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Natural Language Processing (706) Bioinformatics Methods and Algorithms (14) Digital Biomarkers and Digital Phenotyping (198)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34548, first published .
Global Research Trends in Tyrosine Kinase Inhibitors: Coword and Visualization Study

Global Research Trends in Tyrosine Kinase Inhibitors: Coword and Visualization Study

Global Research Trends in Tyrosine Kinase Inhibitors: Coword and Visualization Study

Authors of this article:

Jiming Hu1 Author Orcid Image ;   Kai Xing2 Author Orcid Image ;   Yan Zhang3 Author Orcid Image ;   Miao Liu4 Author Orcid Image ;   Zhiwei Wang2 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

    Original Paper

    1School of Information Management, Wuhan University, Wuhan, China

    2Department of Cardiovascular Surgery, Renmin Hospital of Wuhan University, Wuhan, China

    3Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China

    4Department of Pediatrics, Renmin Hospital of Wuhan University, Wuhan, China

    *all authors contributed equally

    Corresponding Author:

    Miao Liu, PhD

    Department of Pediatrics

    Renmin Hospital of Wuhan University

    No. 9 Zhang Zhidong Road

    Wuchang District

    Wuhan, 430060

    China

    Phone: 86 13349879352

    Email: liumiao915@whu.edu.cn


    Background: Tyrosine kinase inhibitors (TKIs) have achieved revolutionary results in the treatment of a wide range of tumors, and many studies on this topic continue to be published every year. Some of the published reviews provide great value for us to understand TKIs. However, there is a lack of studies on the knowledge structure, bibliometric analysis, and visualization results in TKIs research.

    Objective: This paper aims to investigate the knowledge structure, hotspots, and trends of evolution of the TKIs research by co-word analysis and literature visualization and help researchers in this field to gain a comprehensive understanding of the current status and trends.

    Methods: We retrieved all academic papers about TKIs published between 2016 and 2020 from the Web of Science. By counting keywords from those papers, we generated the co-word networks by extracting the co-occurrence relationships between keywords, and then segmented communities to identify the subdirections of TKIs research by calculating the network metrics of the overall and local networks. We also mapped the association network topology, including the network within and between TKIs subdirections, to reveal the association and structure among varied subdirections. Furthermore, we detected keyword bursts by combining their burst weights and durations to reveal changes in the focus of TKIs research. Finally, evolution venation and strategic diagram were generated to reveal the trends of TKIs research.

    Results: We obtained 6782 unique words (total frequency 26,175) from 5584 paper titles. Finally, 296 high-frequency words were selected with a threshold of 10 after discussion, the total frequency of which accounted for 65.41% (17,120/26,175). The analysis of burst disciplines revealed a variable number of burst words of TKIs research every year, especially in 2019 and 2020, such as HER2, pyrotinib, next-generation sequencing, immunotherapy, ALK-TKI, ALK rearrangement. By network calculation, the TKIs co-word network was divided into 6 communities: C1 (non-small–cell lung cancer), C2 (targeted therapy), C3 (chronic myeloid leukemia), C4 (HER2), C5 (pharmacokinetics), and C6 (ALK). The venation diagram revealed several clear and continuous evolution trends, such as non-small–cell lung cancer venation, chronic myeloid leukemia venation, renal cell carcinoma venation, chronic lymphocytic leukemia venation. In the strategic diagram, C1 (non-small–cell lung cancer) was the core direction located in the first quadrant, C2 (targeted therapy) was exactly at the junction of the first and fourth quadrants, which meant that C2 was developing; and C3 (chronic myeloid leukemia), C4 (HER2), and C5 (pharmacokinetics) were all immature and located in the third quadrant.

    Conclusions: Using co-word analysis and literature visualization, we revealed the hotspots, knowledge structure, and trends of evolution of TKIs research between 2016 and 2020. TKIs research mainly focused on targeted therapies against varied tumors, particularly against non-small–cell lung cancer. The attention on chronic myeloid leukemia and pharmacokinetics was gradually decreasing, but the focus on HER2 and ALK was rapidly increasing. TKIs research had shown a clear development path: TKIs research was disease focused and revolved around “gene targets/targeted drugs/resistance mechanisms.” Our outcomes will provide sound and effective support to researchers, funders, policymakers, and clinicians.

    JMIR Med Inform 2022;10(4):e34548

    doi:10.2196/34548

    Keywords

    TKIs; coword analysis; literature visualization; NSCLC; targeted therapy; CML; topics distribution; HER2; pharmacokinetics 


    Background

    Tyrosine kinases (TKs) are a collective term for dozens of kinases encoded by multiple genes, which can phosphorylate tyrosine residues in cells [1]. Based on varied cellular localizations, the TKs family is divided into receptor tyrosine kinases (RTKs) [2] and non-RTKs [3]. RTKs consist of 20 subfamilies (eg, epidermal growth factor receptor or EGFR [4], vascular endothelial growth factor receptor or VEGFR [5]), whereas non-RTKs include 10 subfamilies such as ABL, SRC, and CSK [6]. TKs have the common activity to catalyze the transfer of γ-phosphate groups on adenosine triphosphate to the tyrosine residues of a variety of target proteins [1,3-7], and this process plays a key role in signal transduction within the cell. Abnormal activities of TKs are closely associated with proliferation, invasion, metastasis, apoptosis, and tumor angiogenesis in non-small–cell lung cancer (NSCLC) [8], chronic myeloid leukemia (CML) [2,9], and many other tumors. Therefore, TKs have become excellent targets for tumor therapy.

    Tyrosine kinase inhibitors (TKIs) are a class of small-molecule compounds that can specifically inhibit TKs. They can penetrate through the cell membrane and block the signaling pathway of tumor proliferation, with some TKIs also capable of inhibiting angiogenesis [1,10]. TKIs have revolutionized the treatment of a variety of tumors [10-12]; for example, imatinib has been a typical pioneer in successfully translating oncogene research into molecular targeted therapy. Now, TKIs have developed to the fourth generation, which aims to overcome drug resistance due to T790M and C797S mutations [13]. More than 30 small-molecule TKIs have been approved for marketing by the US Food and Drug Administration (FDA), and hundreds of drug candidates are in various stages of clinical trials [13-15]. Therefore, this article aims to understand the development process of TKIs research, identify the main research directions, and analyze the potential research hotspots.

    Co-word analysis is a content analysis method to study the knowledge structure and evolutionary patterns of various fields. It can facilitate researchers to identify hotspots, composition, paradigms, and evolution of a field by calculating the word pairs and co-occurrence of noun phrases in the literature [16-18]. This method has been used widely in medical bibliometric analysis, such as precision medicine [16], neonatal ischemic-hypoxic encephalopathy [19], stem cell research [20], neural stem cells [17], tumor immunotherapy [18], disaster medicine [21], medical big data [22], surgical robotics [23], epilepsy genetics [24]. We propose to use the co-word analysis and literature visualization to explore the knowledge structure, evolution trends, and associations among subtopics of TKIs research, aiming to help clinicians and scholars have a comprehensive understanding of TKIs and to give suggestions for research and usage of TKIs.

    Literature Review

    In recent years, targeted therapies have become a hotspot in the development of antitumor drugs with their advantages of high selectivity and low side effects [25,26]. TKIs are revolutionary targeted drugs that inhibit tumor proliferation by interfering with or inhibiting specific proteins within cancer cells, thus exerting prominent antitumor effects [1-3]. Among them, imatinib was the first targeted antitumor drug [27], which was first approved in 2001 for the treatment of BCR-ABL–positive and Philadelphia chromosome–positive CML [3,6]. And then, the first-, second-, and third-generation TKIs, represented by gefitinib, dasatinib, and osimertinib, have been validated in hundreds of clinical trials and approved for marketing [10-12,28,29].

    Genetic testing has been developed rapidly. Next-generation sequencing allows for sequencing genome and exome within days and makes it possible to identify patients with druggable mutations quickly and precisely [30]. Meanwhile, multidisciplinary collaboration between pharmacology and clinical science has brought a leap forward in basic research and clinical applications of TKIs. First, tumor-targeted therapies are the most established area for TKIs, especially in the treatment of lung cancer [12,15,31,32] and leukemia [10,11,28,29]. TKIs have improved the quality of life and extended survival in patients with advanced NSCLCs [33]. Imatinib and gefitinib have become first-line drugs due to their outstanding clinical efficacy in patients with BCR-ABL–positive CML [10,11,28,29]. Second, clinical trials of various drugs targeting HER2 and ALK (eg, trastuzumab [34,35], palivizumab [36], ceritinib [37]) have manifested excellent effects. Third, pharmacokinetics is another focus of TKIs research. Optimization and selectivity study is an important direction for continuing clinical trials after the launch of many TKIs. Besides, individualized blood concentration monitoring is important for patients with poor efficacy or severe side effects [38].

