Introduction
Acute lymphoblastic leukemia (ALL) is an aggressive hematological malignancy characterized by the uncontrolled proliferation of immature lymphoid cells in the bone marrow, peripheral blood, and extramedullary sites. 1 It represents approximately 25% of childhood cancers and accounts for 75%–80% of pediatric leukemias, with a second incidence peak in adults over 50 y. 2 Conventional treatment approaches involve multi-agent chemotherapy regimens, potentially supplemented with radiation therapy and allogeneic hematopoietic stem cell transplantation (HSCT) for high-risk patients. 3 While pediatric ALL has seen remarkable improvements in outcomes with 5-y overall survival rates exceeding 90%, adult ALL continues to have a more challenging prognosis with survival rates of 30%–40%. 4 Patients with relapsed or refractory disease face particularly poor outcomes, with 5-y survival rates below 30%. 5 This therapeutic challenge has driven the exploration of novel targeted approaches, including immunotherapies, to overcome chemoresistance and improve long-term outcomes while reducing treatment-related toxicities. 6
Chimeric antigen receptor T-cell (CAR-T) therapy represents a groundbreaking advancement in cancer immunotherapy for hematological malignancies. 7 This approach involves genetically engineering a patient’s T cells to express synthetic receptors capable of recognizing specific tumor-associated antigens, most commonly CD19 for B-cell malignancies. 8 Following ex vivo expansion, these modified T cells are reinfused into the patient to specifically target and eliminate cancer cells expressing the target antigen. 9 The remarkable efficacy of CD19-targeted CAR-T therapy in B-cell ALL was first demonstrated in clinical trials, reporting unprecedented complete remission rates of 70%–90% in heavily pretreated patients. 10 These promising results led to the FDA approval of tisagenlecleucel in 2017 for pediatric and young adult patients with relapsed or refractory B-cell ALL. 11 Since then, research has expanded to focus on enhancing efficacy, mitigating toxicities, and exploring next-generation CAR designs, alternative targets, and combinatorial strategies. 12 , 13 Despite growing interest in CAR-T cell therapy for ALL, no comprehensive study has systematically summarized the research trends and focal points in this area.
Bibliometrics provides a quantitative framework for analyzing the structure, evolution, and impact of scientific research. 14 Previous bibliometric studies have separately examined research landscapes in acute leukemia 15 and broader aspects of cancer immunotherapy. 16 Despite the clinical significance and rapidly expanding literature on CAR-T therapy specifically for ALL, a comprehensive bibliometric analysis focusing on this particular therapeutic application is lacking. This study aims to analyze the global research patterns and emerging trends in CAR-T cell therapy for ALL through a bibliometric analysis.
Materials and methods
Data collection and search strategy
A systematic search was performed in the Web of Science Core Collection (WoSCC), a multidisciplinary database specializing in high-quality, peer-reviewed publications such as journal articles, conference proceedings, and books. WoSCC also provides a comprehensive citation network and key bibliometric indicators essential for analysis. 17 The literature search was conducted on December 31, 2024, covering articles published from January 1, 2010, to December 31, 2024. The search formula was as follows: TS = ((CAR-T OR “CAR T cell” OR “CAR-T cell” OR “CAR T-cell” OR CAR-T-cell OR “chimeric antigen receptor T cell” OR “chimeric antigen receptor-T cell” OR “chimeric antigen receptor T-cell” OR “chimeric antigen receptor-modified T-cell” OR “chimeric antigen receptor-transduced T-cell” OR “chimeric antigen receptor-redirected T cell” OR “chimeric antigen receptor redirecting T-cell” OR “chimeric antigen receptor engineered T cell” OR “chimeric antigen receptor-engineered T-cell”) AND (“acute lymphoblastic leukemia” OR “acute lymphocytic leukemia”) AND TS = (treat* OR therap*)). 18 , 19 The inclusion criteria were: 1) English language; 2) published between January 1, 2010, to December 31, 2024; 3) eligible articles on CAR-T therapy for ALL identified via the search strategy. Additionally, item types such as reviews, editorials, letters, and meeting abstracts were excluded. The inclusion of reviews, editorials, and letters would artificially inflate citation metrics and conflate novel research fronts with retrospective commentary, thereby obscuring the true intellectual trajectory; conference abstracts were removed due to incomplete metadata and unreliable citation indexing, while their inclusion could compromise the accuracy of co-citation networks. The information extracted from the selected studies included country/region, institutions, journals, publication and citation counts, authors, and keywords.
