Circulating tumor DNA in lung cancer: a bibliometric analysis of studies published from 2001 to 2024

Introduction

In 2022, there were 2,480,301 new lung cancer cases diagnosed worldwide, accounting for 12.4% of the total global cancer burden, and there were 1,817,172 lung cancer-related deaths, accounting for 18.7% of the total cancer deaths, making this disease the leading cause of cancer incidence and mortality in the world (1). In the majority of countries, the 5-year survival rate of lung cancer is less than 20%, representing a significant health risk (1).

Circulating tumor DNA (ctDNA) is composed of short fragments [approximately 130–150 (2,3), with typically <145 base pairs (4)] of cell-free DNA (cfDNA) released into the systemic circulation from the tumor [i.e., primary tumor, metastasis, or circulating tumor cells (CTCs)] through cellular apoptosis, necrosis, or secretion (5-7). Numerous studies have indicated that ctDNA hold diagnostic, predictive, and prognostic value in various diseases, including lung cancer (8).

Given the proliferation of ctDNA-related studies in lung cancer from bench to bedside over the past decades, we sought to analyze the entire field of ctDNA in lung cancer. Bibliometric analysis (9) has been widely used in cancer research, but to the best of our knowledge, no systematic bibliometric analysis of studies in the field published from 2001 to 2024 has been conducted. To address this deficiency, we retrieved the relevant literature from the Web of Science Core Collection (WoSCC) (9), which served as input for CiteSpace (10) and VOSviewer (11) software in completing descriptive statistics. Basic publication information and the current status of research were visualized through objective measurements, including numbers of annual publications; number of citations; participation, contribution, and collaboration of authors, institutions, journals, and countries/regions; and keywords, hotspots, and frontiers. In addition, visualization analysis was performed on the concurrence of different keywords and their appearance frequency. Keywords were also distributed through the timeline to identify the widely noted and unresolved subjects. Based on the statistical results, the Discussion section mainly focuses on the keywords, hotspots, and research trends of the publications in this field.

Our study provides an overview of the research landscape in ctDNA and lung cancer that has evolved over the past two decades, with an emphasis on hotspot and frontiers, and may serve as a convenient reference in facilitating further research in this field.

Methods

Search strategy for data collection

Literature on ctDNA and lung cancer was identified and collected from the WoSCC, specifically the Science Citation Index (SCI) and Social Science Citation Index (SSCI), with the following search term strategy: TS = ((((“Circulating Tumor DNA”) OR (“Cell-Free Tumor DNA”) OR (“ctDNA”))) AND TI = (((carcino*) OR (cancer) OR (tumor) OR (neopls*) OR (malignancy) OR (malignancies) OR (oncology)) AND ((lung) OR (pulmonary) OR (pneumatic) OR (NSCLC) OR (SCCL) OR (SCLC) OR (bronchogenic) OR (bronchus) OR (Bronchi)))). The data collection date was May 4, 2024, and the publication language was limited to English. A total of 1,478 records were obtained, published from January 1, 2001, to May 4, 2024. Of these, 716 articles appeared in the category of original research papers, 447 in meeting abstracts, 262 in review articles, 32 in editorial materials, 13 in online publications, 11 in letters, 9 in revisions, 6 in proceedings, 1 in news, and 1 in retracted publications. In total, 1478 records garnered a total of 29,813 citations from other publications. Of these records, 24,309 were cited articles, meaning they had received at least one citation each. Moreover, 15,563 citing articles from other publications had referenced these 1,478 records. An h-index of 80 indicated that 80 articles within the dataset had each been cited at least 80 times, reflecting both productivity and citation impact. Furthermore, the average number of citations per item was 20.17, which we calculated by dividing the total times cited [29,813] by the total number of records [1,478]. Only original research papers and review articles were included in our analysis, with 978 documents ultimately being included. Among them, no repeat records were identified by CiteSpace. Thus, all 978 of these records were exported in their form with cited references and saved as plain text files in TXT format.

Data analysis

Publications related to ctDNA and lung cancer were initially retrieved from WoSCC database. Microsoft Office Excel (Microsoft Corp., Redmond, WA, USA), VOSviewer (version 1.6.19.0), CiteSpace (version 6.3.R1), and the “Bibliometrix” package in R (The R Foundation for Statistical Computing, Vienna, Austria) were used for the analysis of all 978 documents. Figure 1 shows the flowchart of the study, including the search strategy and analysis process.

Figure 1 The flowchart illustrating the screening strategy and analysis process of literature on ctDNA in lung cancer. *, truncation symbol. ctDNA, circulating tumor DNA; NSCLC, non-small cell lung cancer; SCLC, small-cell lung cancer; SCCL, squamous cell carcinoma of the lung; SCI, Science Citation Index; SSCI, Social Science Citation Index.

Results

Temporal distribution of the publications

The number of annual publications over a period of time can reflect the degree of interest and research trends in a field. As depicted in Figure 2, from 2001 to 2014, the number of annual publications was fewer than five. In 2015, there appears to have been a breakthrough, and since then, the number of articles published has been steadily increasing each year, indicating that research on ctDNA and lung cancer is growing.

Figure 2 Trends in the growth of publications in the field of ctDNA and lung cancer. ctDNA, circulating tumor DNA.

Distribution of countries/regions and research institutions

A total of 58 countries/regions have contributed publications to this field, among which 30 met the VOSviewer threshold of 5 minimum documents and were included in the analysis of publication numbers and collaboration networks of countries/regions. Based on coauthorship analysis, VOSviewer was used to visualize the distributions of publications among countries/regions and their collaboration. In Figure 3A, different colors depict the cooperation clusters of countries/regions, and the thickness of lines represents the number of connections between nodes. Close cooperation took place between Brazil, Canada, China, Czech Republic, France, Greece, Italy, Poland, Portugal, Russia, Singapore, and South Korea. The Netherlands closely cooperated with Denmark, Hungary, Norway, Scotland, and Sweden. Australia, Belgium, Japan, Spain, Switzerland, and Thailand also formed a close collaborative cluster. Moreover, England, Germany, India, and the United States cooperated closely with one another (Figure 3A).