    Previous Efforts

    In recent years, TKIs have been widely used for tumor-targeted therapies. Numerous research efforts helped clinicians and scholars better understand TKIs and facilitated the clinical translation of study outcomes.

    Based on recent reviews, the current status of TKIs research is summarized as follows: First, resistance to TKIs is becoming increasingly prominent, of which genetic mutations (eg, T790M [39,40], C797S [13], D761Y [41], L747S [42]) are the main cause. It has become essential to find new molecular mechanisms underlying resistance to TKIs and to establish individualized dosing regimens. Second, the application of drugs such as erlotinib [43], osimertinib [15,33], and gefitinib [44] has gradually matured and occupied an important position in the treatment of various tumors such as NSCLCs [11,12]. Third, drugs targeting HER2 and ALK continue to emerge, which offers new hope for solving the plague of drug resistance [45,46]. To date, hundreds of new TKIs candidates are in various stages of clinical research [47].

    Rationale for the Study

    Research on TKIs continues to grow to benefit more patients. However, there is still a lot of uncharted territories to explore in TKIs research. How to discover new biomarkers of TKIs? How many new applications of TKIs have been discovered? How to select TKIs with better clinical effects and fewer side effects for targeted therapy? How to overcome multidrug resistance in patients with tumors? How to individualize the use of TKIs in precision medicine? All these questions need scientific bibliometric analysis based on the results of TKIs research. The purpose of our study is to address the following questions:

    1. What is the overall knowledge structure of TKIs research?
    2. What are the subdirections of TKIs research and how do they interact with each other?
    3. What are the evolutionary status and development trends of TKIs research in the temporal dimension?

    Data Collection and Processing

    It is well known that Web of Science Core Collection (WOSCC) is the most extensive and comprehensive academic literature database, so we used keywords including “Tyrosine kinase inhibitor, Tyrosine kinase inhibitors, TKI, TKIs, Tyrosine kinases inhibitors, Tyrosine kinases inhibitors” in WOSCC to precisely search all studies about TKIs by limiting the period to 2016-2020 and the literature types to journal papers, reviews, and conference papers. The specific search formula was “(TS=(‘Tyrosine kinase inhibitor’ OR ‘Tyrosine kinase inhibitors’ OR ‘TKI’ OR ‘TKIs’ OR ‘Tyrosine kinases inhibitors’ OR ‘Tyrosine kinases inhibitor’)) AND LANGUAGE: (English) Refined by: DOCUMENT TYPES: ( ARTICLE OR REVIEW OR PROCEEDINGS PAPER ) Timespan: 2016-2020. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESCI, CCR-EXPANDED, IC.”

    A total of 13,895 documents were retrieved and exported in the tab-delimited (Win) format. Next, the records containing the aforesaid search terms in the titles or keywords were retained, while those without keywords and with search terms appearing only in the abstracts were excluded [16,48]. Finally, 5584 records were obtained for the subsequent co-word network analysis (Figure 1).

    ‎
    Figure 1. Search procedure for documents in TKIs research. DE: descriptor; TKI: tyrosine kinase inhibitor; TI: title; WOSCC: Web of Science Core Collection.
    View this figure

    Because there are irregularities and inconsistencies in the writing of keywords submitted by the authors themselves in WOS, it is necessary to preprocess them. First, this paper aims to depict the research status of TKIs by using other terms associated with TKIs, so TKI itself as well as the synonyms and hypernyms of TKI were removed. Keywords whose meaning is broad (eg, review, development, problem) were also removed. Second, the co-word analysis generally targets high-frequency keywords and their relationships, as keywords with very low frequencies cannot reflect the main direction of this research. Therefore, this paper (1) generated a list of keywords by frequency-descending order and (2) then defined the threshold of high-frequency keywords according to the cumulative percentage of frequencies in the list [48]. In the next step, (3) keywords with frequencies below the threshold were merged into the words with the closest meanings; besides, words with the same meaning but different forms were merged, such as “BCR-ABL TKI” to “BCR-ABL” and “epidermal growth factor receptor” to “EGFR.” Finally, (4) after deduplicating the merged keywords, a new list of keyword frequencies was generated.

    Network Construction and Analysis

    The keywords in a paper are an accurate description of its main content, so mining keywords and their relationships can help reveal the hidden connotation of a research field [49]. If 2 words co-occur in the same connotation unit (eg, keywords in a paper), they are related or similar in connotation and have consistency in connotation expression. Their co-occurrence frequency is equal to the number of papers that contain them at the same time, and the greater the frequency, the stronger the semantic association between them [50]. By constructing co-word networks and performing structural analysis and visualization, co-word analysis can effectively reveal the underlying connotations, research structures, and even evolutionary trends of a research field [51].

    In this paper, the above preprocessed data were imported into SCI2 [52] for frequency statistics and co-word network generation (.net format). Then, the .net file was imported into the network analysis tool Pajek [53] to calculate network indicators, including centralization and centrality [54], density [55], and the clustering coefficient [56], and to perform community segmentation to identify major subdirections. Centralization refers to the centripetal or consistency of the co-word network as a whole, while centrality reflects the keywords’ position in the network and their ability to influence and control the network [54]. Density represents the degree of association of the network as a whole, and the stronger the association, the more mature the research field. The clustering coefficient reflects the possibility that words will cluster into classes depending on the association and its strength, and the possibility that the network will be distinctly divided into several subnetworks or subclasses. Combined with the community segmentation algorithm (Louvain) [57], the co-word network will be divided into distinctive communities, each of which represents a subdirection, with strong ties within the communities and loose ties between the communities, reflecting a greater concentration or consistency in the connotative associations of words within the community. Keywords are tightly linked within communities and loosely linked between communities, reflecting that keywords possess more focused or consistent connotations within communities.

    Mapping and Visualization

    To show the structure and characteristics of the TKIs research more intuitively and clearly, we visualized the topology, evolutionary venations, and development trend of the co-word network.

    First, the visualization of the network topology was performed. VOSviewer [58] was used for the multilevel presentation of co-word networks and communities, including the intercommunity and intracommunity association network graphs. In the network graphs, nodes represent keywords or communities, and edges represent co-occurrence relationships between words or communities. The size of nodes and the thickness of lines are proportional to the frequency of keywords and the scale of communities, respectively, and the nodes and lines belonging to different communities are distinguished by different colors. These network diagrams visualize the importance and association relationships of keywords or communities in TKIs and help to analyze the distribution and structural characteristics of TKIs research.

    Second, the visualization of evolutionary venations was performed. We divided each year’s records into several communities. Then we used Cortext [59] to calculate the overlapping relationships between communities in adjacent years and connected them through “tubes.” In the tube diagram, bars of different colors and sizes represent communities of different sizes, and the tubes connected by several bars represent the continuation of the research theme, which can be considered as evolutionary venations. The evolutionary trends of TKIs research over time are visualized by graphically characterizing the continuity, convergence, and divergence of communities.

    Third, the visualization of the developmentary degree of the subdirections of TKIs research was performed. These research communities can be considered as subdirections of TKIs research, and each community or subdirection exhibits specific development status depending on the density and centrality. So, we drew a 2D strategic diagram based on the calculation of the density and centrality of each community. The strategic diagram took centrality, which represented the core degree of research directions in TKIs, as the horizontal axis, and density, which represented the developmental maturity of research directions, as the vertical axis, and the mean of community density and centrality as the origin. Ultimately, communities were mapped into 4 quadrants to visualize the degree of centrality and maturity of different research directions in TKIs.

    Fourth, the visualization of burst words was performed. The changes in keyword frequency fluctuate significantly, with some of the words appearing in sudden bursts, reflecting the existence of distinct epochal characteristics of TKIs research. Therefore, we detected keyword bursts and combined their burst weights and durations to reveal changes in the focus of TKIs research [60].


    Themes Involved in TKIs Research

    We extracted 10,956 unique keywords from the 5584 available paper titles, and their total frequency was 28,743 (Figure 2). After preprocessing, 6782 unique words with a total frequency of 26,175 were left. After several rounds of testing and discussion, the threshold value of high-frequency words was taken as 10 in this paper. So after merging the keywords with frequencies lower than 10 into their superordinate words, we finally obtained 296 keywords for the subsequent co-word analysis (Table 1 and Multimedia Appendix 1). These 296 keywords, whose total frequency accounted for 65.41% (17,120/26,175), can represent the mainstream of TKIs research in the past 5 years and can also reflect a strong concentration trend of TKIs research.

    Burst keywords can represent important changes in TKIs research. Figure 3 shows a varying number of burst words in TKIs research each year, whose duration is expressed in terms of the length of the horizontal bar and weight in terms of the area. As can be seen from Figure 3, a variable number of emergent terms have appeared in TKIs research every year since 2016, especially in 2019 and 2020, indicating the emergence of new research themes in this field every year. The greater weight of burst words in 2020-2021 (eg, HER2, pyrotinib, next-generation sequencing, COVID-19, immunotherapy, ALK-TKI, ALK rearrangement, cell-free DNA, liquid biopsy, personalized medicine) suggests that these words in TKIs research were extensively explored by researchers in 2020.