Statistical analysis and visualization
Bibliometric analysis was performed using three complementary software tools, VOSviewer (version 1.6.20), 20 CiteSpace (version 6.3.R1), 21 and the R package “bibliometrix” (version 4.3.3). 22 VOSviewer was primarily employed for constructing and visualizing bibliometric networks, including co-authorship, co-occurrence, and citation networks. 20 CiteSpace was utilized for detecting and visualizing emerging trends and critical transitions in the research field, particularly for keyword burst detection and timeline visualization. 21 The R package “bibliometrix” was used for comprehensive bibliometric analysis, including productivity metrics, collaboration patterns, and impact assessment. 22
Scientific impact was evaluated using several bibliometric indicators: the H-index, which measures productivity and citation impact (where h publications have at least h citations); 23 , 24 the G-index, which gives more weight to highly cited articles (the largest number g where g publications have at least g 2 citations); 25 and the M-index, which normalizes the H-index by dividing it by the number of years since the author’s first publication. 26 Journal impact was assessed using Journal Citation Reports (JCR) quartile rankings (Q1: top 25%, Q2: 25%–50%, Q3: 50%–75%, Q4: bottom 25%) and Impact Factor (IF), which represents the average number of citations received by articles published in the journal during the preceding 2 y. 27
Results
Overview of publications
The literature search and screening process identified 844 articles from 1,768 initially retrieved studies after removing reviews (n = 655), editorial materials (n = 36), letters (n = 44), meeting abstracts (n = 160), non-English publications (n = 25), and other document types (e.g., correction; proceeding paper; retracted publication) (n = 104) ( Figure 1 ). The temporal distribution of publications on CAR-T cell therapy for ALL showed a distinctive growth pattern from 2010 to 2024. Publications remained minimal from 2010 to 2012 with only one article per year, then began a slow increase from 2013 (3 articles) to 2016 (20 articles), followed by a steep acceleration from 2017 (31 articles) to a peak in 2022 (157 articles), with a moderate decline in 2023 (129 articles) and 2024 (112 articles) ( Figure 2 ). Figure 1. Literature selection and screening flowchart. A flowchart outlines the bibliometric analysis process for CAR-T cell research in acute lymphoblastic leukemia. It starts with a search using terms related to CAR-T cells and leukemia, identifying 1,768 studies from the Web of Science. The search spans publications from 2010 to 2024. After removing duplicates and irrelevant records (reviews, editorials, letters, meeting abstracts, non-English and others), 844 studies remain. These are analyzed using VOSviewer, R-bibliometrix and Citespace tools. The analysis focuses on publications, countries, journals, authors and keywords, culminating in a comprehensive bibliometric analysis of CAR-T and acute lymphoblastic leukemia research. A flowchart of literature selection and screening for CAR-T and acute lymphoblastic leukemia. Figure 2. Trends in annual publication output. A line graph showing number of publications by years from 2010 to 2024, about 0 to 180.
