Abstract
Background
Angiogenesis inhibitors are vital in cancer treatment but are increasingly linked to thromboembolic events (TEEs), impacting patient outcomes. Despite extensive clinical trials, real-world data on TEEs associated with these agents remain limited. This study examines real-world TEEs patterns using the FDA Adverse Event Reporting System (FAERS).
Method
A retrospective pharmacovigilance analysis was conducted using FAERS data spanning from 2014 to 2024. Reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) was applied to identify significant safety signals. A signal was considered present when the lower limit of the 95% confidence interval for ROR (ROR025) > 1 and that for information component (IC025) > 0, with a minimum requirement of three or more reported cases.
Results
A total of 13,897 TEEs were identified, with 34.9% classified as arterial thromboembolism events (ATEs), 26.5% as venous thromboembolism events (VTEs), and 38.6% as TEEs of unknown origin (other TEEs). Our findings indicate a significant correlation between the use of angiogenesis inhibitors and an increased reporting frequency of TEEs. The disproportionality analysis revealed strong signals for several agents, with the top five drugs being cediranib, aflibercept, ramucirumab, cabozantinib, and sunitinib. The median time-to-onset (TTO) was 32 days (IQR: 6-141), with 48.5% of cases occurring within the first month and 12% persisting beyond one year. Temporal analysis demonstrated a declining incidence pattern, confirmed by Weibull distribution (shape parameter β = 0.63, indicating early failure type). The most frequently reported outcomes of TEEs associated with angiogenesis inhibitors were hospitalization and other serious events.
Conclusion
This study provides a real-world assessment of TEES risk associated with angiogenesis inhibitors. Identifying high-risk agents and temporal patterns underscores the need for early monitoring and highlights their contribution to TEEs in clinical practice.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12959-025-00770-4.
Keywords: Thromboembolic events, Angiogenesis inhibitors, FAERS database, Pharmacovigilance analysis, Disproportionality analysis
Introduction
Angiogenesis plays a crucial role in the pathophysiology of tumor growth, progression, and metastasis [1–3]. Vascular endothelial growth factor (VEGF) has been identified as the primary pro-angiogenic factor driving this process [4–6]. Therapies targeting the VEGF signaling pathway are a cornerstone in the treatment of various solid malignancies [2, 7]. Based on their sites of action, angiogenic inhibitors can be categorized into four groups: (1) monoclonal antibodies targeting VEGF; (2) monoclonal antibodies targeting VEGF receptors; (3) VEGF soluble decoy receptors that capture free VEGF (VEGF-trap); and (4) small molecule tyrosine kinase inhibitors (TKIs) [8]. Bevacizumab, a humanized monoclonal immunoglobulin G1 antibody, was the first angiogenesis inhibitor approved by the U.S. Food and Drug Administration (FDA). Initially indicated for metastatic colorectal cancer, it subsequently received approval for several other malignancies, including non-squamous non-small cell lung cancer, renal cell carcinoma, recurrent ovarian cancer, glioblastoma, and advanced cervical cancer [9, 10]. However, its benefits were not universal. Specifically, while approved for metastatic breast cancer in 2008, later studies showed no significant survival or quality-of-life improvement, leading U.S. FDA to withdraw that breast cancer approval in 2011 [11, 12]. Ramucirumab, a monoclonal antibody that targets the VEGF receptors, received U.S. FDA approval in 2014 for advanced gastric or gastroesophageal junction adenocarcinoma and metastatic non-small cell lung carcinoma, followed by approval for metastatic colorectal cancer in 2015 [13]. Aflibercept, a peptide-antibody fusion targeting VEGF ligands, received U.S. FDA approval in 2012 for metastatic colorectal cancer in combination with FOLFIRI (5-fluorouracil, folinic acid, and irinotecan). Several VEGFR TKIs have received U.S. FDA approval for indications across diverse solid malignancies, such as renal cell carcinoma, hepatocellular carcinoma, differentiated thyroid cancer, colorectal cancer, and gastrointestinal stromal tumors [14]. Despite the significant anti-tumor effects of angiogenesis inhibitors in various cancer types, emerging evidence indicates a range of adverse reactions associated with their use [8, 15, 16]. Over the past two decades, numerous studies have documented cardiovascular toxicities associated with angiogenesis inhibitor therapy in cancer patients, including hypertension, heart failure, TEEs, hemorrhage, QT interval prolongation, and arrhythmias [16–23].