    ‎
    Figure 2. Yearly number of papers and words related to tyrosine kinase inhibitor (TKI) research (2016-2020).
    View this figure
    Table 1. Top 30 keywords in papers related to tyrosine kinase inhibitor (TKI) research.
    RankingWordsFrequency
    1“Non-small cell lung cancer”1344
    2“EGFR”916
    3“Chronic myeloid leukemia”586
    4“EGFR-TKI”506
    5“EGFR mutation”404
    6“Lung cancer”370
    7“Erlotinib”299
    8“Imatinib”283
    9“Osimertinib”261
    10“Gefitinib”257
    11“Targeted therapy”256
    12“Renal cell carcinoma”227
    13“Sunitinib”219
    14“Lung adenocarcinoma”207
    15“Mutation”201
    16“Resistance”192
    17“Afatinib”186
    18“Chemotherapy”183
    19“Cancer”175
    20“Dasatinib”162
    21“Drug resistance”159
    22“T790M”156
    23“ALK”156
    24“Brain metastasis”136
    25“Tumor”134
    26“BCR-ABL”128
    27“Nilotinib”121
    28“Crizotinib”120
    29“HER2”119
    30“Apoptosis”113
    ‎
    Figure 3. Burst disciplines of TKI research from 2016 to 2021. TKI: tyrosine kinase inhibitor; VEGFR: vascular endothelial growth factor receptor.
    View this figure

    Correlation Structure of Keywords in TKIs Research

    Overview

    Our analysis revealed that the co-word network consisting of the 296 keywords was exactly the maximal connected subgraph, that is, none of the high-frequency words in TKIs research is isolated, and all of them have paths associated with others, indicating that the research topics in this field form a whole that is interrelated and interact with each other.

    Indicators of the Correlation Network

    The overall indicators of the co-word network are shown in Table 2. The average degree of the network is 49.58, which means that a keyword in TKIs research is directly associated with 49.58 other keywords on average. These 49.58 keywords represent 16.75% of the entire network, which is a relatively small percentage, indicating that the range of intertopic associations in TKIs research is not extensive. The high degree of centralization of the network indicates a strong tendency to be centripetal or concentrated; the high closeness centralization and low betweenness centralization indicate that keywords are most directly related to each other rather than indirectly related; the high clustering coefficient indicates that keywords are likely to cluster into communities with certain words as the core. Collectively, TKIs research has clustered in certain specific subdirections in recent years, between which the distinction is obvious. However, the density of the current co-word network is not high, that is, the keywords are not closely related to each other, which indicates that TKIs research is more seriously fragmented and does not form a unified and mature research identity. We further divided the TKIs co-word network into 6 communities and calculated the module degree [61] to ensure a good division.

    The network indicators of the keywords reflected their position and role in the TKIs co-word network (Table 3). Non-small cell lung cancer, EGFR, targeted therapy, lung cancer, EGFR-TKI, erlotinib, chemotherapy, cancer, sunitinib, and resistance all appear in the top 10 list of degree centralization and closeness centralization. The research topics associated with these words play an important role and have a strong influence on the whole field, while other words are likely to be clustered into a community with the above words as the core, forming a distinctive research subdirection. In addition to resistance, the above words also appear in the top 10 list of betweenness centralization, which serves as “bridges” in TKIs research, suggesting that more collaborations or synergies between TKIs research need to pass through these terms.

    Table 2. The whole network indicators.
    IndicatorsValue
    Number of nodes296
    Number of lines7338
    Average degree49.5811
    Network all degree centralization0.5953
    Network all closeness centralization0.5265
    Network betweenness centralization0.0525
    Network clustering coefficient0.4702
    Density0.1681
    Number of communities6 (Modularity: 0.3062)
    Table 3. Top 10 keywords in terms of degree, betweenness, and closeness centrality.
    RankingWordsDegreeWordsClosenessWordsBetweenness
    1“Non-small cell lung cancer”224“Non-small cell lung cancer”0.8060“Non-small cell lung cancer”0.0552
    2“EGFR”204“EGFR”0.7642“EGFR”0.0397
    3“Targeted therapy”180“Targeted therapy”0.7195“Targeted therapy”0.0346
    4“Lung cancer”179“Lung cancer”0.7178“Chemotherapy”0.0320
    5“EGFR-TKI”166“EGFR-TKI”0.6958“Lung cancer”0.0291
    6“Erlotinib”164“Erlotinib”0.6925“EGFR-TKI”0.0251
    7“Chemotherapy”161“Chemotherapy”0.6876“Erlotinib”0.0216
    8“Cancer”153“Cancer”0.6751“Sunitinib”0.0214
    9“Sunitinib”151“Sunitinib”0.6720“Imatinib”0.0211
    10“Resistance”142“Resistance”0.6585“Cancer” 0.0206
    Analysis of Thematic Communities

    Depending on the association between keywords, TKIs are divided into 6 clusters or communities with certain important terms at their core. They are C1 (NSCLC); C2, targeted therapy; C3, CML; C4, HER2; C5, pharmacokinetics; and C6, ALK (Table 4). These 6 communities represent the subdirections of TKIs research in 2016-2020. They are each closely associated within but loosely associated with each other.

    In terms of size, the subdirections of TKIs research can be divided into 3 echelons (Table 5). The first echelon includes C1 (NSCLC), which contains EGFR, EGFR-TKI, EGFR mutation, lung cancer, erlotinib, etc.; and C2 (targeted therapy), which contains renal cell carcinoma, sunitinib, chemotherapy, cancer, tumor, etc. These 2 major subdirections contain the largest number of keywords and the largest sum of frequencies, which are the main subdirections of TKIs research. The second echelon includes C3 (CML), which contains imatinib, dasatinib, BCR-ABL, nilotinib, gastrointestinal stromal tumor, etc.; C4 (HER2), which contains apoptosis, breast cancer, lapatinib, autophagy, combination therapy, etc.; and C5 (pharmacokinetics), which contains ibrutinib, metabolism, molecular docking, plasma, drug-drug interaction, etc. These 3 communities are involved in research topics that are also important for TKIs research. Keywords from these 3 communities are also important themes in TKIs research. The third echelon only contains C6 (ALK), including crizotinib, Met, RTK, ROS1, and ALK-TKI. C6 is still in its infancy, which occupies only a little weight in TKI research.

    The varying centrality and density of each community further illustrate that there are sharply differentiated subdirections in TKIs research (Table 5). For example, C1 (NSCLC) and C2 (targeted therapy) have the highest centrality, which are the core subdirections in TKIs research. Besides, C1 (NSCLC) and C2 (targeted therapy) have the highest density and are the most developed subdirections in TKI research. Furthermore, the internal density of each community is higher than the density of the whole TKIs co-word network, which also indicates that each subdirection is tightly connected internally but loosely connected to each other. The centrality and density of C6 (ALK) do not have comparative value due to its small size.