Figure 3 The cooperation network of countries/regions or institutions in ctDNA and lung cancer. (A,B) Cooperation network of countries/regions from VOSviewer. (C) Cooperation network of countries/regions from CiteSpace. (D) Cooperation network of institutions from VOSviewer. ctDNA, circulating tumor DNA.

Similar to Figure 3A, in Figure 3B, the size of the node represents the number of publications, but in Figure 3B, the color of each node indicates the average date of publications in the given country/region. Thus, it can be seen that the United States, Japan, Spain, Australia, Italy, France, and England were among the first to enter the field.

The CiteSpace parameters were as follows: time slice, 2001–2024; years per slice, 1; term source, entire selection; node type, country/region, k=25, link retaining factor =3.0, maximum links per node =10, latest boundary year =5, and edge weight threshold for top N (e) =1.0. In Figure 3C, the color of the inner rings in each node changes from purple to yellow, indicating a publication date from 2001 to 2024 (N=57; E=389; density =0.2437). The top 10 most productive countries/regions are listed in Table 1. China had the largest number of publications (n=388), followed by the United States (n=279) and Italy (n=97), accounting for 39.673%, 28.528%, and 9.918% of the total number documents included in this study, respectively, and more than half of the total reports collectively. The pink circle nodes in Figure 3C represent countries with high centrality, including the United States, Australia, South Korea, Germany, and Denmark. Studies from England (74.06), Germany (67.54), and the United States (54.37) had the highest average citations per article, while the centrality of the United States (0.23), Germany (0.2), and Italy (0.1) was greater than or equal to 0.1, indicating that these countries have played a leading role in this field (Table 1).

A total of 2008 organizations contributed to the 978 documents analyzed in our study. There were nine institutions whose centrality was greater than 0.10, including, the University of Texas MD Anderson Cancer Center (centrality =0.27), Roche Holding (centrality =0.26), AstraZeneca (centrality =0.25), the Guangdong Academy of Medical Sciences & Guangdong General Hospital (centrality =0.23), the Chinese University of Hong Kong (centrality =0.22), Guardant Health (centrality =0.14), Peking University (centrality =0.14), Harvard University (centrality =0.13), and the University of California (centrality =0.13). The VOSviewer threshold was set to 15, which screened out 25 organizations for the analysis of institution collaborations (Figure 3D). VOSviewer grouped these institutions into three clusters, with close cooperation among the constituent institutions of each cluster. In Figure 3D, the red cluster includes AstraZeneca, the Chinese University of Hong Kong, The German Center for Lung Research (DZL), Guardant Health Inc., Harvard Medical School, Kindai University, Massachusetts General Hospital, Memorial Sloan Kettering Cancer Center, National Cancer Centre Singapore, Stanford University, Sungkyunkwan University, the University of Texas MD Anderson Cancer Center, and Washington University; meanwhile, the green cluster includes Burning Rock Biotech, the Chinese Academy of Medical Sciences, Peking Union Medical College, Fudan University, Nanjing Genepioneer Biotechnologies Inc., Nanjing Medical University, Peking University, Shanghai Jiao Tong University, Sichuan University, Sun Yat-sen University, and Tongji University. The Guangdong Academy of Medical Sciences is colored in blue, indicating collaborations with both other clusters (Figure 3D).

As shown in Table 2, the top five most productive institutions were Shanghai Jiao Tong University (n=29), the University of Texas MD (n=28), Tongji University (n=27), Peking University (n=25), and AstraZeneca (n=23), respectively. Among the top 10 institutions with the highest number of publications, AstraZeneca had the highest number of average citations per item (76.57), followed by the University of Texas MD Anderson Cancer Center (69.04) and Guardant Health Inc. (67.82) (Table 2).

Collaborations and cocitations among authors

VOSviewer parameters included a Linlog/modularity method and a minimum number of eight documents per author. The initially retrieved results included 6,656 authors, and 28 met the threshold for analysis of coauthorship. Among them, 20 authors had close collaborations. In Figure 4A, each node represents one author, the size of the circle indicates the number of articles published by the author, and the line connecting the nodes represents the co-occurrence relationship between authors. Authors were classified by VOSviewer into different clusters, and the nodes with same color represent the cooperation among authors. There were six clusters in total: (I) the red cluster included Myung-Ju Ahn, Suresh S Ramalingam, Yi-Long Wu, Caicun Zhou, and Jie Wang, who closely cooperated and mainly focused on epidermal growth factor receptor (EGFR)-mutated lung cancer; (II) the cyan cluster included Niki Karachaliou who closely cooperated with Rafael Rosell in lung cancer treatment studies; (III) the green cluster included Richard B. Lanman and Victoria M. Raymond, who closely cooperated, with their focus being on the dynamics and utility of ctDNA, and also included Mariano Provencio and Richard B. Lanman, who collaborated on a single study with one another; (IV) the blue cluster included Nicola Normanno and Ed Schuuring who closely cooperated in lung cancer studies, Harry J. M. Groen and Ed Schuuring who examined liquid biopsy markers including ctDNA, and Nicola Normanno and Martin Reck who published several articles focusing on non-small cell lung cancer (NSCLC); (V) the yellow cluster included Umberto Malapelle and Christian Rolfo who closely collaborated on lung cancer studies with the participation of Nir Peled and David Gandara, as well as Nir Peled and David Gandara, who demonstrated close cooperation; and (VI) the purple cluster included Fumio Imamura frequently cooperated with Toru Kumagai in various aspects of lung cancer research.

Figure 4 VOSviewer networks of authors in the field of ctDNA and lung cancer. (A) Cooperation network of authors from VOSviewer. (B) Cocitation author network from VOSviewer. ctDNA, circulating tumor DNA.

According to the number of publications of ctDNA and lung cancer, Richard B. Lanman (Medical Affairs, Guardant Health, Inc., Redwood City, USA) was the author who contributed to the largest number of studies in this field (n=16), followed by Yi-Long Wu (Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China) (n=15), and Yang Fan (Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, China) (n=14) (Table 3). As a corresponding author, Yi-Long Wu published articles on ctDNA analysis in immunotherapy treatment and research articles/reviews on EGFR and MET mutations.