Cancer patients face an elevated risk for TEEs [24]. This risk is influenced by specific cancer-related factors, including malignancy type, histology, stage, and treatment [25]. Extensive research has established associations between malignancy types and risk of TEEs, with pancreatic, brain, lung, and ovarian cancers demonstrating the highest incidence [26–28]. Furthermore, anticancer therapies themselves may increase TEEs risk during active treatment [29]. Several studies have reported the incidence, risk factors, and prognostic impact of TEEs associated with angiogenesis inhibitors [22, 30]. However, the detailed safety profile of TEEs associated with angiogenesis inhibitors has not been thoroughly investigated. It remains largely unclear whether the increased risk of TEEs is directly attributable to the prothrombotic effects of angiogenesis inhibitors or simply a result of underlying malignancy and other baseline risk factors. Additionally, the majority of these data come from clinical trials with strict inclusion criteria and selected cohorts, which may underestimate the true burden of TEEs [22, 30–33].
Therefore, this pharmacovigilance analysis aimed to systematically investigate the real-world patterns of TEEs associated with angiogenesis inhibitors, using data from the FDA Adverse Event Reporting System (FAERS) database.
Patients and methods
Data sources
The FAERS database is a publicly accessible post-marketing safety surveillance database that provides researchers with raw data available on the FDA website (https://fis.fda.gov/extensions/FPDQDE-FAERS/FPD-QDE-FAERS.html). FAERS data files are comprised of seven databases, including demographic and administrative information (DEMO), adverse drug reaction details (REAC), patient outcomes (OUTC), drug information (DRUG), drug therapy start and end dates (THER), report sources (RPSR), and indications for use or diagnosis (INDI).
Procedures
This retrospective analysis utilized data from the FAERS database, spanning from the third quarter of 2014 to the first quarter of 2024 because Many newer angiogenesis inhibitors were approved after 2014 [17]. In the FAERS database, adverse events (AEs) are coded using preferred terms (PTs) based on the Medical Dictionary for Regulatory Activities (MedDRA) (version 24.1). Based on a literature review and Standardized MedDRA Queries (SMQs) [34, 35], we categorized PTs for TEEs into three mutually exclusive groups: VTEs, ATEs, and TEEs of unknown origin, classified as other TEEs. A comprehensive list of PTs within the relevant SMQs is provided in Supplementary Table 1.
In the FAERS database, drug entries are recorded as free text, which may include generic names, brand names, and potential misspellings. To address this, we utilized a comprehensive drug name archive that includes all generic and brand names for angiogenesis inhibitors approved by the U.S. FDA and the National Medical Products Administration (NMPA) in China. Drugs are classified into roles (primary suspect, secondary suspect, concomitant, interacting) by the person AEs. Only cases in which an angiogenesis inhibitor was designated as the ‘primary suspect’ for the reported AEs were included.
Statistical analysis
Disproportionality analysis is currently a widely used method for signal detection in pharmacovigilance, relying on a two-by-two contingency table (Table 1). Signal detection was conducted using the ROR method and the BCPNN method [36]. A potential signal was defined as statistically significant when meeting all of the following criteria: ROR025 > 1, IC025 > 0, and at least three reported cases [17]. Signal strength was categorized into three tiers based on IC025 values: weak (0 < IC025 ≤ 1.5), moderate (1.5 < IC025 ≤ 3.0), and strong (IC025 > 3.0) [37]. The TTO of TEEs associated with angiogenesis inhibitor was calculated as the duration between the event date (EVENT_DT, recorded in DEMO file) and the treatment initiation date (START_DT, documented THER file). We utilized Weibull distribution modeling to analyze TTO patterns of TEEs, with the shape parameter (β) and its 95% confidence interval (CI) serving as key indicators of temporal risk profiles [38]. Specifically, β values revealed three distinct hazard patterns: (1) β < 1 corresponded to a decreasing hazard (early failure pattern), indicating highest risk during initial treatment; (2) β ≈ 1 represented a constant hazard (random failure pattern); and (3) β > 1 reflected an increasing hazard (wear-out failure pattern), suggesting progressive risk accumulation over time [38]. Kaplan-Meier analysis was performed to evaluate the temporal distribution of TEEs, with cumulative incidence curves generated to visualize event occurrence over time. The baseline characteristics were generated using the ‘autoReg’ package. Kaplan-Meier analysis was performed using the ‘survminer’ and ‘survival’ packages, while Weibull distribution analysis was conducted with the ‘fitdistrplus’ package. All visualizations were performed using the ‘ggplot2’ package. All statistical analyses were conducted using R version 4.3.3 (R Foundation for Statistical Computing).