    Table 4. Topic communities related to tyrosine kinase inhibitor (TKI) research.
    CommunityWordsa
    C1-64“Non-small cell lung cancer”; “EGFR”; “EGFR-TKI”; “EGFR mutation”; “lung cancer”; “erlotinib”; “osimertinib”; “gefitinib”; “lung adenocarcinoma”; “mutation”; “resistance”; “afatinib”; “drug resistance”; “T790M”; “brain metastasis”; “acquired resistance”; “next-generation sequencing”; “adenocarcinoma”; “T790M mutation”; “circulating tumor DNA”; “icotinib”; “liquid biopsy”; “epithelial-mesenchymal transition”; “EGFR-TKI resistance”; “sequencing”; “small cell lung cancer”; “case report”; “bevacizumab”; “gefitinib resistance”; “pemetrexed”; “squamous cell carcinoma”; “KRAS”; “epidermal growth factor”; “real-world study”; “leptomeningeal metastasis”; “advanced NSCLC”; “cost-effectiveness”; “rebiopsy”; “dacomitinib”; “IGF-1R”; “STAT3”; “droplet digital PCR”; “whole-brain radiotherapy”; “pleural effusion”; “BIM”; “uncommon mutation”; “polymorphism”; “Met amplification”; “first-line treatment”; “EGFR exon 20”; “computed tomography”; “cell-free DNA”; “TP53”; “radiosurgery”; “cisplatin; “docetaxel”; “leptomeningeal carcinomatosis”; “skin rash; “exosomes”; “cerebrospinal fluid”; “exon 19 deletion”; “exon 19”; “cetuximab”; “metformin”
    C2-97“Targeted therapy”; “renal cell carcinoma”; “sunitinib”; “chemotherapy”; “cancer”; tumor”; “metastasis”; “sorafenib”; “prognosis”; “pazopanib”; “hepatocellular carcinoma”; “lenvatinib”; “apatinib”; “immunotherapy”; “toxicity”; “metastatic renal cell carcinoma”; “radiotherapy”; “VEGF”; “progression-free survival”; “biomarker”; “angiogenesis”; “overall survival”; “adverse event”; “carcinoma”; “meta-analysis”; “sarcoma”; “VEGFR2”; “oncology”; “cabozantinib”; “renal cancer”; “VEGFR-TKI”; “PD-L1”; “VEGFR”; “clinical trial”; “axitinib”; “immune checkpoint inhibitor”; “molecular targeted therapy”; “thyroid cancer”; “FGFR”; “PDGFR”; “Phase I clinical trial”; “cardiotoxicity”; “vandetanib”; “regorafenib”; “angiogenesis inhibitor”; “PD-1”; “ovarian cancer”; “colorectal cancer”; “hypertension”; “anlotinib”; “bone metastasis”; “microRNA”; “recurrence”; “soft tissue sarcoma”; “mTOR”; “hypoxia”; “anti-angiogenesis”; “nivolumab”; “prognostic factor”; “AXL”; “RET”; “phase II clinical trial”; “everolimus”; “melanoma”; “anaplastic thyroid cancer”; “differentiated thyroid cancer”; “receptor TKI”; “clear cell renal cell carcinoma”; “medullary thyroid cancer”; “cancer therapy”; “mTOR inhibitor”; “kidney”; “adjuvant”; “tumor microenvironment”; “solid tumor”; “treatment response”; “multitargeted TKI”; “neutrophil-lymphocyte ratio”; “toceranib”; “multikinase inhibitor”; “antiangiogenic therapy”; “monoclonal antibody”; “sequential treatment”; “osteosarcoma”; “neoplasm metastasis”; “tolerability”; “esophageal cancer”; “hypothyroidism”; “circulating tumor cell”; “neoadjuvant therapy”; “Met TKI”; “PDGF”; “paclitaxel”; “neuroblastoma”; “oligoprogression”; “cervical cancer”; “pembrolizumab”
    C3-46“chronic myeloid leukemia”; “imatinib; dasatinib”; “BCR-ABL”; “nilotinib”; “gastrointestinal stromal tumor”; “acute myeloid leukemia”; “leukemia”; “molecular response”; “ponatinib”; “acute lymphoblastic leukemia”; “Philadelphia chromosome”; “TKI resistance”; “FLT3”; “kit”; “quality of life”; “adherence”; “head and neck squamous cell carcinoma”; “imatinib resistance”; “stem cell transplantation”; “leukemia stem cell”; “bosutinib”; “stem cell”; “Ph+ALL”; “treatment-free remission”; “protein kinase inhibitor”; “pulmonary arterial hypertension”; “minimal residual disease”; “PDGFRA”; “discontinuation”; “T315I”; “adverse drug reaction”; “chronic phase”; “cytokine”; “interferon”; “c-Kit”; “midostaurin”; “single nucleotide polymorphism”; “ruxolitinib”; “BCR-ABL mutation”; “kit mutation”; “treatment discontinuation”; “rechallenge”; “Src tyrosine kinase”; “BCR-ABL TKI”; “patient-reported outcome”
    C4-38“HER2”; “apoptosis”; “breast cancer”; “lapatinib”; “autophagy”; “combination therapy”; “neratinib”; “c-Met”; “gastric cancer”; “glioblastoma”; “AKT”; proliferation”; “nanoparticles”; “ERK”; “pancreatic cancer”; “reactive oxygen species”; “cancer stem cell”; “chemoresistance”; “Src”; “hepatotoxicity”; “oxidative stress”; “cell cycle”; “pyrotinib”; “radiation”; “PI3K”; “mitochondria”; “trastuzumab”; “migration”; “NF-kappa B”; “gemcitabine”; “drug delivery”; “glioma”; “triple-negative breast cancer”; “diarrhea”; “adjuvant therapy”; “metastatic breast cancer”; “PI3K/AKT”; “invasion”
    C5-37“Pharmacokinetics”; “ibrutinib”; “metabolism”; “molecular docking”; “plasma”; “drug-drug interaction”; “P-glycoprotein”; “therapeutic drug monitoring”; “Bruton tyrosine kinase”; “BTK inhibitor”; “nintedanib”; “chronic lymphocytic leukemia”; “positron emission tomography”; “LC-MS/MS”; “personalized medicine”; “anticancer”; “lymphoma”; “breast cancer resistance protein”; “interstitial lung disease”; “spleen tyrosine kinase”; “multidrug resistance”; “molecular dynamics”; “antitumor”; “lung”; “inflammation”; “diabetes”; “anticancer drug”; “BCL-2”; “bioavailability”; “synthesis”; “human plasma”; “mantle cell lymphoma”; “idiopathic pulmonary fibrosis”; “virtual screening”; “UPLC-MS/MS”; “pulmonary fibrosis”; “blood-brain barrier”
    C6-14“ALK”; “crizotinib”; “Met”; “receptor tyrosine kinase”; “ROS1”; “ALK-TKI”; “alectinib”; “ALK rearrangement”; “immunohistochemistry”; “lorlatinib”; “BRAF”; “ceritinib”; “resistance mutation”; “brigatinib”

    aKeywords in each community are listed in descending order of frequency.

    Table 5. Indicators of 6 theme communities in tyrosine kinase inhibitor (TKI) research.
    CommunityNumber of nodesNumber of linesTotal frequencyAverage degreeDensity
    C1: Non-small–cell lung cancer64817710661.84380.3989
    C2: Targeted therapy971386462154.95880.2946
    C3: Chronic myeloid leukemia46297233236.36960.2807
    C4: HER238203122644.15790.2812
    C5: Pharmacokinetics37161123035.86490.2352
    C6: ALK145960550.64290.6020
    Visualization of the Correlation Network

    The diverse correlation structure within and between each subdirection of the TKIs research is visualized in Figures 4 and 5. Figure 4 shows that there are differences in the influence of varied subdirections and that the association between these subdirections is uneven. C1 (NSCLC) and C2 (targeted therapy) have the strongest associations with the other subdirections, which reflect the strong influence of these 2 directions in TKIs research. The other four subdirections are oriented to C1 and C2, or depend on them to varying degrees. The association between C1 and C2 is significantly stronger than that between other subdirections, so C1 and C2 are the mainstream of current TKIs research. In particular, C1 is the most central and influential subdirection in the whole TKIs research, and the associations between C1 and other directions are generally strong. Isolated C6 (ALK) is not strongly associated with any other subdirection except for a closer association with C1.

    Figure 5 further shows the correlation structure within each subdirection, where each term has a different location, function, and role. Each subdirection has a clear hierarchy, with the most influential terms at the core, which are important research themes, and the more distant from the core of the community, the less important the terms are. For example, EGFR, EGFR-TKI, EGFR mutation, lung cancer, and erlotinib are important research themes in C1 (NSCLC), which are closely related to the other terms or extended to other themes.

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    Figure 4. Correlation structure of subdirections in tyrosine kinase inhibitor (TKI) research.
    View this figure
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    Figure 5. The internal correlation network structure of each subdirections in tyrosine kinase inhibitor (TKI) research.
    View this figure

    Evolution Patterns of and Trends in TKIs Research

    The Evolutionary Venations of Research Themes

    Figure 6 illustrates the historical evolution of TKIs research themes, which includes both the scale and the clear evolutionary venation of TKIs research themes. Overall, there are clear subdirections and good continuity of TKIs research from 2016 to 2020, but their size and distribution are uneven. In the descending order of size, several major evolutionary venations are the NSCLC venation (involving apoptosis, gefitinib, osimertinib, EGFR-TKI, T790M mutation, etc.), the CML venation (involving BCR-ABL, dasatinib, nilotinib, etc.), the renal cell carcinoma venation (involving sunitinib, angiogenesis, sorafenib, regorafenib, pazopanib, immunotherapy, etc.), the chronic lymphocytic leukemia venation (involving lymphoma, Bruton tyrosine kinase, ibrutinib, BTK inhibitor, etc.), and the lapatinib venation (involving breast cancer, next-generation sequencing, ALK rearrangement, ALK, alectinib, etc.). There are also isolated, intermittent research themes, such as crizotinib and ALK in 2016, apoptosis and PI3K/AKT in 2017, molecular docking and anticancer in 2019, and cabozantinib and AXL in 2020 for the first time as a subdirection.