In the co-citation analysis, we found that Tony S. K. Mok ( Department of Clinical Oncology, The Chinese University of Hong Kong, Hong Kong, China) (n=349), Geoffrey R. Oxnard (Foundation Medicine, Inc., Cambridge, USA) (n=300), and Aaron M. Newman (the Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, USA) (n=247) were frequently cited together with papers written by the most prolific authors in the field of ctDNA and lung cancer (Table 3), suggesting that their studies occupied a foundational position in this field and are essential to contextualizing and interpreting the predictive and prognostic use of ctDNA for lung cancer.

There was a total of 14,053 cocitations for the included literature. VOSviewer threshold was set to 150, and 19 authors met the threshold for cocitation network analysis. In Figure 4B, the cocitation relationship is indicated by the line connecting the nodes. The nodes are colored in red and green, indicating the formation of two clusters: (I) the red cluster includes Christopher Abbosh, Chetan Bettegowda, Aadel A. Chaudhuri, Luis A. Diaz Jr, Frank Diehl, Ahmedin Jemal, Aaron M. Newman, Martin Reck, Christian Rolfo, and Alice T. Shaw; (II) the green cluster includes Jean-Yves Douillard, Tony S. K. Mok, Geoffrey R. Oxnard, Rafael Rosell, Lecia V. Sequist, Kenneth S. Thress, Yi-Long Wu, and Helena A. Yu. The research conducted by authors in the green cluster is more related to lung cancer.

Distribution of journals and cocited references

VOSviewer and Bibliometrix analysis indicated that articles in this field were published in 253 journals, while 19 journals met the threshold (minimum of 10 publications). In Figure 5A,5B, each node represents one journal, and the size of the node indicates the number of related publications in the field. Different clusters are shown in different colors, representing cocitations of these journals. Annals of Oncology, Annals of Translational Medicine, Clinical Cancer Research, Clinical Lung Cancer, JCO Precision Oncology, Journal of Thoracic Disease, Journal of Thoracic Oncology, Lung Cancer, Oncotarget, Oncotargets and Therapy, and Scientific Reports are grouped in the red cluster, while Cancer Medicine, Cancers, Frontiers in Oncology, International Journal of Molecular Sciences, Journal of Cancer Research and Clinical Oncology, Molecular Oncology, Thoracic Cancer, and Translational Lung Cancer Research are grouped in the green cluster (Figure 5A). Based on the analysis (Figure 5A), Figure 5B displays the number of articles in this field published by each journal, with size and color density being positively correlated the number of indexed articles.

Figure 5 Cocitation network of journals in ctDNA and lung cancer generated by VOSviewer. (A) Cocitation analysis of journals from VOSviewer. (B) Visualization for the density of journal cocitations. ctDNA, circulating tumor DNA.

In the research on ctDNA and lung cancer, Cancers, Lung Cancer, Frontiers in Oncology, Translational Lung Cancer Research, Journal of Thoracic Oncology, Clinical Cancer Research, Oncotargets and Therapy, and Oncotarget were the top eight journals in terms of publication numbers (Table 4). Moreover, Clinical Cancer Research had the highest number of citations (n=2,738).

Table 5 lists the top five most cited articles. “An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage” (12) was the most cited article, followed by “Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution” (13) and “Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling” (14).

Research hotspots and frontier analysis

We used VOSviewer and found that among 2,329 keywords, 21 had a minimum number of occurrences of 80. The keywords with similar meanings were then merged. Finally, 19 keywords were included in the keyword co-occurrence network graph, and the strength of co-occurrence among links was calculated. VOSviewer grouped these 19 keywords into different clusters, represented by 19 nodes with different colors in Figure 6A. The frequency of occurrence of each keyword is reflected in the diameter of each node. The concurrent frequency of two keywords is reflected in the thickness of the line connecting them. Keywords formed two clusters, indicating two main research directions in lung cancer and ctDNA. The red cluster included “ctDNA”, “EGFR”, “liquid biopsy”, “lung cancer”, “mutations”, “NSCLC”, “plasma”, “resistance”, “survival”, and “therapy”, while the green cluster included “first-line treatment”, “acquired resistance”, “chemotherapy”, “EGFR mutations”, “erlotinib”, “gefitinib”, “open-label”, “osimertinib”, and “tyrosine kinase inhibitors” (TKIs). The color-coded separation produced by VOSviewer mirrors the clinical workflow: the red cluster represents the diagnostic/monitoring phase enabled by ctDNA, while the green cluster represents the subsequent therapeutic choices that are informed by these molecular findings.

Figure 6 The co-occurrence of keywords. (A) The cluster of keywords in the studies of ctDNA and lung cancer in VOSviewer; (B) timeline of research in ctDNA and lung cancer. ctDNA, circulating tumor DNA.

Based on the keyword co-occurrence network, we depicted the emergent time and duration of the top 20 keywords with the strongest citation bursts in ctDNA and NCSLC in Figure 6B. The pale blue line represents the time axis, the blue line represents the duration time, and the red line represents the burst time of each keywords. “Residual disease” had the strongest citation burst (14.1), followed by “plasma DNA” (11.81), “receptor mutations” (8.85), “gefitinib” (8.43), “drug resistance” (8.26), “growth factor receptor” (6.87), “immunotherapy” (5.82), “adjuvant chemotherapy” (5.03), “pembrolizumab” (4.97), “efficacy” (4.28), “tyrosine kinase inhibitor” (4.23), “PD-L1 (programmed cell death ligand-1) expression” (3.74), “personalized medicine” (3.63), “next-generation sequencing” (3.63), “outcome prediction” (3.58), “quantification” (3.58), “risk” (3.47), “recurrence” (3.42), “T790M mutation” (3.36), and “first-line therapy” (3.34). Based on the burst time, “plasma DNA” and “quantification” appeared early in 2008. “Residual disease”, “adjuvant chemotherapy”, “outcome prediction”, “immunotherapy”, and “PD-L1 expression”, “pembrolizumab”, “efficacy”, “recurrence”, and “risk” and currently in their burst period (Figure 6B) and may represent the research frontiers in this field.