Table 1.
Disproportionality analysis based on two-by-two contingency table
| Target adverse events | Other adverse events | Total | |
|---|---|---|---|
| Target drug | a | b | a + b |
| Other drugs | c | d | c + d |
| Total | a + c | b + d | a + b + c + d |
Results
Descriptive analysis
We identified 13,897 TEEs associated with angiogenesis inhibitors in the FAERS database from the third quarter of 2014 to the first quarter of 2024. Among these, 4,852 cases (34.9%) were ATEs, 3,683 cases (26.5%) were VTEs, and 5,353 cases (38.6%) were classified as other TEEs. In the available reports, half of the cases were observed in adults aged 18 to 65 years, with a higher proportion in male patients (33.6%) compared to female patients (25.7%). The majority of reports (56.3%) on TEEs associated with angiogenesis inhibitors were submitted by healthcare professionals. The top five reporting countries were the United States (32.1%), Japan (12.5%), Canada (4.4%), France (4.1%), and Germany (3.5%). Cancer (60.5%) and eye disorders (16%) were the most common indications, while other conditions accounted for 12.7%, and 10.8% were either unknown or missing. The most frequently reported outcomes of TEEs associated with angiogenesis inhibitors were hospitalization and other serious events. The characteristics of TEE reports are detailed in Table 2.
Table 2.
Baseline characteristics of tees associated with angiogenesis inhibitors
| Characteristics | Anti-VEGF mAb (N = 4658) | Anti-VEGF TKI (N = 6742) | VEGF-Trap (N = 2497) | total (N = 13897) |
|---|---|---|---|---|
| Adverse event, N (%) | ||||
| ATE | 1029 (22.1) | 2083 (30.9) | 1741 (69.7) | 4853 (34.9) |
| VTE | 1604 (34.4) | 1895 (28.1) | 185 (7.4) | 3684 (26.5) |
| Other TEEs | 2025 (43.5) | 2764 (41) | 571 (22.9) | 5360 (38.6) |
| Age group, N (%) | ||||
| < 18 | 25 (0.5) | 12 (0.2) | 0 (0) | 2771 (19.9) |
| 18–65 | 1159 (24.9) | 1531 (22.7) | 81 (3.2) | 6965 (50.1) |
| ≥ 65 | 1151 (24.7) | 2748 (40.8) | 225 (9) | 37 (0.3) |
| Unknown | 2323 (49.9) | 2451 (36.4) | 2191 (87.7) | 4124 (29.7) |
| Patient’s gender, N (%) | ||||
| F | 1461 (31.4) | 1866 (27.7) | 247 (9.9) | 3574 (25.7) |
| M | 1407 (30.2) | 3016 (44.7) | 250 (10) | 4673 (33.6) |
| Unknown | 1790 (38.4) | 1860 (27.6) | 2000 (80.1) | 5650 (40.7) |
| Type of reporter, N (%) | ||||
| Health professional | 3277 (70.4) | 3445 (51.1) | 1101 (44.1) | 7823 (56.3) |
| Non-health professional | 604 (13) | 1728 (25.6) | 933 (37.4) | 3265 (23.5) |
| Unknown | 777 (16.7) | 1569 (23.3) | 463 (18.5) | 2809 (20.2) |
| Reported countries, N (%) | ||||
| United States | 1308 (28.1) | 2225 (33) | 921 (36.9) | 4454 (32.1) |
| Japan | 773 (16.6) | 742 (11) | 221 (8.9) | 1736 (12.5) |