Analysis of countries
In the field of CAR-T cell therapy for ALL. The USA (total publication (TP) = 372; total citation (TC) = 38,065) led clear dominance, followed by China (TP = 275; TC = 5,636) and Germany (TP = 32; TC = 844). When examining average citation impact, the United Kingdom (104.6 citations per article) demonstrated remarkable influence despite its modest publication volume, followed by the USA (102.9) and Italy (66.0), all significantly exceeding Germany (26.4) and China (20.7). The Netherlands and Canada demonstrated the highest proportion of internationally collaborative publications (MCP ratio of 0.778 and 0.750, respectively), while several countries including Australia, Israel, Brazil, Poland, and Turkey conducted research exclusively through domestic collaboration (MCP ratio of 0). China, despite being the second-largest contributor overall, exhibited the lowest international collaboration ratio (0.076) ( Figure 3(A) , Table S1). Figure 3. Analysis of country contributions and collaborations. (A) Distribution of corresponding author publications by country. (B) Collaboration network map among countries. Image A displays a horizontal stacked bar chart with countries on the y-axis: USA, China, Germany, Spain, Italy, UK, France, Japan, Netherlands, Canada, Australia, Israel, Brazil, Poland, Switzerland, Thailand, Turkey, India, Iran, Norway. The x-axis shows ′N. of Documents′ with ticks at 0, 100, 200, 300. Bars are divided by Collaboration types: SCP and MCP. USA′s bar exceeds 300, China follows between 200-300, others are below 100. Image B features a collaboration network map with country nodes linked by curves. No axis labels or units. Nodes include USA, China, Germany, Italy, Spain, France, UK, Netherlands, Canada, Australia, Japan, Belgium, Norway, Switzerland, Israel, Poland, Sweden, Denmark, Ireland, Czech Republic, Croatia, Finland, Russia, Thailand, India, Iran, Brazil, Singapore. USA is the largest node, center-right, with many connections; Germany is large, center-left; Italy is large, lower center; China is upper center with multiple links. A bar chart and a network graph showing country publication counts and international collaborations.
Among the 29 countries involved in international collaborations with a minimum of 3 articles, the USA emerged as the country with the highest level of collaboration (total link strength = 210), followed by Germany (total link strength = 153) and France (total link strength = 135) ( Figure 3(B) ).
Analysis of institutions
To assess the academic impact of various institutions in the field, a ranking of the top 10 institutions with the highest publication counts was established. The University of Pennsylvania (TP = 353) led institutional contributions, followed by Memorial Sloan Kettering Cancer Center (TP = 170) and Children’s Hospital of Philadelphia (TP = 131) ( Figure 4(A) ). Among the 128 institutions involved in international collaborations with a minimum of 5 articles, the University of Pennsylvania (total link strength = 292) demonstrated the most extensive collaborative network, followed by Children’s Hospital of Philadelphia (total link strength = 251) and Memorial Sloan Kettering Cancer Center (total link strength = 220) ( Figure 4(B) ). Figure 4. Institution ranking and collaboration network. (A) Ranking of the top ten institutions by article count. (B) Network visualization of institutional collaborations. The image A showing a horizontal lollipop chart of institution ranking by article count. The x-axis label is Articles (unit: articles), ranging from 0 to 350 with major ticks at 0, 100, 200 and 300. The y-axis label is Affiliations (unit: none), listing 10 institutions. Data points (institution, articles): University of Pennsylvania 353; Memorial Sloan Kettering Cancer Center 170; Children’s Hospital of Philadelphia 131; Pennsylvania Medicine 131; Harvard University 122; National Institutes of Health (NIH) - USA 111; Stanford University 100; Zhejiang University 97; NIH National Cancer Institute (NCI) 93; University of London 90. The image B showing a network visualization of institutional collaborations. The x-axis label is not shown (unit: none). The y-axis label is not shown (unit: none). Nodes are labeled institutions connected by many curved links, forming clusters. Prominent labeled nodes include univ penn, mem sloan kettering canc ctr, harvard med sch, columbia univ, univ calif san francisco, univ cincinnati, univ minnesota, univ colorado, johns hopkins sch med, massachusetts gen hosp, univ texas md anderson canc c and a dense group including tongji univ, zhejiang univ, peking univ and other hospitals and universities. Links are densest within clusters and between the large central groups. Two plots ranking institutions by articles and mapping institutional collaboration clusters and links.
Analysis of journals
High-impact journals in this study employed the H-index as a criterion for ranking. Blood (TP = 41, TC = 4,866, H-index = 30, Q1) emerged as the most influential journal in this field, followed by Blood Advances (TP = 39, TC = 624, H-index = 25, Q1) and Leukemia (TP = 21, TC = 1,065, H-index = 16, Q1) (Table S2).