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    Figure 6. The evolution of themes of TKIs research over time (2016-2020). BTK: Bruton tyrosine kinase; EGFR: epidermal growth factor receptor; LC: liquid chromatography; MS/MS: tandem mass spectrometry; TKI: tyrosine kinase inhibitor; VEGFR: vascular endothelial growth factor receptor.
    View this figure
    The Developmentary Degree of the Subdirections of TKIs Research

    Based on Table 5, we drew a strategic diagram (Figure 7) to visualize the developmentary trends of the subdirections in TKIs research. The C6 community was not drawn in the strategy diagram due to its small size and noncomparable network indicators. As shown in Figure 7, we plotted the nodes of different sizes to represent the total frequencies of the varied subdirections of TKIs research and distributed them in 4 quadrants according to their density and centrality. C1 (NSCLC) is in the first quadrant due to its high density and centrality, again indicating that this community is the core direction and most developed in TKIs research. C2 (targeted therapy) is located exactly at the junction of the first and fourth quadrants, which means that C2 is also the core direction of TKIs research, but is in the process of maturing. C3 (CML), C4 (HER2), and C5 (pharmacokinetics) are all in the third quadrant, with relatively low centrality and density, which indicates that they are at the margins of TKIs research and are immature.

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    Figure 7. The relative development status and trends of 5 subdirections in the strategic diagram.
    View this figure

    Principal Findings

    With the continuous progress of medical field, the targeted drugs (ie, TKIs) have made great development in basic research and have been widely used for clinical applications. Based on the results of the co-word analysis, we have a better understanding of the main research directions of TKIs and thus can accurately assess their maturity, centrality, and interactions. First, in general, TKIs research is unbalanced. The 296 words we selected from 6782 words accounted for 65.41% of the total word frequency and have a greater impact. Since the first TKIs were introduced, researchers have focused on some hot terms such as NSCLC, EGFR, CML, EGFR-TKI, EGFR mutation, erlotinib, imatinib, osimertinib, gefitinib, targeted therapy, renal cell carcinoma, resistance. These terms can be broadly classified into the following categories: clinical applications (eg, NSCLC, CML, renal cell carcinoma, lung adenocarcinoma), genetic studies (eg, EGFR, EGFR mutation, BCR-ABL), typical drugs (eg, erlotinib, imatinib, gefitinib, sunitinib, afatinib, dasatinib, osimertinib, nilotinib), and chemotherapy and drug resistance (eg, targeted therapy, resistance, chemotherapy, drug resistance). These terms not only reflect the areas of interest of researchers but also indicate the trends of TKIs research.

    Based on the visualized co-word network, we found that the research topics tend to be clustered around a few keywords, eventually forming a hierarchical and relatively balanced thematic community. The thematic communities of the TKIs consist of C1 (NSCLC), C2 (targeted therapy), C3 (CML), C4 (HER2), C5 (pharmacokinetics), and C6 (ALK). There is also an imbalance between communities. First, the C1 and C2 communities are the main areas of TKIs research because of their high centrality and frequency. Among them, C1 (NSCLC), the largest thematic community, has received a lot of attention from scholars. From gene targets (eg, KRAS, EGFR, TP53, BIM) to signaling pathways (eg, IGF-1R, STAT3), from conventional chemotherapy (eg, cisplatin, docetaxel, pemetrexed) to targeted drugs (eg, erlotinib osimertinib, gefitinib), scholars have studied NSCLC in increasing depth. C2 (targeted therapy) indicates that TKIs are widely used in targeted therapies, and the application of TKIs has been extended to lung cancer [12,31,32], breast cancer [62], renal cancer [63,64], liver cancer [65], ovarian cancer [66], colorectal cancer [67], leukemia [11,28,29], thyroid cancer [68,69], cervical cancer [70], and many other tumors. Chemotherapy regimens containing TKIs were effective in reducing tumor metastasis and recurrence and improving the overall survival of patients [12,31]. The use of TKIs will be further expanded to more tumor types as more clinical trials are conducted.

    Second, C3 and C5 communities have declined during the development of TKIs, both of which are in a marginal position in the strategic diagram. Among C3 (CML), imatinib is the first targeted antitumor drug that was first approved by the FDA in 2001 for patients with BCR-ABL–positive CML [10,11,28,29,71]. With the emergence of drug resistance, dasatinib [72], which targets the SRC, and nilotinib [73], which targets BCR-ABL, have been applied to patients who are resistant. Research on CML has been conducted for a long time and this field is now mature, so the application of TKIs has gradually expanded from CML to other diseases, which is leading to the gradual marginalization of the C3 community. C5 (pharmacokinetics) is an important interdisciplinary discipline related to TKIs, which is widely involved in the development process of TKIs [74,75]. However, as small-molecule drugs, the absorption, transport, distribution, and transformation of most TKIs in vivo have been clearly studied. Meanwhile, several new technologies in molecular biology (eg, molecular docking [76] and virtual high-throughput screening [77]) are used increasingly more, so C5 (pharmacokinetics) is gradually fading.

    Despite the gradual decline of the C3 and C5 communities, new research areas such as C4 (HER) and C6 (ALK) have flourished in recent years. The overexpression, amplification, and mutations of HER2 have been found in a variety of tumors including breast cancer and NSCLC [45], and targeting HER2 has achieved excellent efficacy in breast cancer [62]. Although several early drugs targeting HER2 had poor efficacy in NSCLC [34,36,45], the advent of newer-generation HER2-targeting drugs such as poziotinib [78] and pyrotinib [79,80] exhibited good antitumor effects in clinical trials. Scholars are increasingly interested in targeting HER2 in NSCLC, while research on HER2 for breast cancer is relatively well established. Therefore, C4 may evolve in different directions in the future. The C6 (ALK) community is small but promising. ALK mutations, especially rearrangements, exhibit strong translational activity in NSCLC [81]. ALK-targeted agents such as alectinib [82] and brigatinib [83] have shown extraordinary efficacy in ALK-positive NSCLC and have become the first-line therapies [84]. Lorlatinib, an ALK inhibitor [85], appears as a burst word in 2020, and ALK-TKI and ALK rearrangement have a high weight from 2019 to 2020 (Figure 3), both of which indicate the rapidly rising attention on ALK. In addition, there are still several ALK-targeted drugs in development, so more literature on ALK will be published in the future and the C6 community will grow further.

    We found several evolutionary venations by analyzing the evolution of themes of TKIs research over time. These highly concentrated evolutionary venations indicate scholars’ continuous and steady focus on NSCLC, CML, renal cell carcinoma, chronic lymphocytic leukemia, etc. These different evolutionary lines show a clear development path: TKIs research is disease focused and revolved around “gene targets/targeted drugs/resistance mechanisms.” For example, in the CML venation, investigators focused on the BRC-ABL in 2016 and on dasatinib and nilotinib in 2017-2020, which could both target BCR-ABL and overcome imatinib resistance [72,73]. In the NSCLC venation, investigators focused on EGFR genes in 2016, on EGFR-TKIs represented by osimertinib in 2017-2019, and on resistance mechanisms represented by T790M in 2020. In the renal cell carcinoma venation, investigators continued to focus on various TKIs such as sunitinib, sorafenib, regorafenib, and pazopanib, and also paid attention to gene targets such as PDGFR, FGFR, c-Kit, and VEGF, and multidrug resistance. In the chronic lymphocytic leukemia venation, investigators focused on B-cell–derived chronic lymphocytic leukemia and lymphoma in 2016, on the aberrant Bruton tyrosine kinase (BTK) from B cells in 2017, and on BTK inhibitors such as ibrutinib that can treat chronic lymphocytic leukemia and lymphoma in 2018-2020. Moreover, lapatinib, a dual EGFR/HER2 TKI [86], showed good efficacy in breast cancer, which made it an independent evolutionary venation in 2017 with continuous attention to date.

    In addition to the main few evolutionary venations, we identified some isolated themes that depict the current state of TKIs research. Crizotinib gained attention as an ALK inhibitor in 2016, but its popularity declined rapidly due to its poor efficacy and the emergence of second-generation ALK inhibitors, making crizotinib and ALK an isolated topic. Circulating tumor DNA is important for efficacy assessment and prognosis analysis of tumors. The future trend in TKIs research will likely be to use next-generation sequencing or liquid biopsy technology to precisely analyze circulating tumor DNA in cell-free DNA. The PI3K/AKT pathway plays an important role in cell growth, proliferation, migration, and angiogenesis, which can be activated by RTKs. Thus, as one of the mechanisms of TKIs, the apoptosis and PI3K/AKT venation was noticed in 2017-2018. Cabozantinib is a multitarget TKI that can target 9 genes (eg, AXL, Met) [87], and cabozantinib and AXL appeared as a separate topic in 2020. Furthermore, the burst words pyrotinib and anlotinib, which accounted for a relatively large weight in 2020, are multitarget inhibitors [79,80,88]. This suggests that multitarget drugs may become an important direction for the development of TKIs and will likely receive more attention in the future. Immune checkpoint inhibitors and TKIs are important drugs for tumors. The combination of PD-L1 inhibitors (eg, pembrolizumab) and TKIs (eg, lenvatinib) in patients with malignant tumors was more effective than single drug [89], suggesting that the combination therapy was an important development direction for future tumor therapy. For well-known reasons, many patients being treated with TKIs were co-infected with SARS-CoV-2 in 2020 [90], and it was also suggested that some TKIs such as BTKs may have therapeutic effects on COVID-19 [91], which made COVID-19 a burst word in TKIs research. Because of global spread of COVID-19 in 2021, investigators’ interest in TKIs for patients infected with SARS-CoV-2 would further increase.