| Canada | 275 (5.9) | 222 (3.3) | 121 (4.8) | 618 (4.4) |
| France | 213 (4.6) | 282 (4.2) | 81 (3.2) | 576 (4.1) |
| Germany | 212 (4.6) | 210 (3.1) | 58 (2.3) | 480 (3.5) |
| Great Britain | 97 (2.1) | 170 (2.5) | 93 (3.7) | 360 (2.6) |
| Italy | 181 (3.9) | 126 (1.9) | 17 (0.7) | 324 (2.3) |
| China | 168 (3.6) | 118 (1.8) | 6 (0.2) | 292 (2.1) |
| Australia | 27 (0.6) | 59 (0.9) | 156 (6.2) | 242 (1.7) |
| Other countries | 667 (14.3) | 1103 (16.4) | 405 (16.2) | 2175 (15.7) |
| Unknown | 737 (15.8) | 1485 (22) | 418 (16.7) | 2640 (19) |
| Indication, N (%) | ||||
| Cancer | 3859 (82.8) | 4492 (66.6) | 61 (2.4) | 8412 (60.5) |
| Eye disorder | 249 (5.3) | 0 (0) | 1972 (79) | 2221 (16) |
| Others | 108 (2.3) | 1616 (24) | 37 (1.5) | 1761 (12.7) |
| Unknown | 442 (9.5) | 634 (9.4) | 427 (17.1) | 1503 (10.8) |
| Outcome of adverse events, N (%) | ||||
| Death | 262 (5.6) | 243 (3.6) | 39 (1.6) | 544 (3.9) |
| Disability | 30 (0.6) | 28 (0.4) | 8 (0.3) | 66 (0.5) |
| Hospitalization | 819 (17.6) | 2510 (37.2) | 95 (3.8) | 3424 (24.6) |
| Life-threatening | 226 (4.9) | 392 (5.8) | 26 (1) | 644 (4.6) |
| Other serious | 3267 (70.1) | 3490 (51.8) | 2314 (92.7) | 9071 (65.3) |
| Unknown | 54 (1.2) | 79 (1.2) | 15 (0.6) | 148 (1.1) |
| Reported year, N (%) | ||||
| 2014 (Q3 ~ Q4) | 219 (4.7) | 146 (2.2) | 74 (3) | 439 (3.2) |
| 2015 | 681 (14.6) | 412 (6.1) | 142 (5.7) | 1235 (8.9) |
| 2016 | 536 (11.5) | 444 (6.6) | 215 (8.6) | 1195 (8.6) |
| 2017 | 631 (13.5) | 564 (8.4) | 294 (11.8) | 1489 (10.7) |
| 2018 | 445 (9.6) | 681 (10.1) | 451 (18.1) | 1577 (11.3) |
| 2019 | 366 (7.9) | 820 (12.2) | 290 (11.6) | 1476 (10.6) |
| 2020 | 424 (9.1) | 877 (13) | 291 (11.7) | 1592 (11.5) |
| 2021 | 383 (8.2) | 782 (11.6) | 329 (13.2) | 1494 (10.8) |
| 2022 | 404 (8.7) | 925 (13.7) | 173 (6.9) | 1502 (10.8) |
| 2023 | 463 (9.9) | 908 (13.5) | 163 (6.5) | 1534 (11) |
| 2024 (Q1) | 106 (2.3) | 183 (2.7) | 75 (3) | 364 (2.6) |
mAbs Monoclonal antibodies, TKI Tyrosine kinase inhibitors, ATE Arterial thromboembolism events, VTE Venous thromboembolism events, Other TEEs other thromboembolism events
Disproportionality analysis of tees for angiogenesis inhibitors
The use of angiogenesis inhibitors was significantly associated with a higher frequency of reporting TEEs compared to the entire database. Significant signals were detected for majority of the angiogenesis inhibitors, with the exceptions of sorafenib, regorafenib, vandetanib and erdafitinib. In terms of signal strength, the five drugs with the strongest signal strengths were cediranib, aflibercept, ramucirumab, cabozantinib, and sunitinib. In contrast, axitinib showed the weakest signal value. The results obtained using the ROR and BCPNN methods are consistent (Tables 3 and 4).