The co-occurrence network analysis of journals identified 43 journals with at least 5 occurrences, revealing distinct citation and reference patterns. Blood demonstrated the highest total link strength (704) in the co-occurrence network, followed by New England Journal of Medicine (666) and Blood Advances (420) ( Figure 5(A) ). Similarly, the coupling network analysis showed Blood (34,057), Blood Advances (32,857), and Frontiers in Immunology (30,624) as the journals with the highest link strength ( Figure 5(B) ). Figure 5. Journal Co-occurrence and coupling networks. (A) Co-occurrence network of journals. (B) Coupling network of journals. The image features two VOSviewer network maps. Map A shows a co-occurrence network of journals, highlighting major hubs like Blood, New England Journal of Medicine and Blood Advances. Node size indicates total link strength, with clusters connected by lines whose thickness represents co-occurrence strength. Journals such as Science Translational Medicine and Nature Medicine form distinct clusters. Map B displays a coupling network, with Blood, Blood Advances and Frontiers in Immunology as central nodes. Node size reflects link strength in this context and lines indicate coupling strength. Differences between the maps include varying journal prominence and cluster formations. Both maps use node size, line thickness and clustering to visually represent citation and reference patterns. A diagram showing VOSviewer maps of journal co-occurrence and coupling networks.
Analysis of authors
Among 6,459 authors who contributed to the analyzed literature, a small cohort demonstrated exceptional productivity and impact. Stephan A. Grupp (TP = 38; TC = 9,619; H-index = 30) emerged as the most influential researcher, followed by Carl H. June (TP = 26; TC = 7,963; H-index = 24) and Shannon L. Maude (TP = 25; TC = 6,647; H-index = 20) (Table S3).
Among the 183 authors involved in collaborations with a minimum of 5 articles, Grupp Stephan A. maintained the most extensive collaborative network (total link strength = 151), followed by Maude Shannon L. (total link strength = 122) and Hu Yongxian (total link strength = 116) ( Figure 6 ). Figure 6. Visualization of author collaboration network. The image is a VOSviewer author collaboration network visualization. It represents collaborations among authors based on a dataset of publications. The layout is force-directed, meaning there are no axes; proximity indicates relatedness. Nodes represent authors, with size indicating total link strength, reflecting the number of collaborations. Links show collaborative relationships, with thickness representing link strength. Colors denote clusters or communities. Key hubs include Hu Yongxian and Grupp Stephan A, who have the most extensive networks. Major clusters are centered around these hubs, with several smaller, loosely connected groups. Notable bridging authors include June Carl H and Mackall Crystal L, connecting different clusters. The visualization highlights two dominant collaboration communities with several smaller groups, illustrating the structure of author collaborations in the dataset. A VOSviewer author collaboration network showing major hubs and clusters.