A network with eight clusters was obtained using CiteSpace (Figure 7). Figure 7A ranks the ctDNA-lung cancer research hotspots by the number of keywords assigned to each hotspot; hotspot #0 contains the largest keyword set, whereas hotspot #7 contains the smallest. The eight clusters were osimertinib, DNA methylation, immunotherapy, lung adenocarcinoma (LUAD), progression, CTCs, ctDNA, and NATCH (Randomized Trial of Surgery With or Without Paclitaxel Plus Carboplatin as Neoadjuvant or Adjuvant Chemotherapy in Patients With Operable, Non-Small Cell Lung Cancer). In Figure 7B, the keywords in one cluster are spread on the same horizontal timeline from 2001 to 2024, representing the evolution path of each research hotspot across time, with each node representing one keyword. There are multiple rings with different colors in each node. The color changes from purple to yellow indicating appearance times of the keyword from 2001 to 2024. The width of each ring represents the appearance frequency of the keyword in the corresponding year. Immunotherapy, osimertinib, DNA methylation, and CTCs also appeared as research hotspots and frontiers (with more newly formed related keywords within the last 5 years). The research content gradually scatter outward showing multidirectional research paths.

Figure 7 The cluster of keywords in the studies on ctDNA and lung cancer in CiteSpace. (A) The top 8 keywords with the strongest citation bursts. (B) The development directions of hotspots across time. ctDNA, circulating tumor DNA.

Discussion

In the early phase of ctDNA and lung cancer research, the terms “plasma DNA” and “quantification” were already central, and they first appeared as key concepts in 2008 (Figure 6B), suggesting that the most used sampling biofluid of ctDNA has been plasma. In addition, mutation analysis (tissue-derived/tumor-informed or non-tissue-derived/tumor-agnostic assay), involving the quantification and mutation changes of ctDNA was measured and applied in clinical use. From 2011 to 2013, the citation bursts occurred for the keywords “growth factor receptor”, “factor receptor mutations”, “tyrosine kinase inhibitor”, “gefitinib”, “first-line therapy”, “drug resistance”, and “T790M mutation”. This suggests that the focus research in ctDNA in lung cancer has shifted from the use of gefitinib, to first-generation EGFR-TKIs, and then to targeting tumors with EGFR mutations as the drug resistance caused by EGFR mutations, such as T790M, has emerged. From 2014 to 2016, researchers began to focus on personalized medicine, next-generation sequencing (NGS), and first-line therapy, with several EGFR-TKIs being used as first-line therapy in the treatment of patients with lung cancer with EGFR mutations as detected by NGS in ctDNA. From 2017 to 2019, the appearance of the keywords “residual disease”, “adjuvant chemotherapy”, “outcome prediction”, “immunotherapy”, and “PD-L1 expression” suggests that in addressing drug resistance, ctDNA has been used in residual disease detection and outcome prediction. Moreover, blood tumor mutational burden (bTMB) in ctDNA and PD-L1 expression have been used as biomarkers for the guidance and surveillance of immunotherapy administration. From 2020 to 2022, “pembrolizumab”, “efficacy”, “recurrence”, and “risk” appeared as keywords in publications. Keywords currently in their burst period include “residual disease”, “adjuvant chemotherapy”, “outcome prediction”, “immunotherapy”, “PD-L1 expression”, “pembrolizumab”, “efficacy”, “recurrence”, and “risk” and indicate the current research frontiers in ctDNA and lung cancer (Figure 6B).

This section mainly focuses on results of keywords analysis, as the clusters of keywords can help identify the major interests of research in a given field (Figure 6A), while timelines can display the trends in research hotspots and frontiers (Figure 6B); together, they were employed to characterize the dynamic evolution of the ctDNA in lung cancer field. The top 19 keywords with the strongest citation bursts formed two clusters: The green cluster mainly relates to the predictive value of analyzing EGFR mutations in ctDNA for lung cancer treatment management, especially the breakthrough in EGFR-TKIs treatment for patients with EGFR mutations (Figure 6A). The red cluster mainly includes keywords concerning biofluids used for ctDNA sampling and the prognostic value of ctDNA in lung cancer (Figure 6A). Both VOSviewer and CiteSpace listed “immunotherapy” as the current research frontier in ctDNA and lung cancer. Moreover, “osimertinib”, “DNA methylation”, and “circulating tumor cells” appeared as related keywords emerging within the last 5 years (bright yellow dots, Figure 7B) and thus may also constitute research hotspots and frontiers in this field.

EGFR mutations in ctDNA and EGFR-TKIs

The analysis of EGFR mutations and the use of EGFR-TKIs are breakthroughs in lung cancer treatment. Among the top five most prolific authors in this field (Table 3), Yi-Long Wu appeared as a corresponding author, and together with E-E Ke, comprehensively reviewed the history and contemporary perspectives in this field (15). Our bibliometric analysis of the ctDNA-lung-cancer literature addresses the “outstanding questions” highlighted by Ke and Wu (15), offering evidence-based answers derived from the evolving ctDNA research landscape. Moreover, their timeline of the major progress in this area matched our citation burst analysis.

Studies with the keywords “EGFR” and “gefitinib” appeared and surged in 2011 (Figure 6B), soon after gefitinib treatment in EGFR-mutant-positive patients was approved and after EGFR-TKIs were evaluated as first-line therapy in EGFR-mutated lung cancers in 2009 (15).

EGFR-T790M mutations were first reported in 2005 and first detected in activating mutations in plasma DNA in 2009 (15). From 2012, keywords related to EGFR mutations, especially “T790M” mutations, “drug resistance”, and “TKI” began to appear in publications (Figure 6B). In 2013, erlotinib and afatinib were approved for patients with the EGFR mutation (15). By then, erlotinib, gefitinib, and afatinib, were approved as EGFR-TKIs for treating EGFR-mutated NSCLC in the first-line setting, after which, beginning in 2014, “first-line therapy” appeared with more frequency in publications (Figure 6B).