Table 3.
Disproportionality analysis for tees associated with different Anti-VEGF agents order by ROR
| Order | Agent | N | ROR | ROR025 | ROR975 | IC | IC025 | IC975 |
|---|---|---|---|---|---|---|---|---|
| 1 | Cediranib | 4 | 5.179 | 1.879 | 14.273 | 2.294 | 0.11 | 2.792 |
| 2 | Aflibercept | 2497 | 3.749 | 3.601 | 3.903 | 1.851 | 1.79 | 1.908 |
| 3 | Ramucirumab | 387 | 3.3 | 2.98 | 3.653 | 1.678 | 1.52 | 1.819 |
| 4 | Cabozantinib | 472 | 3.095 | 2.823 | 3.394 | 1.59 | 1.448 | 1.719 |
| 5 | Sunitinib | 41 | 2.08 | 1.525 | 2.837 | 1.036 | 0.548 | 1.45 |
| 6 | Lenvatinib | 2121 | 1.936 | 1.854 | 2.021 | 0.933 | 0.869 | 0.996 |
| 7 | Bevacizumab | 4271 | 1.522 | 1.476 | 1.569 | 0.593 | 0.548 | 0.637 |
| 8 | Pazopanib | 144 | 1.357 | 1.151 | 1.6 | 0.433 | 0.188 | 0.672 |
| 9 | Nintedanib | 2116 | 1.336 | 1.28 | 1.395 | 0.41 | 0.347 | 0.473 |
| 10 | Axitinib | 739 | 1.101 | 1.023 | 1.184 | 0.136 | 0.029 | 0.243 |
| 11 | Sorafenib | 619 | 0.94 | 0.868 | 1.018 | −0.088 | −0.205 | 0.029 |
| 12 | Erdafitinib | 28 | 0.879 | 0.605 | 1.275 | −0.184 | −0.718 | 0.36 |
| 13 | Regorafenib | 411 | 0.693 | 0.629 | 0.764 | −0.523 | −0.665 | −0.379 |
| 14 | Vandetanib | 42 | 0.533 | 0.393 | 0.722 | −0.899 | −1.326 | −0.442 |
| 15 | Fruquintinib | 5 | 0.33 | 0.137 | 0.795 | −1.585 | −2.597 | −0.235 |
Table 4.
Summary of disproportionality analysis for subgroup tees associated with different Anti-VEGF agents order by ROR
| Order | ATE | VTE | Other_TEEs | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Agent | ROR | IC | Agent | ROR | IC | Agent | ROR | IC | |
| 1 | Aflibercept | 7.925 | 2.933 | Ramucirumab | 5.205 | 2.361 | Ramucirumab | 2.892 | 1.515 |
| 2 | Sunitinib | 3.845 | 1.925 | Cabozantinib | 5.19 | 2.356 | Cabozantinib | 2.5 | 1.308 |
| 3 | Ramucirumab | 2.35 | 1.224 | Lenvatinib | 2.575 | 1.354 | Aflibercept | 1.863 | 0.889 |
| 4 | Cabozantinib | 2.299 | 1.192 | Bevacizumab | 2.315 | 1.198 | Lenvatinib | 1.707 | 0.763 |
| 5 | Lenvatinib | 1.761 | 0.81 | Pazopanib | 2.13 | 1.086 | Bevacizumab | 1.491 | 0.569 |
| 6 | Nintedanib | 1.385 | 0.466 | Sunitinib | 1.779 | 0.828 | Axitinib | 1.325 | 0.403 |
| 7 | Pazopanib | 1.14 | 0.188 | Nintedanib | 1.726 | 0.782 | Pazopanib | 1.117 | 0.158 |
| 8 | Bevacizumab | 1.004 | 0.006 | Erdafitinib | 1.396 | 0.48 | Nintedanib | 1.094 | 0.128 |
| 9 | Axitinib | 0.944 | −0.083 | Aflibercept | 1.187 | 0.246 | Sorafenib | 0.921 | −0.118 |
| 10 | Sorafenib | 0.918 | −0.122 | Sorafenib | 1.012 | 0.017 | Sunitinib | 0.897 | −0.155 |
| 11 | Erdafitinib | 0.667 | −0.582 | Axitinib | 0.884 | −0.178 | Regorafenib | 0.797 | −0.325 |
| 12 | Regorafenib | 0.544 | −0.875 | Regorafenib | 0.713 | −0.487 | Erdafitinib | 0.775 | −0.366 |
| 13 | Vandetanib | 0.503 | −0.988 | Vandetanib | 0.452 | −1.142 | Fruquintinib | 0.747 | −0.418 |
| 14 | Cediranib | NA | NA | Cediranib | NA | NA | Vandetanib | 0.6 | −0.733 |
| 15 | Fruquintinib | NA | NA | Fruquintinib | NA | NA | Cediranib | NA | NA |
ATE Arterial thromboembolism events, VTE Venous thromboembolism events, Other TEEs other thromboembolism events, ROR Reporting odds ratio, IC Information component
We further classified TEEs into ATEs, VTEs, and other TEEs based on MedDRA. Our results indicated that the use of angiogenesis inhibitors was significantly associated with a higher frequency of reported ATEs, VTEs, and other TEEs. Signals for ATEs were detected in six agents, including ramucirumab, aflibercept, sunitinib, Lenvatinib, nintedanib, and cabozantinib (Table 4, Supplement Table 2). Signals for VTEs were detected in eight agents, including bevacizumab, ramucirumab, aflibercept, lenvatinib, nintedanib, pazopanib, and cabozantinib (Table 4, Supplement Table 3). Signals for other TEEs were detected in seven agents, including bevacizumab, ramucirumab, aflibercept, lenvatinib, nintedanib, cabozantinib, and axitinib (Table 4, Supplement Table 4).