Analysis of keywords
The keyword co-occurrence network visualization revealed five distinct clusters representing different research dimensions: (1) basic mechanisms and cell engineering (red cluster), featuring terms such as “activation,” “adoptive transfer,” “antigen,” “CD8(+),” “cell therapy,” “cytotoxicity,” “differentiation,” “gene,” and “memory”; (2) CAR design and target selection (green cluster), containing keywords such as “4-1bb,” “4-1bb costimulation,” “acute lymphoblastic-leukemia,” “adoptive immunotherapy,” “antitumor efficacy,” “B-cell,” “chimeric antigen receptor,” “CD28 costimulation,” and “domains”; (3) patient demographics and treatment combinations (blue cluster), encompassing “adolescents,” “adult patients,” “blinatumomab,” “blood,” “chemotherapy,” “childhood,” “children,” “clofarabine,” “combination,” and “cyclophosphamide” (4); clinical outcomes and follow-up (yellow cluster), with terms such as “acute lymphocytic-leukemia,” “adult B-cell,” “antibody,” “bone-marrow-transplantation,” “clinical-trials,” “diagnosis,” “donor,” “follow-up,” “minimal residual disease,” and “quality-of-life”; and (5) toxicity management and product specification (purple cluster), featuring “axicabtagene ciloleucel,” “biomarkers,” “CAR-T,” “CD19,” “central-nervous-system,” “cytokine release syndrome,” “disease,” “leukemia,” “lymphoblastic-leukemia,” “management,” “neurotoxicity,” and “outcomes” ( Figure 7(A) ). Figure 7. Analysis of research themes and trends. (A) Network visualization of keyword co-occurrence analysis; (B) Top 20 keywords with the most significant citation bursts. The image A illustrates a network visualization of keyword co-occurrence analysis, identifying clusters in research dimensions. Notable terms include ′activation,′ ′adoptive transfer,′ ′antigen,′ ′CD8(+),′ ′cell therapy,′ and ′cytotoxicity.′ These clusters represent areas such as basic mechanisms, CAR design, patient demographics, clinical outcomes and toxicity management. The image B presents the top 20 keywords with significant citation bursts, spanning from 2010 to 2024. Keywords like ′adoptive immunotherapy,′ ′antitumor activity,′ ′in vivo,′ ′gene therapy,′ and ′antibody′ are featured. Each keyword is linked to a strength value, start year and end year, with a timeline showing the duration of the citation burst. Visualization of keyword clusters and citation bursts in research, highlighting trends and key terms.
Analysis of burst keywords
This temporal pattern aligned with the burst detection analysis, which identified keywords with sudden increases in occurrence frequency. The most significant citation burst belonged to “adoptive immunotherapy” (strength = 7.56, 2010–2019), followed by “B cell” (strength = 8.16, 2017–2019), “antitumor activity” (strength = 6.26, 2010–2018), and “in vivo” (strength = 6.3, 2011–2018). Recent burst keywords (2021–2024) included “term follow up” (strength = 2.79) and “risk” (strength = 3.1) ( Figure 7(B) ).
Discussion
This study analyzed 844 articles on CAR-T cell therapy for ALL published between 2010 and 2024. The USA and China emerged as the leading countries in CAR-T therapy for ALL, with institutions such as the University of Pennsylvania, and Memorial Sloan Kettering Cancer Center being major contributors. The dominance of the USA and China can be attributed to substantial investments in CAR-T research and drug development, as well as the presence of renowned experts and research centers in these countries. 28 , 29 The most prolific journals publishing research in this field were Blood , Blood Advances , Leukemia , and Clinical Cancer Research . These high-impact hematology and oncology journals have been at the forefront of disseminating cutting-edge research on CAR-T therapies for ALL. Blood , with the highest number of publications and citations, has played a particularly prominent role in advancing this field. 30 Prominent authors such as Stephan A. Grupp, Carl H. June, and Shannon L. Maude have made significant contributions to the field, as evidenced by their high publication and citation counts. Their research has focused on the development, efficacy, and safety of CAR-T therapies for pediatric and adult ALL patients. 31 , 32
Research hotspots and frontier trends
Keyword co-occurrence analysis provides valuable insights into the intellectual structure and evolving research focuses within a field. Our visualization of keyword co-occurrence in CAR-T cell therapy for acute lymphoblastic leukemia research revealed five distinct clusters, each representing different research dimensions and frontier trends.
