelial Cells Progressed to Malignancy through Specific Oncogenic Manipulations. Molecular Cancer Research. 11(6). 638–650. 156 indexed citations

11.

Shames, David S., Kristi Elkins, Kimberly Walter, et al.. (2013). Loss of NAPRT1 Expression by Tumor-Specific Promoter Methylation Provides a Novel Predictive Biomarker for NAMPT Inhibitors. Clinical Cancer Research. 19(24). 6912–6923. 59 indexed citations

12.

Zhang, Wei, Junichi Soh, Victor Stastny, et al.. (2013). CDKN2A/p16 Inactivation Mechanisms and Their Relationship to Smoke Exposure and Molecular Features in Non–Small-Cell Lung Cancer. Journal of Thoracic Oncology. 8(11). 1378–1388. 74 indexed citations

13.

McCleland, Mark L., Adam S. Adler, Ely Cosino, et al.. (2012). Lactate Dehydrogenase B Is Required for the Growth of KRAS-Dependent Lung Adenocarcinomas. Clinical Cancer Research. 19(4). 773–784. 93 indexed citations

14.

Kim, Walter, Thomas Holcomb, Tom Januario, et al.. (2012). DNA Methylation Profiling Defines Clinically Relevant Biological Subsets of Non–Small Cell Lung Cancer. Clinical Cancer Research. 18(8). 2360–2373. 66 indexed citations

15.

Spoerke, Jill M., Carol O’Brien, Ling Huw, et al.. (2012). Phosphoinositide 3-Kinase (PI3K) Pathway Alterations Are Associated with Histologic Subtypes and Are Predictive of Sensitivity to PI3K Inhibitors in Lung Cancer Preclinical Models. Clinical Cancer Research. 18(24). 6771–6783. 149 indexed citations

16.

Sunaga, Noriaki, David S. Shames, Luc Girard, et al.. (2011). Knockdown of Oncogenic KRAS in Non–Small Cell Lung Cancers Suppresses Tumor Growth and Sensitizes Tumor Cells to Targeted Therapy. Molecular Cancer Therapeutics. 10(2). 336–346. 135 indexed citations

17.

Cai, Di, David S. Shames, Maria Gabriela Raso, et al.. (2010). Steroid Receptor Coactivator-3 Expression in Lung Cancer and Its Role in the Regulation of Cancer Cell Survival and Proliferation. Cancer Research. 70(16). 6477–6485. 48 indexed citations

18.

Soh, Junichi, Naoki Okumura, William W. Lockwood, et al.. (2009). Oncogene Mutations, Copy Number Gains and Mutant Allele Specific Imbalance (MASI) Frequently Occur Together in Tumor Cells. PLoS ONE. 4(10). e7464–e7464. 179 indexed citations

19.

Dineen, Seán, Kristi D. Lynn, Shane E. Holloway, et al.. (2008). Vascular Endothelial Growth Factor Receptor 2 Mediates Macrophage Infiltration into Orthotopic Pancreatic Tumors in Mice. Cancer Research. 68(11). 4340–4346. 161 indexed citations

20.

Gao, Boning, Xian‐Jin Xie, David S. Shames, et al.. (2008). RASSF1A Polymorphism A133S Is Associated with Early Onset Breast Cancer in BRCA1/2 Mutation Carriers. Cancer Research. 68(1). 22–25. 40 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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