TTO of adverse events
The TTO of TEEs associated with angiogenesis inhibitors was analyzed using data extracted from the FAERS database. The median TTO was 32 days (IQR: 6–141). The temporal distribution of TEEs associated with angiogenesis inhibitors demonstrated a declining incidence pattern. Nearly half of all cases (48.5%) occurred within the first month of treatment, while 20.1% were reported during months 2–3. Notably, late-onset TEEs persisted beyond one year of therapy, comprising 12% of reported cases (Fig. 1A). In the evaluation of the Weibull distribution test, the shape parameter (β) was calculated to be 0.63. The value of β < 1 suggested that the incidence of TEEs was considered to decrease over time, indicating an early failure type (Table 5). The cumulative incidence of TEEs over time is depicted in a Kaplan-Meier plot (Fig. 1B).
Fig. 1.
TTO of TEEs associated with angiogenesis inhibitors. (A) The distribution of TTO of TEEs associated with angiogenesis inhibitors, (B) The cumulative incidence of TEEs associated with angiogenesis inhibitors over time
Table 5.
TTO analysis for tees associated angiogenesis inhibitors using the Weibull distribution test
| Case number | TTO (days) | Weibull distribution | Failure type | |||
|---|---|---|---|---|---|---|
| Scale parameter | Shape parameter | |||||
| Median (IQR) | α | 95% CI | β | 95% CI | ||
| 1254 | 32 (6-141) | 121.83 | 110.44-133.22 | 0.63 | 0.60-0.653 | Early failure |
TEEs thromboembolic events, CI confidence interval, IQR interquartile range, TTO time to onset
Outcome of adverse events
For most angiogenesis inhibitors, hospitalization and other serious adverse events were the most commonly reported outcomes. Aflibercept had the lowest proportion (2.6%) of death and life-threatening events, compared to other angiogenesis inhibitors (Fig. 2).
Fig. 2.
Outcomes of TEEs associated with angiogenesis inhibitors
Discussion
In this study, we conducted a comprehensive pharmacovigilance analysis to investigate the association between angiogenesis inhibitors and TEEs using data from the FAERS database. A total of 13,897 TEEs were identified, with 34.9% classified as ATEs, 26.5% as VTEs, and 38.6% as other TEEs. Our findings indicate a significant correlation between the use of angiogenesis inhibitors and an increased reporting frequency of TEEs, with distinct patterns emerging across different classes of angiogenesis inhibitors. The disproportionality analysis revealed strong signals for several agents, with the top five drugs being cediranib, aflibercept, ramucirumab, cabozantinib, and sunitinib. The TTO of TEEs associated with angiogenesis inhibitors was 32 days (IQR: 6–141). Nearly half of all cases (48.5%) occurred within the first month of treatment. Weibull distribution analysis revealed a decreasing hazard rate over time, suggesting that TEEs associated with angiogenesis inhibitors were more likely to occur early rather than later. The most frequently reported outcomes of TEEs associated with angiogenesis inhibitors were hospitalization and other serious events. These results provide critical insights into the thromboembolic risk associated with angiogenesis inhibitors and underscore the need for vigilance in clinical practice to mitigate adverse outcomes.