In 2016, “personalized medicine” and “NGS” first surged as high-frequency keywords (15) (Figure 6B). In 2014, it was found that T790M mutation-positive patients with acquired resistance received good results from the third-generation EGFR-TKIs, among which osimertinib was approved by US Food and Drug Administration in 2015. This development marked a turning point, drawing attention to the field from that year onward. In the same year, EGFR C797S was detected by NGS in osimertinib-resistant tumors. NGS can be effective in detecting an expanding myriad of treatable mutations, including EGFR and other gene mutations. This makes it possible to address multiple resistance mechanisms and intratumoral heterogeneity. From 2017 to 2018, the research attention on this topic waned, but interest into NGS remained high.

EGFR mutations

The use of ctDNA in the clinical management of patients began in the form of mutational analysis of EGFR in patients with NSCLC (16). EGFR mutations found in lung cancer ctDNA included in-frame deletion in exon 19 (codons 746–750); point mutations in L718Q, G719X, S768I, T790M, C797S, L858R, and L861Q; and exon 20 insertions. The cobas EGFR Mutation Test v2 (Roche, Basel, Switzerland) is often used for EGFR mutation detection. Detection methods, such as giant magnetoresistive nanosensor analysis, droplet digital polymerase chain reaction (ddPCR), NGS, amplification refractory mutation systems, and single-allele base extension reaction combined with mass spectroscopy, were compared in several studies (17-20). In addition, their features and applications in patient management are described in depth elsewhere (21-24) and have been further investigated in observational studies (25-27) and clinical trials (28-32). However, the details of the clinical trials are beyond the scope of this article and have already been discussed in other work (33).

Due to the difference in EGFR mutation status between tumor tissue and ctDNA (27,34-37), several studies have suggested that ctDNA should initially be used for the screening of EGFR mutation-positive cases, and then tissue tests should be used as a supplement for patients with a negative ctDNA result (30,35,38).

EGFR-TKIs and acquired resistance

Erlotinib, gefitinib, afatinib, dacomitinib, and osimertinib EGFR-TKIs that have been approved for treating EGFR-mutated NSCLC in the first-line setting (39,40). The efficacy of EGFR-TKI can be predicted by EGFR status. Analyzing targetable gene mutations through ctDNA sequencing can assist in patient stratification and can identify individuals who may experience improved survival outcomes from certain treatments.

In one study, patients with advanced NSCLC and the EGFR activating mutation were first treated with an EGFR-TKI, and it was found that patients with lung cancer and ALK alterations could also benefit from TKIs (41). NSCLC evolves during treatment with first-generation EGFR-TKIs, with EGFR-resistant mutations leading to acquired resistance (42,43), among which the most studied is the EGFR-T790M mutation (44-46).

In the randomized phase II APPLE trial, treatment-naive patients with NSCLC and the common EGFR mutation (65% patients had EGFR exon 19 deletion) were first treated with gefitinib. Longitudinal ctDNA EGFR T790M monitoring during treatment with first-generation EGFR inhibitors detected a molecular progression before Response Evaluation Criteria in Solid Tumors (RECIST)-based progression and thus suggested a sequencing strategy consisting of an earlier switch to osimertinib, yielding superior progression-free survival (PFS) and overall survival (OS) (47). ctDNA genomic alterations were also monitored to predict osimertinib efficacy and outcome in patients with NSCLC (48,49).

The evolution or adaptation of tumor to EGFR-TKI treatment involves various resistant mechanisms beyond EGFR T790M, including EGFR mutations such as G724S, L792F/H, G796S/R, C797S/G, and V802F (50-52) as well as ctDNA FCGR3A amplification (53). In comparison to first-generation EGFR-TKIs, second-generation and third-generation EGFR-TKIs have broader inhibitory profiles (39). Passaro et al. [2021] proposed to include uncommon EGFR mutations in data collection and molecular analysis for the optimization of personalized treatment in order to improve patients’ outcomes according to their own genotype (39). Plasma-based ctDNA can offer information on EGFR-TKI-sensitizing and resistance mutation patterns, which can reflect treatment responses (30); meanwhile, longitudinal EGFR-mutation ctDNA monitoring can detect progressive disease before radiologic detection (RECIST-defined progression) in approximately 60% of patients with advanced -stage NSCLC and EGFR mutation (54). Treatment strategies against resistance were investigated in clinical trials. For the treatment of patients with advanced NSCLC and the EGFR T790M mutation, osimertinib was alternated with gefitinib, leading to a delay in the development of resistance to osimertinib (55).

Other hot spots and frontiers

Liquid biopsy

In our analysis, “liquid biopsy” and “NSCLC” ranked second in frequency only to the core term “ctDNA” in the red cluster (Figure 6A). Liquid biopsy has emerged as a practical alternation for understanding tumor genetic constitution, especially when tissue samples are unavailable or insufficient (56,57). In addition, liquid biopsy may better reflect tumor heterogeneity (58). A multitude of NSCLC studies have been conducted (2,17,21,23,26,28-30,32-37,42,46,53-56,59), and thus the data on this disease are robust. ctDNA, as a minimally invasive liquid biopsy, enables the assessment of longitudinal samples for the detection of mutations and tumor mutational burden, thereby enabling the early identification and diagnosis of diseases. Moreover, it helps expand treatment options, monitor treatment responses, and predict outcomes and tumor recurrence. In addition, liquid biopsy with ctDNA supports the development of personalized diagnostics and treatment regimens and aids in clarifying resistance mechanisms (42,60-63). Methods employed for ctDNA isolation and analysis have been previously examined (59,64,65), but whether molecular testing should start with liquid or tissue biopsy remains controversial. For single-gene screening, such as that for EGFR mutations, ctDNA is suggested as the first choice, with tissue tests advised as a supplement in negative cases (30,35,38,66). This also applies to gene panel analysis (67). NGS-based ctDNA assays using blood samples can be effective in detecting gene mutations, return results 26.8 days faster than do samples based on tissue (67), shortens the time from diagnosis to treatment by 23 days (68), and are even able to reveal a greater depth of clinical information as compared to tissue testing (42,60,69,70). Thus, NGS sequencing of treatable driver mutations has been recommended both in ctDNA and tissue testing, allowing for better diagnostic accuracy (71).