Cluster 1 (red): basic mechanisms and cellular engineering
Cluster 1 encompasses fundamental research on the cellular and molecular aspects of CAR-T cell therapy, featuring keywords such as “activation,” “adoptive transfer,” “antigen,” “CD8(+),” “cell therapy,” “cytotoxicity,” “differentiation,” “gene,” and “memory.” This cluster represents the mechanistic foundation of CAR-T therapy, focusing on T cell biology, genetic engineering, and preclinical development. Early work by Milone et al. established the fundamental concept of genetic redirection of T cells against CD19-expressing malignancies, 33 while subsequent studies by Long et al. explored how different costimulatory domains affect T cell persistence and functionality. 34 The keyword “memory” reflects the growing recognition that formation of memory T cell subsets is crucial for long-term CAR-T efficacy, with studies by Fraietta et al. demonstrating that persistence of CAR-T cells with memory phenotypes correlates with durable clinical responses. 35 The inclusion of “naive” highlights research into how starting T cell phenotypes influence final product efficacy, with Sommermeyer et al. showing that defined composition products derived from naive and memory T cells exhibit superior potency. 36 Keywords such as “inhibition” and “resistant” indicate ongoing challenges in overcoming immunosuppressive tumor microenvironments and antigen escape mechanisms, with cutting-edge approaches including development of logic-gated CARs and combination with checkpoint inhibitors as described by Chong et al. 37 The presence of “clinical-trial” and “phase-1” underscores the translational nature of this research cluster, with continuous feedback between laboratory discoveries and clinical observations driving iterative improvements in CAR design and manufacturing.
Cluster 2 (green): CAR design and target selection
Cluster 2 focuses on CAR design and target selection, containing keywords such as “4-1bb,” “4-1bb costimulation,” “acute lymphoblastic-leukemia,” “adoptive immunotherapy,” “antitumor efficacy,” “B-cell,” “chimeric antigen receptor,” “CD28 costimulation,” and “domains.” This cluster represents the engineering aspects and molecular targeting of CAR-T technology. The prominence of “4-1bb costimulation” and “CD28 costimulation” reflects the dichotomy in signaling domain approaches, with pivotal work by Kawalekar et al. demonstrating that these domains differentially affect T cell metabolism and memory formation. 38 The inclusion of both “acute lymphoblastic-leukemia” and “acute myeloid-leukemia” indicates expanding applications beyond the initial B-cell ALL focus, with Kenderian et al. exploring CD33-targeted approaches for AML. 39 The keywords “chimeric antigen receptor” and “chimeric receptors” emphasize the core technology, while “affinity” and “design” highlight engineering aspects critical for optimizing clinical performance, as demonstrated by Liu et al. in their work on how CAR affinity affects efficacy and toxicity profiles. 40 The appearance of “chronic lymphocytic-leukemia” alongside “multiple-myeloma” and “non-hodgkin-lymphoma” reflects the broadening application of CAR-T therapy across hematologic malignancies, with Porter et al. reporting durable responses in CLL patients. 41 The presence of “PD-1 blockade” indicates combination strategies with checkpoint inhibition, while “in-vivo persistence” and “sustained remissions” highlight the critical relationship between CAR-T cell persistence and durable clinical responses.
Cluster 3 (blue): CAR-T population treatment integration
Cluster 3 addresses patient demographics and treatment combinations, including keywords such as “adolescents,” “adult patients,” “blinatumomab,” “blood,” “chemotherapy,” “childhood,” “children,” “clofarabine,” “combination,” and “cyclophosphamide.” This cluster highlights the application of CAR-T therapy across different patient populations and its integration with existing treatment modalities. The keywords “children,” “childhood,” and “adolescents” reflect the initial focus on pediatric populations, with Gardner et al. reporting high response rates in pediatric and young adult patients. 42 The term “adult patients” indicates expansion to older populations, where Turtle et al. demonstrated that defined composition CAR products could achieve efficacy in adult ALL patients despite age-related T cell deficits. 43 The inclusion of “blinatumomab” and “chemotherapy” points to integration of CAR-T therapy within broader treatment algorithms, with Brown et al. investigating sequential use of these therapies. 44 The presence of “gvhd” (graft-versus-host disease) suggests research into allogeneic CAR-T approaches or post-CAR-T transplantation complications, while “combination” reflects emerging strategies to enhance CAR-T efficacy through combinatorial approaches. Keywords such as “cyclophosphamide” and “clofarabine” highlight the importance of lymphodepletion regimens in optimizing CAR-T cell expansion and persistence, as demonstrated by Turtle et al. in their dose-finding and lymphodepletion optimization studies. 43