    Limitation

    Our study also has some limitations: first, our search included only English literature from 2016 to 2020, while non-English literature was excluded; second, the co-word analysis did not take the quality, influence, and rigor of the literature into account, which was a common shortcoming of such papers [92-94].

    Conclusions

    In conclusion, we presented a visualization of TKIs research during 2016-2020 utilizing co-word analysis and the hotspots, knowledge structure, and trends of evolution revealed in our work will help researchers in the field of TKIs to gain a comprehensive understanding of the current status and trends. Based on the above results, we speculate that the general status of TKIs research is as follows: (1) NSCLC and CML are the most important clinical application areas for TKIs; (2) EGFR is the most common target gene for TKIs, and EGFR-TKIs are the most commonly used molecularly targeted TKIs, among which erlotinib, osimertinib, and gefitinib have gradually matured; (3) TKIs have become a mature field for targeted therapeutic applications, and drugs targeting HER2 and ALK have further expanded the application of TKIs; and (4) drug resistance remains a major challenge for TKIs. In a nutshell, our work remains valuable in revealing the knowledge structure and evolutionary trends of TKIs research.

    Acknowledgments

    This study was supported by grants from the Natural Science Foundation of Hubei Province (Grant No. 2020CFB611), the National Natural Science Foundation of China Funded Project (No. 71874125), and the Young Top-notch Talent Cultivation Program of Hubei Province.

    Authors' Contributions

    JH, ML, and ZW designed this research; ML and JH developed the search strategy and screened the literature; KX, ML, and JH screened and processed the keywords; JH and YZ participated in the data visualization and made the images and tables; ML, JH, and KX wrote the manuscript; KX translated the manuscript; ML and ZW reviewed and revised the manuscript; JH, KX, and YZ are common first authors. Authors ML (liumiao915@whu.edu.cn) and ZW (wangzhiwei@whu.edu.cn) are co-corresponding authors for this article.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Top 100 keywords in papers related to research.