The majority of liquid biopsies are performed with blood specimens (69). Other media have also been investigated to improve ctDNA sampling, including exhaled breath condensate (EBC) (58), saliva (72), bronchial aspirates, bronchial wash or lavage, bronchoalveolar lavage (73-75), urine (74,76-78), pericardial effusions (79), ascites, pleura, and pericardial fluid. Recently, cerebrospinal fluid (CSF) has garnered attention (78,80,81), but plasma remains the prevalent specimen type in liquid biopsy for clinical applications (82-84).

EGFR and mutations

“EGFR” and “mutations” were the keywords grouped in the red cluster (Figure 6A). Genes often found mutated in NSCLC include TP53, EGFR, LRP1B, KRAS, ALK, ROS1, BRAF, ERBB2, ERBB4, STK11, and ARID1A (34,85-87). EGFR mutations are among the most common actionable alterations detected in ctDNA in NSCLC (21,27). Plasma-based ctDNA was first used clinically for detecting EGFR mutations in NSCLC (59), and other driver mutations have been discovered (21,43,88). Additional alterations such as MET amplification and EGFR exon 20 p.C797X were associated with osimertinib resistance (21,59,89). KRAS G12C/D has been frequently detected in cases with KRAS mutations (21), and HER2 mutation allele frequencies correspond to the clinical course of disease (90). The KRAS mutation has the strongest correlation between ctDNA variant allele frequency (VAF) and computed tomography (CT) or brain magnetic resonance imaging, as quantified by tumor burden, followed by the TP53 and EGFR mutations (91).

Residual disease, recurrence, and survival

Residual disease was found to be one of the topics in the frontier research in the field of ctDNA and lung cancer (Figure 6B). Outcome prediction and recurrence were identified as key research frontiers, with “survival” and “therapy” being the keywords clustered in the red group (Figure 6A,6B). ctDNA has a direct relationship with different therapies, patient outcomes, and survival. “NSCLC” was another keyword that appeared in the red cluster, for which ctDNA holds significant promise for detecting and monitoring residual disease. The key term “molecular residual disease” or “minimal residual disease” (MRD) appeared in the clusters with “DNA methylation” and “immunotherapy” (Figure 7), emphasizing the application of ctDNA. The timeline regarding the research on using ctDNA to determine MRD has been discussed elsewhere (83). In localized NSCLC, serial radiographic imaging is used for routine clinical surveillance of residual disease, the detection of which is limited to macroscopic disease recurrence and is often inconclusive (92). The MRD in solid tumors, including lung cancer, appears to be a strong predictive and prognostic factor (93). One study found that ctDNA half-life was longer in patients with MRD (103.2 minutes) than in those without MRD (29.7 minutes) (49). The detection of ctDNA level and/or its specific genetic alterations can provide reliable MRD information and has been studied as a marker in MRD monitoring after treatment (14,92,94-99). Residual ctDNA—whether measured at a landmark timepoint or measured repeatedly in longitudinal sampling—has been reported to be an early predictor for treatment efficacy, molecular recurrence, metastasis prediction, and risk classification in NSCLC, and therefore may facilitate early intervention, inform adjuvant treatment choices, and improve the PFS of patients (12,14,63,97,100-105).

Personalized tumor-informed technologies (92,93,106) have potential advantages in MRD detection, with higher sensitivity, better prognostic value, and earlier disease progression or recurrence detection (92,107) as compared to conventional methods. However, these technologies rely on the initial detection of tumor-specific somatic DNA mutations in tissue, which are subsequently monitored over time in ctDNA (93).

ctDNA during chemoradiotherapy

ctDNA during chemoradiotherapy is a critical indicator of MRD. The dynamics of ctDNA during the initial chemotherapy cycles and in long-term follow-up reflect the clinically observed response (93). In patients with NSCLC, the timing of sample collection after chemoradiation therapy is important; for instance, in one study, it was found that high tumor recurrence and low recurrence-free survival (RFS) were associated with plasma-based ctDNA detection at 4.5 months after chemoradiotherapy but not that at 1.6 months after chemoradiotherapy (108).

ctDNA detection before and after definitive radiotherapy

ctDNA detection helps identify optimal treatments such as locally consolidative or systemic chemoradiotherapy. In patients with oligometastatic NSCLC, undetectable ctDNA before radiation therapy is associated with better PFS and OS, while higher ctDNA VAF and mutational burden measured before chemoradiotherapy is correlated with worse outcomes (108).

During definitive chemoradiotherapy, decreasing ctDNA concentrations indicates treatment response and better outcomes (94), with ctDNA positivity after radiotherapy being associated with a worse PFS (109). One study observed a rapid ctDNA level increase after a single fraction of radiotherapy, but its implications for treatment response or mutational profiling remain unclear (110).

ctDNA analysis during the perioperative period

Several studies have examined the value of longitudinal ctDNA analysis during the perioperative period in patients with lung cancer undergoing curative-intent surgical resection. Plasma samples are collected at different perioperative time points (before surgery, during surgery, and post-surgery) and during follow-up for predicting clinical outcomes (103,111,112). The ctDNA dynamics during perioperative period are effective in early detection of MRD and highly concordant with pathologic response and thus could benefit the management of patients with lung cancer.

(I) Preoperative ctDNA

Preoperative ctDNA is detected more frequently in patients with advanced-stage NSCLC as compared to patients with early-stage NSCLC (99) and has been positively correlated with tumor size (113) and recurrence risk but negatively correlated with outcomes in patients with localized NSCLC (92,96,103,111,114,115), including EGFR mutant-positive early-stage (I to IIIA) NSCLC (116). Certain research indicates that ctDNA clearance after neoadjuvant therapy, rather than baseline ctDNA status, is significantly associated with higher disease-free survival in resectable stage IIIA NSCLC (117,118). In a study that compared the detection of actionable mutations in ctDNA between preoperative pulmonary venous blood, surgical discharge, and peripheral blood at the first and last follow-up, it was found that the preoperatory time point offered the highest sensitivity in mutation detection in early-stage lung cancer (119).