Cluster 4 (yellow): clinical outcomes and follow-up
Cluster 4 encompasses clinical outcomes and follow-up, with keywords including “acute lymphocytic-leukemia,” “adult B-cell,” “antibody,” “bone-marrow-transplantation,” “clinical-trials,” “diagnosis,” “donor,” “follow-up,” “minimal residual disease,” and “quality-of-life.” This cluster reflects the evaluation of CAR-T therapy efficacy, safety, and long-term outcomes in clinical practice. The presence of “minimal residual disease” highlights a critical endpoint, with Hay et al. demonstrating that MRD status after CAR-T therapy strongly predicts long-term outcomes (Kevin A.). 45 Keywords such as “hyper-cvad” and “imatinib” represent conventional therapy components that CAR-T cells have been integrated with or compared against, as discussed by Jain et al. 46 The terms “follow-up” and “phase-ii” emphasize the increasing attention to durability of responses and advancement to later-phase clinical trials, with Park et al. reporting long-term outcomes from early CAR-T trials. 47 The appearance of “bone-marrow-transplantation” reflects ongoing questions about optimal sequencing with cellular therapy, with Pulsipher et al. investigating whether CAR-T therapy can serve as a bridge to transplant or potentially replace it. 48 Keywords such as “escape” and “mortality” indicate recognition of treatment failures and their consequences, while “quality-of-life” represents the growing emphasis on patient-reported outcomes, as examined by Ruark et al. 49 The presence of “inotuzumab ozogamicin” indicates research into alternative or sequential CD22-targeted therapies for patients relapsing after CD19-directed CAR-T therapy.
Cluster 5 (purple): toxicity management and product specification
Cluster 5 focuses on toxicity management and product specification, containing keywords such as “axicabtagene ciloleucel,” “biomarkers,” “CAR-T,” “CD19,” “central-nervous-system,” “cytokine release syndrome,” “disease,” “leukemia,” “lymphoblastic-leukemia,” “management,” “neurotoxicity,” and “outcomes.” This cluster represents the critical interface between therapy efficacy and safety, with particular focus on managing unique toxicities. The prominence of “cytokine release syndrome” and “neurotoxicity” reflects the defining toxicities of CAR-T therapy, with landmark work by Neelapu et al. establishing standardized grading and management approaches. 50 The inclusion of “axicabtagene ciloleucel” alongside other product-specific terms indicates the expansion of commercial products, as demonstrated by Locke et al. for non-Hodgkin lymphoma. 51 The term “biomarkers” highlights efforts to identify predictors of both efficacy and toxicity, with Hay et al. characterizing cytokine profiles associated with severe CRS. 52 Keywords such as “central-nervous-system” reflect specific toxicity manifestations, with Gust et al. elucidating mechanisms of CAR-T-associated neurotoxicity involving blood–brain barrier disruption. 53 The appearance of “management” alongside toxicity terms demonstrates the development of mitigation strategies, including early tocilizumab intervention as reported by Le et al. 54 The term “prognostic-factors” indicates the growing emphasis on patient selection and risk stratification, with studies by Nastoupil et al. identifying baseline characteristics associated with outcomes. 55 The presence of “1st-in-human” alongside established products reflects the continuous pipeline of novel approaches entering clinical testing, including next-generation constructs designed to enhance control over CAR-T activity.
Study trends transition
The burst keywords analysis revealed the temporal evolution of research priorities in CAR-T cell therapy for ALL. During 2010–2019, research progressed through distinct phases. Keywords including “adoptive immunotherapy” and “antitumor activity,” reflected basic mechanisms and proof-of-concept studies. Seminal work by Brentjens et al. established the preclinical efficacy of CD19-targeted CAR-T cells, 56 while Kochenderfer et al. demonstrated first-in-human evidence. 57 Keywords including “antibody,” “transplantation,” and “activation” marked a transition toward clinical translation, coinciding with Maude et al.‘s landmark publication on sustained remissions. 32 The strong burst of “B cell” paralleled tisagenlecleucel’s FDA approval in 2017.