    DOCX File , 24 KB
    1. Jo DH, Kim JH, Kim JH. Targeting tyrosine kinases for treatment of ocular tumors. Arch Pharm Res 2019 Apr 23;42(4):305-318. [CrossRef] [Medline]
    2. Du Z, Lovly CM. Mechanisms of receptor tyrosine kinase activation in cancer. Mol Cancer 2018 Feb 19;17(1):58 [FREE Full text] [CrossRef] [Medline]
    3. Siveen KS, Prabhu KS, Achkar IW, Kuttikrishnan S, Shyam S, Khan AQ, et al. Role of non receptor tyrosine kinases in hematological malignances and its targeting by natural products. Mol Cancer 2018 Feb 19;17(1):31 [FREE Full text] [CrossRef] [Medline]
    4. An Z, Aksoy O, Zheng T, Fan Q, Weiss WA. Epidermal growth factor receptor and EGFRvIII in glioblastoma: signaling pathways and targeted therapies. Oncogene 2018 Mar 11;37(12):1561-1575 [FREE Full text] [CrossRef] [Medline]
    5. Ma Z, Yu Y, Badea CT, Kovacs JJ, Xiong X, Comhair S, et al. Vascular endothelial growth factor receptor 3 regulates endothelial function through β-arrestin 1. Circulation 2019 Mar 26;139(13):1629-1642 [FREE Full text] [CrossRef] [Medline]
    6. Loscocco F, Visani G, Galimberti S, Curti A, Isidori A. BCR-ABL independent mechanisms of resistance in chronic myeloid leukemia. Front Oncol 2019;9:939 [FREE Full text] [CrossRef] [Medline]
    7. Szilveszter KP, Németh T, Mócsai A. Tyrosine kinases in autoimmune and inflammatory skin diseases. Front Immunol 2019;10:1862 [FREE Full text] [CrossRef] [Medline]
    8. Morris TA, Khoo C, Solomon BJ. Targeting ROS1 rearrangements in non-small cell lung cancer: crizotinib and newer generation tyrosine kinase inhibitors. Drugs 2019 Aug;79(12):1277-1286. [CrossRef] [Medline]
    9. Zeng P, Schmaier A. Ponatinib and other CML tyrosine kinase inhibitors in thrombosis. Int J Mol Sci 2020 Sep 08;21(18):6556 [FREE Full text] [CrossRef] [Medline]
    10. Radich JP, Deininger M, Abboud CN, Altman JK, Berman E, Bhatia R, et al. Chronic myeloid leukemia, version 1.2019, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2018 Sep 04;16(9):1108-1135. [CrossRef] [Medline]
    11. Pallera A, Altman JK, Berman E, Abboud CN, Bhatnagar B, Curtin P, et al. NCCN Guidelines insights: chronic myeloid leukemia, version 1.2017. J Natl Compr Canc Netw 2016 Dec 12;14(12):1505-1512. [CrossRef] [Medline]
    12. Ettinger D, Wood D, Aggarwal C, Aisner D, Akerley W, Bauman J, OCN, et al. NCCN Guidelines insights: non-small cell lung cancer, version 1.2020. J Natl Compr Canc Netw 2019 Dec;17(12):1464-1472. [CrossRef] [Medline]
    13. Wang S, Song Y, Liu D. EAI045: The fourth-generation EGFR inhibitor overcoming T790M and C797S resistance. Cancer Lett 2017 Jan 28;385:51-54. [CrossRef] [Medline]
    14. Tan C, Kumarakulasinghe NB, Huang Y, Ang YLE, Choo JR, Goh B, et al. Third generation EGFR TKIs: current data and future directions. Mol Cancer 2018 Feb 19;17(1):29-29 [FREE Full text] [CrossRef] [Medline]
    15. Remon J, Steuer C, Ramalingam S, Felip E. Osimertinib and other third-generation EGFR TKI in EGFR-mutant NSCLC patients. Ann Oncol 2018 Jan 01;29(suppl_1):i20-i27 [FREE Full text] [CrossRef] [Medline]
    16. Lyu X, Hu J, Dong W, Xu X. Intellectual structure and evolutionary trends of precision medicine research: coword analysis. JMIR Med Inform 2020 Feb 04;8(2):e11287 [FREE Full text] [CrossRef] [Medline]
    17. Wei W, Shi B, Guan X, Ma J, Wang Y, Liu J. Mapping theme trends and knowledge structures for human neural stem cells: a quantitative and co-word biclustering analysis for the 2013-2018 period. Neural Regen Res 2019 Oct;14(10):1823-1832 [FREE Full text] [CrossRef] [Medline]
    18. Lu K, Yu S, Yu M, Sun D, Huang Z, Xing H, et al. Bibliometric analysis of tumor immunotherapy studies. Med Sci Monit 2018 May 23;24:3405-3414 [FREE Full text] [CrossRef] [Medline]
    19. Huang J, Tang J, Qu Y, Zhang L, Zhou Y, Bao S, et al. Mapping the knowledge structure of neonatal hypoxic-ischemic wncephalopathy over the past decade: a co-word analysis based on keywords. J Child Neurol 2016 May 10;31(6):797-803. [CrossRef] [Medline]
    20. An XY, Wu QQ. Co-word analysis of the trends in stem cells field based on subject heading weighting. Scientometrics 2011 Apr 5;88(1):133-144. [CrossRef]
    21. Wei W, Ge J, Xu S, Li M, Zhao Z, Li X, et al. Knowledge maps of disaster medicine in China based on co-word analysis. Disaster Med Public Health Prep 2019 Jun 23;13(3):405-409. [CrossRef] [Medline]
    22. Hsu W, Li J. Visualising and mapping the intellectual structure of medical big data. Journal of Information Science 2018 Jul 16;45(2):239-258. [CrossRef]
    23. Shen L, Wang S, Dai W, Zhang Z. Detecting the interdisciplinary nature and topic hotspots of robotics in surgery: social network analysis and bibliometric study. J Med Internet Res 2019 Mar 26;21(3):e12625 [FREE Full text] [CrossRef] [Medline]
    24. Gan J, Cai Q, Galer P, Ma D, Chen X, Huang J, et al. Mapping the knowledge structure and trends of epilepsy genetics over the past decade: A co-word analysis based on medical subject headings terms. Medicine (Baltimore) 2019 Aug;98(32):e16782 [FREE Full text] [CrossRef] [Medline]
    25. Lee YT, Tan YJ, Oon CE. Molecular targeted therapy: Treating cancer with specificity. Eur J Pharmacol 2018 Sep 05;834:188-196. [CrossRef] [Medline]
    26. Krajewski KM, Braschi-Amirfarzan M, DiPiro PJ, Jagannathan JP, Shinagare AB. Molecular targeted therapy in modern oncology: imaging assessment of treatment response and toxicities. Korean J Radiol 2017;18(1):28-41 [FREE Full text] [CrossRef] [Medline]
    27. Claudiani S, Apperley JF. The argument for using imatinib in CML. Hematology Am Soc Hematol Educ Program 2018 Nov 30;2018(1):161-167 [FREE Full text] [CrossRef] [Medline]
    28. O'Brien S, Radich JP, Abboud CN, Akhtari M, Altman JK, Berman E, et al. Chronic myelogenous leukemia, version 1.2015. J Natl Compr Canc Netw 2014 Nov 31;12(11):1590-1610. [CrossRef] [Medline]
    29. O'Brien S, Radich JP, Abboud CN, Akhtari M, Altman JK, Berman E, Ntational comprehensive cancer network. Chronic Myelogenous Leukemia, Version 1.2014. J Natl Compr Canc Netw 2013 Nov 13;11(11):1327-1340 [FREE Full text] [CrossRef] [Medline]
    30. Rieder D, Finotello F. Analysis of high-throughput RNA bisulfite sequencing data. Methods Mol Biol 2017;1562:143-154. [CrossRef] [Medline]
    31. Hanna N, Johnson D, Temin S, Baker S, Brahmer J, Ellis PM, et al. Systemic therapy for stage IV non-small-cell lung cancer: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol 2017 Oct 20;35(30):3484-3515. [CrossRef] [Medline]
    32. Novello S, Barlesi F, Califano R, Cufer T, Ekman S, Levra MG, ESMO Guidelines Committee. Metastatic non-small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2016 Sep;27(suppl 5):v1-v27. [CrossRef] [Medline]
    33. Soria J, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee KH, FLAURA Investigators. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N Engl J Med 2018 Dec 11;378(2):113-125. [CrossRef] [Medline]
    34. Li BT, Shen R, Buonocore D, Olah ZT, Ni A, Ginsberg MS, et al. Ado-trastuzumab emtansine for patients with HER2-mutant lung cancers: results from a phase II basket trial. JCO 2018 Aug 20;36(24):2532-2537. [CrossRef]
    35. Mazières J, Barlesi F, Filleron T, Besse B, Monnet I, Beau-Faller M, et al. Lung cancer patients with HER2 mutations treated with chemotherapy and HER2-targeted drugs: results from the European EUHER2 cohort. Ann Oncol 2016 Feb;27(2):281-286 [FREE Full text] [CrossRef] [Medline]
    36. Heymach J, Negrao M, Robichaux J, Carter B, Patel A, Altan M, et al. OA02.06 a phase II trial of poziotinib in EGFR and HER2 exon 20 mutant non-small cell lung cancer (NSCLC). Journal of Thoracic Oncology 2018 Oct;13(10):S323-S324 [FREE Full text] [CrossRef]
    37. Soria J, Tan DSW, Chiari R, Wu Y, Paz-Ares L, Wolf J, et al. First-line ceritinib versus platinum-based chemotherapy in advanced ALK-rearranged non-small-cell lung cancer (ASCEND-4): a randomised, open-label, phase 3 study. Lancet 2017 Mar 04;389(10072):917-929. [CrossRef] [Medline]
    38. Baccarani M, Druker BJ, Branford S, Kim DW, Pane F, Mongay L, et al. Long-term response to imatinib is not affected by the initial dose in patients with Philadelphia chromosome-positive chronic myeloid leukemia in chronic phase: final update from the Tyrosine Kinase Inhibitor Optimization and Selectivity (TOPS) study. Int J Hematol 2014;99(5):616-624. [CrossRef] [Medline]
    39. Kobayashi S, Boggon TJ, Dayaram T, Jänne PA, Kocher O, Meyerson M, et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med 2005 Feb 24;352(8):786-792. [CrossRef] [Medline]
    40. Wu S, Liu Y, Tsai M, Chang Y, Yu C, Yang P, et al. The mechanism of acquired resistance to irreversible EGFR tyrosine kinase inhibitor-afatinib in lung adenocarcinoma patients. Oncotarget 2016 Mar 15;7(11):12404-12413 [FREE Full text] [CrossRef] [Medline]
    41. Balak MN, Gong Y, Riely GJ, Somwar R, Li AR, Zakowski MF, et al. Novel D761Y and common secondary T790M mutations in epidermal growth factor receptor-mutant lung adenocarcinomas with acquired resistance to kinase inhibitors. Clin Cancer Res 2006 Nov 01;12(21):6494-6501. [CrossRef] [Medline]
    42. Yamaguchi F, Fukuchi K, Yamazaki Y, Takayasu H, Tazawa S, Tateno H, et al. Acquired resistance L747S mutation in an epidermal growth factor receptor-tyrosine kinase inhibitor-naïve patient: A report of three cases. Oncol Lett 2014 Feb;7(2):357-360 [FREE Full text] [CrossRef] [Medline]
    43. Steins M, Thomas M, Geißler M. Erlotinib. Recent Results Cancer Res 2018;211:1-17. [CrossRef] [Medline]
    44. Rawluk J, Waller C. Gefitinib. Recent Results Cancer Res 2018;211:235-246. [CrossRef] [Medline]
    45. Baraibar I, Mezquita L, Gil-Bazo I, Planchard D. Novel drugs targeting EGFR and HER2 exon 20 mutations in metastatic NSCLC. Crit Rev Oncol Hematol 2020 Apr;148:102906 [FREE Full text] [CrossRef] [Medline]
    46. Du X, Shao Y, Qin H, Tai Y, Gao H. ALK-rearrangement in non-small-cell lung cancer (NSCLC). Thorac Cancer 2018 Apr 28;9(4):423-430 [FREE Full text] [CrossRef] [Medline]