(II) Intraoperative ctDNA

Mutations identical with resected lung tumors have been detected in ctDNA collected from intraoperative pulmonary venous blood and peripheral blood (120), with higher ctDNA detection rates and concentrations compared to those of presurgical or postsurgical specimens in early-stage NSCLC (112,119).

(III) Postoperative ctDNA

MRD can be determined via both ctDNA-based and tissue-based testing, with some studies only considering positive status when both tests identify a shared mutation from the same case (48,111). In a study on early-stage NSCLC, ctDNA concentration and the average mutant allele fraction decreased after radical tumor resection (48), and the majority of patients positive for preoperative ctDNA achieved ctDNA clearance at week 4 postoperation (116). Moreover, postoperative ctDNA positivity has been associated with histological grade (113), poor RFS (63,103,113,121,122), and OS (92,103) and may be an independent predictor of recurrence but not for treatment response to targeted therapy (95). ctDNA-based MRD can be detected within 2 weeks after surgery in most patients with disease recurrence, preceding radiologic imaging by 6.83 to 12.6 months (92,96). In a study on early-stage (stages I to IIIA) EGFR mutant-positive NSCLC, longitudinal monitoring of ctDNA recurrence was detected before radiological recurrence in 69% of patients with exon 19 deletion and in 20% with the L858R mutation (116). Other research reported that ctDNA-based MRD was more closely associated with RFS than was tumor-node-metastasis (TNM) stage or all other clinicopathologic variables examined (111). The timing for the monitoring of ctDNA postsurgery varies, with some studies suggesting early detection (on the third day after R0 resection) (48,97,99,111,112) and others indicating detection within 1 week to months after surgery as significant for predicting recurrence and survival outcomes (96,99,105,111,112,114). Detection timing and technologies for postoperative MRD have not yet been standardized, which has hindered the use of ctDNA-based MRD in clinical management (102,123,124). Nonetheless, compared to other clinicopathological variables, ctDNA-based MRD has a greater ability to predict RFS (121) and appears to be superior to carcinoembryonic antigen level in monitoring postoperative recurrence (122).

ctDNA analysis in early-stage NSCLC

The bulk of the studies on ctDNA have focused on advanced-stage NSCLC, as the detection rate of ctDNA by liquid biopsy is lower for early-stage NSCLC, leading to the relatively low concordance between ctDNA and tissue-based DNA (125). In recent years, researchers have intensively investigated novel methods in the field of ctDNA analysis (66,106,126-128). Although the correlation between plasma and tissue samples in early-stage NSCLC is weaker than that in advanced-stage NSCLC, longitudinal ddPCR detection of ctDNA has been used as a biomarker for MRD; moreover, research indicates that ctDNA detected at any time point in early-stage NSCLC is significantly associated with shorter RFS (129) and poor disease-free survival (116). It is expected that with the improvement of sensitivity and specificity, ctDNA can be applied in daily clinical practice for the early diagnosis of NSCLC, monitoring of treatment effect, early prediction of recurrence, and guidance for adjuvant therapy.

CTCs and ctDNA

“Circulating tumor cells” was among the keywords with the strongest citation bursts. Beyond ctDNA, circulating analytics also includes CTCs and circulating tumor-derived endothelial cells (130,131). Some research has sought to develop assays that combine CTCs and ctDNA (132-137). However, whether assays combining CTC and ctDNA can improve lung cancer detection remains unknown.

Greater sensitivity in mutation detection has observed in ctDNA than in CTCs, with detected mutations being strongly associated with mutation status in matched tumors (138-140).

DNA methylation in ctDNA

“CTCs” and “DNA methylation” were among the top 8 keywords with the strongest citation bursts in CiteSpace (Figure 7), but neither appeared in the top 20 keywords from VOSviewer (Figure 6). Aberrant DNA methylation is considered to be one common cause and an early event in tumorigenesis and tumor progression (141,142). DNA methylation often occurs in cytosine–phosphate–guanine dinucleotide (CpG)-enriched regions around gene promoters and in the gene body, resulting in gene silencing and transcriptional activation, respectively (6). A novel plasma-based diagnostic assay using DNA methylation markers was developed that was capable of detecting and identifying early-stage lung cancers (66,128,143-146). Moreover, a ctDNA methylation assay may help detect potential tumor progression in early-stage NSCLC (141,147).

A study used blood-based diagnostic assays incorporating a set of ctDNA methylation markers, which were filtered from tissue-derived DNA methylation markers by a training set and an independent validation set of plasma samples to detect early-stage lung cancer and to differentiate malignant tumors from benign pulmonary nodules (128). In plasma DNA, the methylation of both SHOX2 and PTGER4 was found to be able to differentiate lung cancer and nonmalignant diseases (148). CDO1, TAC1, SOX17, and HOXA7 methylation in plasma cfDNA was significantly higher in stage IA or IB NSCLC than in benign noncancerous lesions. Combinations of some of these markers have demonstrated high sensitivity and specificity for tumor detection (149). Urine-based cfDNA has also been shown to increase the methylation levels of CDO1 and SOX17 in patients with NSCLC (150). The association between aberrant ctDNA methylation and p16 (CDKN2A/INK4A), APC, RASSFIA1, SHOX2, FOXA1, SLFN11, and RARB genes has been investigated (2,73,141). Analysis of SEPT9 promoter methylation may aid early lung cancer diagnosis (151).

The alternation of ctDNA methylation is not only used as an indicator for early lung cancer detection but also for posttreatment monitoring and outcome predication (115,142,152-160). Lianidou examined the ctDNA methylation markers for early detection (e.g., APC, HOXA9, RARβ2, RASSF1A, KMT2C), prognosis (e.g., BRMS1, SOX17, MGMT, p16, DAP kinase, GSTP1, HOXA9, KRTAP8-1, CCND1, TULP2), and therapy response in lung cancer (161).

Furthermore, DNA Methylation in ctDNA can also be used for the classification of small-cell lung cancer (SCLC) subtypes (162). Promoter methylation levels of HOXD3 and RASSF1A in SCLC have been reported to differ from those of NSCLC (158), with the methylation levels of HOXA9 and RASSF1A being higher in SCLC than in NSCLC (163).