Previous studies have reported TEEs in cancer clinical trials involving biologic VEGF/VEGFR inhibitors such as bevacizumab, ramucirumab, and aflibercept. Bevacizumab, a humanized monoclonal antibody targeting VEGF, was the first therapy approved in 2004. Large meta-analyses have investigated the relative risks of VTE and arterial thrombosis associated with bevacizumab use. These analyses found elevated risks, with risk ratios of 1.33 (95% CI, 1.13–1.56) for VTE and 1.44 (95% CI, 1.08–1.91) for arterial thrombotic complications compared to control groups [39, 40]. Ramucirumab, a monoclonal antibody targeting VEGFR-2, has been linked to an increased risk of thrombosis. In the REGARD study, Fuchs and colleagues observed a modest rise in arterial thrombosis incidence in the ramucirumab group compared to the placebo group [41]. However, a meta-analysis of multiple ramucirumab trials found no significant increase in the risk of VTE or arterial thrombosis [40]. Van Cutsem et al. reported that aflibercept, a recombinant fusion protein comprising a VEGF decoy receptor and an IgG1 Fc domain, was associated with higher rates of arterial thrombosis (1.8% vs. 0.5%) and venous thromboembolism (7.9% vs. 6.3%) compared to placebo [42]. Similarly, a cohort study demonstrated significant associations between aflibercept and both VTE and ATE [43]. However, a meta-analysis found no significant increase in VTE risk with aflibercept versus placebo or other therapies in cancer patients [44]. This inconsistency highlights the need for careful interpretation of thrombotic risk associated with aflibercept across different study designs and patient populations. Our real-world analysis confirms that bevacizumab, ramucirumab, and aflibercept significantly increase TEE risk, consistent with known associations from clinical trials and meta-analyses.
VEGFR-targeted TKIs are Small molecules developed to inhibit the VEGFR family of cell surface receptors. Sorafenib and sunitinib were the first two VEGFR-targeted TKIs to receive approval in the United States, in 2005 and 2007, respectively. A systematic review and meta-analysis revealed a three-fold increase in the risk of arterial thrombosis in patients treated with sorafenib and sunitinib compared to control patients [45]. Several newer-generation anti-angiogenic TKIs, such as lenvatinib, nintedanib, pazopanib, cabozantinib, axitinib, regorafenib, vandetanib and erdafitinib have been developed. One meta-analysis reported a 3% incidence of VTEs with VEGFR-TKIs, without a statistically significant increase in VTE risk compared to controls [46]. The author suggests that underreporting of VTEs may explain this finding, noting that the number of trials involving oral VEGFR-TKIs in cancer that report these vascular events is quite limited. Another meta-analysis found a significant association between VEGFR-TKI use and an increased risk of ATEs [47]. Our analysis reveals a previously unrecognized agent-specific risk stratification among VEGFR-TKIs for TEEs: Sunitinib, lenvatinib, nintedanib, pazopanib, and cabozantinib demonstrated significantly increased risk of ATEs, VTEs, and other TEEs. Sorafenib was uniquely associated with elevated VTE incidence. Axitinib showed exclusive association with non-ATE/VTE TEEs. Notably, certain angiogenesis inhibitors, including cediranib and sunitinib, demonstrated high signal strengths despite their rare event frequencies, suggesting their potential risks warrant careful consideration. These findings highlight the need for further clinical studies to validate the safety signals and inform evidence-based prescribing practices.