    47. Dhillon S. Regorafenib: a review in metastatic colorectal cancer. Drugs 2018 Jul 26;78(11):1133-1144. [CrossRef] [Medline]
    48. Hu J, Zhang Y. Research patterns and trends of Recommendation System in China using co-word analysis. Information Processing & Management 2015 Jul;51(4):329-339. [CrossRef]
    49. Callon M, Courtial J, Turner WA, Bauin S. From translations to problematic networks: An introduction to co-word analysis. Social Science Information 2016 Sep 03;22(2):191-235. [CrossRef]
    50. Callon M, Courtial JP, Laville F. Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics 1991 Sep;22(1):155-205. [CrossRef]
    51. Alcaide–Muñoz L, Rodríguez–Bolívar MP, Cobo MJ, Herrera–Viedma E. Analysing the scientific evolution of e-Government using a science mapping approach. Government Information Quarterly 2017 Sep;34(3):545-555. [CrossRef]
    52. Börner K. Plug-and-play macroscopes. Commun. ACM 2011 Mar 01;54(3):60-69. [CrossRef]
    53. Doreian P, Lloyd P, Mrvar A. Partitioning large signed two-mode networks: Problems and prospects. Social Networks 2013 May;35(2):178-203. [CrossRef]
    54. Freeman LC. Centrality in social networks conceptual clarification. Social Networks 1978 Jan;1(3):215-239. [CrossRef]
    55. Yan E, Ding Y, Zhu Q. Mapping library and information science in China: a coauthorship network analysis. Scientometrics 2009 Jun 19;83(1):115-131. [CrossRef]
    56. Leydesdorff L, Park HW, Wagner C. International coauthorship relations in the Social Sciences Citation Index: Is internationalization leading the Network? J Assn Inf Sci Tec 2014 Mar 10;65(10):2111-2126. [CrossRef]
    57. Blondel VD, Guillaume J, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J. Stat. Mech 2008 Oct 09;2008(10):P10008. [CrossRef]
    58. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2009 Dec 31;84(2):523-538. [CrossRef]
    59. Rosvall M, Bergstrom CT. Mapping change in large networks. PLoS One 2010 Jan 27;5(1):e8694 [FREE Full text] [CrossRef] [Medline]
    60. Kleinberg J. Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery 2003 Oct;7(4):373-397. [CrossRef]
    61. Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys. Rev. E 2004 Feb 26;69(2):026113-002628. [CrossRef]
    62. Wuerstlein R, Harbeck N. Neoadjuvant therapy for HER2-positive breast cancer. Rev Recent Clin Trials 2017 May 09;12(2):81-92. [CrossRef] [Medline]
    63. Shah A, Kotecha R, Lemke E, Chandramohan A, Chaim J, Msaouel P, et al. Outcomes of patients with metastatic clear-cell renal cell carcinoma treated with second-line VEGFR-TKI after first-line immune checkpoint inhibitors. Eur J Cancer 2019 Jun;114:67-75 [FREE Full text] [CrossRef] [Medline]
    64. Tannir NM, Pal SK, Atkins MB. Second-line treatment landscape for renal cell carcinoma: a comprehensive review. Oncologist 2018 May 27;23(5):540-555 [FREE Full text] [CrossRef] [Medline]
    65. Jindal A, Thadi A, Shailubhai K. Hepatocellular carcinoma: etiology and current and future drugs. J Clin Exp Hepatol 2019 Mar;9(2):221-232 [FREE Full text] [CrossRef] [Medline]
    66. Liu J, Nicum S, Reichardt P, Croitoru K, Illek B, Schmidinger M, et al. Assessment and management of diarrhea following VEGF receptor TKI treatment in patients with ovarian cancer. Gynecol Oncol 2018 Jul;150(1):173-179. [CrossRef] [Medline]
    67. Kircher SM, Nimeiri HS, Benson AB. Targeting angiogenesis in colorectal cancer: tyrosine kinase inhibitors. Cancer J 2016;22(3):182-189. [CrossRef] [Medline]
    68. Ferrari SM, Centanni M, Virili C, Miccoli M, Ferrari P, Ruffilli I, et al. Sunitinib in the treatment of thyroid cancer. Curr Med Chem 2019 May 13;26(6):963-972. [CrossRef] [Medline]
    69. Porcelli T, Sessa F, Luongo C, Salvatore D. Local ablative therapy of oligoprogressive TKI-treated thyroid cancer. J Endocrinol Invest 2019 Aug 9;42(8):871-879. [CrossRef] [Medline]
    70. Lv Y, Cang W, Li Q, Liao X, Zhan M, Deng H, et al. Erlotinib overcomes paclitaxel-resistant cancer stem cells by blocking the EGFR-CREB/GRβ-IL-6 axis in MUC1-positive cervical cancer. Oncogenesis 2019 Nov 26;8(12):70 [FREE Full text] [CrossRef] [Medline]
    71. Waller C. Imatinib mesylate. Recent Results Cancer Res 2018;212:1-27. [CrossRef] [Medline]
    72. McCafferty EH, Dhillon S, Deeks ED. Dasatinib: a review in pediatric chronic myeloid leukemia. Paediatr Drugs 2018 Dec 22;20(6):593-600. [CrossRef] [Medline]
    73. Sacha T, Saglio G. Nilotinib in the treatment of chronic myeloid leukemia. Future Oncol 2019 Mar;15(9):953-965. [CrossRef] [Medline]
    74. Suttorp M, Bornhäuser M, Metzler M, Millot F, Schleyer E. Pharmacology and pharmacokinetics of imatinib in pediatric patients. Expert Rev Clin Pharmacol 2018 Mar 06;11(3):219-231. [CrossRef] [Medline]
    75. Kucharczuk CR, Ganetsky A, Vozniak JM. Drug-drug interactions, safety, and pharmacokinetics of EGFR tyrosine kinase inhibitors for the treatment of non-small cell lung cancer. J Adv Pract Oncol 2018 Mar;9(2):189-200 [FREE Full text] [Medline]
    76. Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci 2019 Sep 04;20(18):4331 [FREE Full text] [CrossRef] [Medline]
    77. Geromichalos GD, Alifieris CE, Geromichalou EG, Trafalis DT. Overview on the current status on virtual high-throughput screening and combinatorial chemistry approaches in multi-target anticancer drug discovery; Part II. J BUON 2016;21(6):1337-1358 [FREE Full text] [Medline]
    78. Robichaux J, Elamin Y, Vijayan R, Nilsson M, Hu L, He J, et al. Pan-cancer landscape and analysis of ERBB2 mutations identifies poziotinib as a clinically active inhibitor and enhancer of T-DM1 activity. Cancer Cell 2019 Oct 14;36(4):444-457.e7 [FREE Full text] [CrossRef] [Medline]
    79. Wang Y, Jiang T, Qin Z, Jiang J, Wang Q, Yang S, et al. HER2 exon 20 insertions in non-small-cell lung cancer are sensitive to the irreversible pan-HER receptor tyrosine kinase inhibitor pyrotinib. Ann Oncol 2019 Mar 01;30(3):447-455 [FREE Full text] [CrossRef] [Medline]
    80. Ma F, Ouyang Q, Li W, Jiang Z, Tong Z, Liu Y, et al. Pyrotinib or lapatinib combined with capecitabine in HER2-positive metastatic breast cancer with prior taxanes, anthracyclines, and/or trastuzumab: a randomized, phase II study. J Clin Oncol 2019 Oct 10;37(29):2610-2619. [CrossRef] [Medline]
    81. Soda M, Isobe K, Inoue A, Maemondo M, Oizumi S, Fujita Y, et al. A prospective PCR-based screening for the EML4-ALK oncogene in non-small cell lung cancer. Clin Cancer Res 2012 Aug 20;18(20):5682-5689. [CrossRef]
    82. Herden M, Waller C. Alectinib. Recent Results Cancer Res 2018;211:247-256. [CrossRef] [Medline]
    83. Spencer SA, Riley AC, Matthew A, Di Pasqua AJ. Brigatinib: novel ALK inhibitor for non-small-cell lung cancer. Ann Pharmacother 2019 Jun 13;53(6):621-626. [CrossRef] [Medline]
    84. Elsayed M, Christopoulos P. Therapeutic sequencing in ALK NSCLC. Pharmaceuticals (Basel) 2021 Jan 21;14(2):80 [FREE Full text] [CrossRef] [Medline]
    85. Shaw AT, Bauer TM, de Marinis F, Felip E, Goto Y, Liu G, CROWN Trial Investigators. First-line lorlatinib or crizotinib in advanced -positive lung cancer. N Engl J Med 2020 Nov 19;383(21):2018-2029. [CrossRef] [Medline]
    86. Voigtlaender M, Schneider-Merck T, Trepel M. Lapatinib. Recent Results Cancer Res 2018;211:19-44. [CrossRef] [Medline]
    87. Deeks ED. Cabozantinib: a review in advanced hepatocellular carcinoma. Target Oncol 2019 Feb 14;14(1):107-113. [CrossRef] [Medline]
    88. Shen G, Zheng F, Ren D, Du F, Dong Q, Wang Z, et al. Anlotinib: a novel multi-targeting tyrosine kinase inhibitor in clinical development. J Hematol Oncol 2018 Sep 19;11(1):120 [FREE Full text] [CrossRef] [Medline]
    89. Taylor MH, Lee C, Makker V, Rasco D, Dutcus CE, Wu J, et al. Phase IB/II trial of lenvatinib plus pembrolizumab in patients with advanced renal cell carcinoma, endometrial cancer, and other selected advanced solid tumors. JCO 2020 Apr 10;38(11):1154-1163. [CrossRef]
    90. Başcı S, Ata N, Altuntaş F, Yiğenoğlu TN, Dal MS, Korkmaz S, Turkish Ministry of Health‚ Hematology Scientific Working Group. Outcome of COVID-19 in patients with chronic myeloid leukemia receiving tyrosine kinase inhibitors. J Oncol Pharm Pract 2020 Oct;26(7):1676-1682 [FREE Full text] [CrossRef] [Medline]
    91. McGee MC, August A, Huang W. BTK/ITK dual inhibitors: Modulating immunopathology and lymphopenia for COVID‐19 therapy. J Leukoc Biol 2020 Jul 08;109(1):49-53. [CrossRef]
    92. Yao R, Ren C, Wang J, Wu G, Zhu X, Xia Z, et al. Publication trends of research on sepsis and host immune response during 1999-2019: a 20-year bibliometric analysis. Int J Biol Sci 2020;16(1):27-37 [FREE Full text] [CrossRef] [Medline]
    93. Lei F, Ye J, Wang J, Xia Z. A bibliometric analysis of publications on oxycodone from 1998 to 2017. Biomed Res Int 2019 Oct 31;2019:9096201-9096209 [FREE Full text] [CrossRef] [Medline]
    94. Zyoud SH, Smale S, Waring WS, Sweileh WM, Al-Jabi SW. Global research trends in microbiome-gut-brain axis during 2009-2018: a bibliometric and visualized study. BMC Gastroenterol 2019 Aug 30;19(1):158 [FREE Full text] [CrossRef] [Medline]

    ‎
    ATP: adenosine triphosphate
    BTK: Bruton tyrosine kinase
    ctDNA: circulating tumor DNA
    CML: chronic myeloid leukemia
    EGFR: epidermal growth factor receptor
    FDA: Food and Drug Administration
    NSCLC: non-small small–cell lung cancer
    RTKs: receptor tyrosine kinases
    TKIs: tyrosine kinase inhibitors
    TKs: tyrosine kinases
    VEGFR: vascular endothelial growth factor receptor
    WOSCC: Web of Science Core Collection

    Edited by C Lovis; submitted 29.10.21; peer-reviewed by K Fultz Hollis, P Zarogoulidis; comments to author 02.01.22; revised version received 05.01.22; accepted 08.01.22; published 08.04.22

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    ©Jiming Hu, Kai Xing, Yan Zhang, Miao Liu, Zhiwei Wang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 08.04.2022.

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