In conclusion, DNA methylation alterations of certain genes in lung cancer can provide novel epigenetic biomarkers in NSCLC that may be used for early detection, diagnosis, prognosis, risk assessment, disease monitoring, and the prediction of therapeutic response and outcomes.

Adjuvant chemotherapy

Adjuvant chemotherapy also emerged as a potential frontier in research in our analysis. Postoperative ctDNA detection may guide therapeutic intervention after surgical treatment in early-stage NSCLC. Adjuvant therapy can benefit ctDNA MRD-positive patients (63,97,111,121) but may worsen outcomes in ctDNA MRD-negative ones (98,111,121). In addition, ctDNA positivity after adjuvant chemotherapy has been significantly associated with worse RFS (63).

After nonsurgical treatment, ctDNA-MRD detection might also guide further therapeutic intervention to avoid overtreatment. During and after chemoradiotherapy, early undetectable ctDNA in patients indicates superior survival outcomes, regardless of whether subsequent consolidation with immune checkpoint inhibitors (ICIs) is applied (94).

Overall, these findings suggest that ctDNA-MRD is effective in the early detection of relapse and in risk stratification and thus may inform further personalized therapeutic intervention, facilitate decision-making, and identify those who may benefit from adjuvant therapy. However, the results remain to be further confirmed in prospective trials with a larger sample size.

Immunotherapy, PD-L1 expression, and pembrolizumab

Both VOSviewer and CiteSpace indicated immunotherapy as being a current research frontier in the field of ctDNA and lung cancer (Figure 6B, Figure 7). Recent clinical results support the use of ICIs, such as anti-programmed cell death-1 (PD-1) inhibitors (e.g., pembrolizumab and nivolumab), anti-PD-L1 inhibitors (e.g., atezolizumab) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) (164). Pembrolizumab-based immunotherapy and chemoimmunotherapy have become standard first-line treatments for patients with metastatic NSCLC without a targetable mutation (89). However, only a portion of patients with NSCLC respond to these agents (165,166), and some experience severe immune-related adverse events (164). Tumor-immune infiltration during ICI treatment reduces the effectiveness of radiological surveillance in evaluating ICI efficacy (165).

ctDNA surveillance may be a promising tool for monitoring the treatment efficacy of immunotherapy, whether applied with conventional therapy (167,168) or as consolidation therapy (169-171)/neoadjuvant therapy (117,172,173); moreover, it may assist clinicians in therapeutic decision-making (88,174-178). The tumor-informed assays are superior to tumor-agnostic assays for ctDNA detection (179,180). In one study, panels individualized from whole-exome sequencing data of treatment-naïve tumors were used to track neoantigens, and such personalized panels were used for ctDNA sequencing (181). The absence of ctDNA at baseline and a change in ctDNA abundance in the early posttreatment period (e.g., ctDNA level at 8 weeks after ICI) have been associated with radiographic responses, PFS, and OS (172,174,179-185). This association is particularly informative in predicting outcomes among patients with radiographically stable disease (186). The initial ctDNA response occurs at a median 6–33 weeks earlier than does the initial radiographic response [median 42.5 days (182); mean 21 weeks (186)]. Additionally, novel biomarkers for immunotherapy such as bTMB, and unfavorable mutation score can be calculated from the ctDNA profiles (2,59,185,187-190).

In our study, we examined publications on ctDNA and lung cancer to characterize the trends in research in this field and the value of ctDNA in lung cancer management. Although VOSviewer, CiteSpace, and the “Bibliometrix” R package were employed to ensure reliability of data resources and objectivity of the outcome, there remained certain limitations in the methodology employed for bibliometric and visualization analyses. First, in order to ensure the material used for analyses were of high-quality, selection bias inevitably occurred. Only the WoSCC database was used for gathering relevant publications, and under further screening, only original research papers and review articles in English were used for analyses. Consequently, high-quality papers indexed in other databases, such as PubMed and Scopus, or those written in other languages, might have been excluded. Second, only articles published before May 4, 2024, were included in this study, and thus any trend beyond this point could not be discerned. Third, our analysis might have missed the influence of newly published articles, as more recent studies could have been excluded due to their low citation number at this time. Finally, the results of the cluster keyword analysis from CiteSpace may vary due to the lack of a unified parameter setting.

The means to integrating ctDNA-guided treatment strategy into current standard clinical practice for lung cancer needs to be further investigated. To avoid overtreatment and to improve the reliability of ctDNA in lung cancer as an indicator for early detection, guidance of treatment strategy, and prediction of relapse and outcomes, ctDNA detection and analysis should be further standardized. This may include determining which mutations should be tested (e.g., tumor-informed or tumor-naive, treatment-influenced or treatment-dependent), how to choose the sampling time points (e.g., starting point and interval), and the means to combining tissue biopsy or radiographic assessment with liquid biopsy.

Conclusions

This study employed bibliometric and visualization analyses in the field of ctDNA and lung cancer to assess the literature distribution across various dimensions, including time, country/region, institution, journal, and author. We found that the field began attracting attention in 2015. China had the highest number of publications, while the United States had the highest centrality. The University of Texas MD Anderson Cancer Center had the highest centrality among organizations. Centrality/productivity and collaborations among countries/regions, institutions, journals, and authors were visualized, facilitating the identification of potential research collaborators for future research. The evolution of research hotspots developed from mutations—especially EGFR mutations—TKIs, NSCLC, and acquired resistance to the current research frontiers of residual disease, adjuvant chemotherapy, outcome prediction, immunotherapy, PD-L1 expression, pembrolizumab, efficacy, recurrence, and risk. The keyword “immunotherapy” also appeared in the eight clusters, indicating it as a research hotspot. In summary, this study lays the groundwork for understanding the research topics, focal points, and developmental trends in the applications of ctDNA in lung cancer.

Acknowledgments

None.

Footnote

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-76/prf

Funding: This work was supported by the Xi’an Medical University under grants 2023BS32 (to S.Z.) and 2023BS43 (to P.C.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-76/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)