The most recent phase (2020–2024) demonstrated an evolution toward practical clinical considerations and long-term outcomes. Burst keywords included “pediatric patients” (strength 3.15, 2018–2020), “inotuzumab ozogamicin” (strength 2.74, 2018–2020), and “CD8(+)” (strength 3.66, 2019–2021). The emergence of “survival” (strength 2.55, 2019–2020) signaled increased attention to long-term efficacy as follow-up data matured. Notably, the most recent burst keywords “term follow up” (strength 2.79, 2021–2024) and “risk” (strength 3.1, 2022–2024) indicated a current research frontier focused on long-term efficacy, safety, and risk stratification. This trend aligns with recent studies by Pasquini et al. examining real-world outcomes 58 and Shah et al. investigating predictive biomarkers for patient selection. 59 Beyond our bibliometric scope, CAR-T manufacturing heterogeneity remains a key challenge. Recent frameworks reveal that harvest timing dictates product composition 60 : mid-harvest yields memory-enriched, metabolically active cells, whereas late-harvest yields terminally differentiated but functional cells. This identifies novel quality attributes governing performance and may yield biomarkers to guide next-gen design.
Limitations
However, the study has some limitations. Firstly, relying exclusively on the WoSCC may have omitted relevant literature indexed in other major databases (e.g., Scopus, PubMed, Embase) or regional repositories. Furthermore, the restriction to English-language publications may have narrowed the scope of the analysis. Therefore, when interpreting the results, their potential influence should be taken into consideration in the absence of cross-validation or robustness checks. For instance, high-quality clinical trials and technological advancements in non-English-speaking regions may be underrepresented, which could lead to an underestimation of their impact. Future studies should consider integrating multiple databases and analyzing broader document types to provide a more comprehensive understanding of research trends in this field. Secondly, it is important to note that bibliometric analysis, which depends on citation-based indicators (such as citation counts, H-index, G-index, and M-index), is not designed to directly evaluate research quality. For instance, recently published high‑quality clinical trials may not yet have accumulated a substantial number of citations, which could result in an underestimation of their scholarly impact within such an analysis and may overstate the current hotspots and future frontiers concluded by analysis of keywords.
In conclusion, this bibliometric analysis identified five primary research hotspots in CAR-T cell therapy for ALL including basic mechanisms and cell engineering, CAR design and target selection, patient demographics and treatment combinations, clinical outcomes and follow-up, and toxicity management and product specification. Future research directions may focus on enhancing long-term outcomes through improved risk stratification models and developing predictive biomarkers for patient selection.
Supplementary Material
Supplementary material.docx
Acknowledgments
Xiaolong Tian: Conceptualization, Data curation, Writing - original draft, Writing - review & editing; Yingping Zhang: Formal analysis, Funding acquisition, Investigation, Writing - original draft, Writing - review & editing; Chen Zhang: Software, Resources, Supervision, Writing - original draft, Writing - review & editing; Dongdong Liu: Validation, Visualization, Writing - original draft, Writing - review & editing; Yanmin Ma: Methodology, Project administration, Writing - original draft, Writing - review & editing.
Xiaolong Tian is a doctor in the Hematology Department of Xiangyang Central Hospital (an affiliated hospital of Hubei University of Literature and Science), specializing in the study of blood-related diseases.
Yingping Zhang is a doctor in the Hematology Department of Xiangyang Central Hospital (an affiliated hospital of Hubei University of Literature and Science), specializing in the study of blood-related diseases.
Chen Zhang is a physician in the Laboratory Department of Xiangyang Central Hospital (an affiliated hospital of Hubei University of Literature and Science), specializing in research related to laboratory work.
Dongdong Liu is a doctor in the Hematology Department of Xiangyang Central Hospital (an affiliated hospital of Hubei University of Literature and Science), specializing in the study of blood-related diseases.
Yanmin Ma is a nurse at Xiangyang Central Hospital (an affiliated hospital of Hubei University of Literature and Science), and she is engaged in nursing work.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
All data generated or analyzed during this study are included in this published article.
Supplemental material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/21645515.2026.2690723
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