Our analysis identified cediranib, aflibercept, ramucirumab, cabozantinib, and sunitinib as the five agents most strongly associated with overall TEEs. Aflibercept exhibited the strongest signal strength for ATEs, while ramucirumab showed the highest signal strength for VTEs and other TEEs. Regorafenib and vandetanib demonstrated no significant association with TEEs, suggesting their potential as safer alternatives for patients at high TEEs risk. Our study demonstrates that TEEs following angiogenesis inhibitors treatment predominantly occur within the first three months, with peak incidence (48.5%) observed during the initial month. These findings underscore the critical need for vigilant monitoring of TEEs during the initial month following angiogenesis inhibitor administration. Early detection and management of angiogenesis inhibitor-associated TEEs are clinically imperative. Notably, existing literature lacks comprehensive investigations into the temporal patterns of these adverse effects, highlighting the unique contribution of our study in elucidating this critical aspect of treatment safety.
The exact mechanism through which angiogenesis inhibitors trigger TEEs remains unclear. The primary hypothesis proposes that VEGF signaling is crucial for supporting endothelial cell survival and transmitting anti-apoptotic signals [48, 49]. Inhibiting VEGF may compromise vascular integrity by reducing the regenerative ability of endothelial cells and exposing pro-coagulant phospholipids on the luminal membrane or underlying matrix, ultimately resulting in thrombosis [50]. Furthermore, VEGF enhances the synthesis of nitric oxide (NO) and prostacyclin (PGI2), while inhibiting pathways associated with endothelial cell activation, apoptosis, and pro-coagulant alterations. Inhibition of VEGF signaling reduces the levels of NO and PGI2, thereby elevating the risk of thrombosis [51]. Inhibition of VEGF may also heighten the risk of thrombosis by elevating hematocrit levels and blood viscosity through excessive erythropoietin production [52]. Additionally, inhibition of VEGF may enhance the expression of pro-inflammatory cytokines, leading to tissue damage and the formation of thrombi at the site [53]. Another hypothesis suggests that bevacizumab IgG immune complexes may raise thrombotic risk by activating platelets via the FcγRIIa receptor found on their surface [54].
One of the major strengths of this study is its use of real-world data from the FAERS database, providing a more comprehensive view of TEEs associated with angiogenesis inhibitors than might be observed in controlled clinical trials. Additionally, the use of multiple signal detection methods (ROR and BCPNN) enhances the robustness of the findings.
However, this study also has several limitations. First, the FAERS database relies on spontaneous reporting, which can introduce reporting bias and under-reporting of adverse events. Second, causality cannot be firmly established from the data, as it is unclear whether the observed TEEs were directly caused by angiogenesis inhibitors or influenced by confounding factors such as cancer type, stage, concurrent therapies, and comorbidities. The absence of detailed clinical information in the FAERS database also limits the ability to adjust for these potential confounders. Furthermore, the lack of standardized dose information precluded meaningful dose-response analysis, while absent therapy end dates prevented assessment of exposure duration effects. These data gaps may affect the generalizability of our TTO estimates, particularly for drugs with known cumulative toxicity patterns. Future studies incorporating electronic health records with structured prescription data could help validate and extend these findings by enabling dose- and duration-adjusted analyses.
Conclusion
In conclusion, this study provides valuable insights into the real-world occurrence of TEEs associated with angiogenesis inhibitors. While these therapies remain a cornerstone of cancer treatment and other medical conditions, their use is linked to a significant risk of TEEs, particularly arterial and venous TEEs. These findings emphasize the importance of vigilant monitoring and proactive management in clinical practice.
Supplementary Information
Acknowledgements
None.
Authors’ contributions
Conception and design of study: JTZ and JHZ, acquisition of data: TTC, YTZ, YL and PGN, analysis and interpretation of data: JTZ, MLW and YTZ, drafting the manuscript: JTZ, YTZ and PGN, revising the manuscript critically for important intellectual content: HJC, XPZ and JHZ. All authors approved the version of the manuscript to be published.
Funding
This research was supported by the Startup Fund for scientific research, Fujian Medical University (Grant number: 2024QH1197).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
Ethical approval and consent to participate was not needed for this study because FAERS is a public anonymized database.
Consent for publication
Not applicable.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jintuo Zhou, Meiling Wu and Tingting Chen contributed equally.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.