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Journal of the Chinese Medical Association : JCMA logoLink to Journal of the Chinese Medical Association : JCMA
. 2023 Nov 22;87(1):48–57. doi: 10.1097/JCMA.0000000000001026

Major adverse cardiovascular events of vascular endothelial growth factor tyrosine kinase inhibitors among patients with different malignancy: A systemic review and network meta-analysis

Yen-Chou Chen a,b,c, Jin-Hua Chen d,e, Fang-I Hsieh f,g,*
PMCID: PMC12718917  PMID: 37991373

Abstract

Background:

Vascular endothelial growth factor tyrosine kinase inhibitors (VEGF-TKIs) are a common cancer treatment. However, the pharmacologic characteristics of VEGF-TKIs may influence cardiovascular risks. The relative risks of major adverse cardiovascular events (MACEs) associated with VEGF-TKIs are poorly understood.

Methods:

We searched PubMed, Embase, and ClinicalTrials.gov from inception until August 31, 2021, for phase II/III randomized controlled trials of 11 VEGF-TKIs (axitinib, cabozantinib, lenvatinib, pazopanib, ponatinib, ripretinib, regorafenib, sorafenib, sunitinib, tivozanib, and vandetanib). The endpoints were heart failure, thromboembolism, and cardiovascular death. The Mantel-Haenszel method was used to calculate the risk of VEGF-TKI among users by comparing it to nonusers. Pairwise meta-analyses with a random-effects model were used to estimate the risks of the various VEGF-TKIs. We estimated ranked probability with a P-score and assessed credibility using the Confidence in Network Meta-Analysis framework.

Results:

We identified 69 trials involving 30 180 patients with cancer. The highest risk of MACEs was associated with high-potency tivazonib (odds ratio [OR]: 3.34), lenvatinib (OR: 3.26), and axitinib (OR: 2.04), followed by low-potency pazopanib (OR: 1.79), sorafenib (OR: 1.77), and sunitinib (OR: 1.66). The risk of heart failure significantly increased in association with less-selective sorafenib (OR: 3.53), pazopanib (OR: 3.10), and sunitinib (OR: 2.65). The risk of thromboembolism significantly increased in association with nonselective lenvatinib (OR: 3.12), sorafenib (OR: 1.54), and sunitinib (OR: 1.53). Higher potency (tivozanib, axitinib) and lower selectivity (sorafenib, vandetanib, pazopanib, sunitinib) were associated with a higher probability of heart failure. Low selectivity (lenvatinib, cabozantinib, sorafenib, sunitinib) was associated with a higher probability of thromboembolism.

Conclusion:

Higher-potency and lower-selectivity VEGF-TKIs may influence the risks of MACEs, heart failure, and thromboembolism. These findings may facilitate evidence-based decision-making in clinical practice.

Keywords: Angiogenesis inhibitors, Cardiovascular system, Cardiotoxicity, Protein kinase inhibitors, Vascular endothelial growth factors

1. INTRODUCTION

Tyrosine kinase inhibitors with anti-vascular endothelial growth factor tyrosine kinase inhibitors (VEGF-TKIs) inhibit tumor growth by interfering with VEGF signaling and off-target kinases.1–3 VEGF-TKIs influence cardiovascular homeostasis, potentially leading to hypertension, cardiac dysfunction, and thromboembolism.4 The potency and selectivity of VEGF-TKIs against VEGF receptors vary, with probable effects on cardiovascular risks.1,2 In clinical trials, VEGF-TKIs have increased the risks of heart failure and arterial thromboembolism,5–7 but the effect on the risk of venous thromboembolism remains controversial.8,9

Evidence regarding the cardiovascular risks associated with various VEGF-TKIs has mostly been obtained from meta-analyses of randomized control trials (RCTs) and retrospective studies, with head-to-head (RCTs) being relatively rare. One retrospective study demonstrated that axitinib, a selective VEGF-TKI, may induce higher blood pressure than less-selective VEGF-TKIs.10 One RCT meta-analysis revealed that the risk of heart failure was similar between selective and nonselective VEGF-TKIs,6 and a Bayesian network meta-analysis revealed that VEGF-TKIs are associated with varying risks of hypertension, cardiovascular events, and cardiotoxicity.11 However, these studies were either conventional meta-analysis that compared VEGF-TKIs with placebo, or they defined cardiovascular toxicity as composite cardiovascular outcomes, including heart failure, cardiac ischemia, arrhythmia, and QT prolongation. Studies are needed to clarify the risk of major adverse cardiovascular events (MACEs) associated with VEGF-TKIs for individualized risk management.

This systemic review and network meta-analysis thus analyzed evidence from phase II and III RCTs to understand the relative risk of MACEs, including heart failure, thromboembolism, and cardiovascular death, associated with 11 VEGF-TKIs according to their potency and selectivity against VEGF receptors. VEGF-TKIs were grouped as follows: high-potency and high-selectivity (axitinib and tivozanib), high-potency and low-selectivity (cabozantinib and lenvatinib), low-potency and intermediate-selectivity (pazopanib and vandetanib), and low-potency, and low-selectivity (ponatinib, ripretinib, regorafenib, sorafenib, and sunitinib) VEGF-TKIs (Fig. 1).1,12–19

Fig. 1.

Fig. 1

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Potency and selectivity of vascular endothelial growth factor tyrosine kinase inhibitors (VEGF-TKIs). The numbers represent the VEGF-TKI concentration required for 50% inhibition of various kinases (IC50, nM). Potency indicates inhibitory ability against vascular endothelial growth factor receptor (VEGFR). Selectivity indicates a tendency to inhibit off-target kinases other than VEGFR. Green color represents VEGFR potency: light green indicates high potency (IC50 < 1.0 nM); dark green indicates low potency (IC50 > 1.0 nM). Orange color represents VEGFR selectivity; light orange indicates selective inhibition (IC50 < 100 nM); dark orange indicates nonselective inhibition (IC50 100-600 nM). Gray color represents IC50 > 600 nM or not reported. ABL = Abelson murine leukemia viral oncogene homolog; AXL = AXL receptor tyrosine kinase; CSF-1R = colony-stimulating factor 1 receptor; c-KIT = Mast/stem cell growth factor receptor; c-MET = mesenchymal-epithelial transition factor; EGFR = endothelial growth factor receptor; FLT3 = FMS-like receptor tyrosine kinase-3; PDGFR = platelet-derived growth factor receptor; RAF = rapidly accelerated fibrosarcoma; RET = rearranged during transfection; Src = sarcoma; TIE2 = proangiogenic tunica intima endothelial kinase 2; TKRB = tropomyosin receptor kinase B.1,2–19

2. METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension Statement for Systematic Reviews Incorporating Network Meta-Analysis (Supplemental Method S1, http://links.lww.com/JCMA/A224)20 and was registered in an international registry of systematic reviews (PROSPERO [The International Prospective Register of Systematic Reviews] CRD42023423053).

2.1. Search strategy and selection criteria

We searched PubMed, Embase, and ClinicalTrials.gov from inception until August 31, 2021. Two authors (Y.C.C. and F.I.H.) independently screened abstracts and full-text articles and resolved disagreements by reaching a consensus. The search strategy and keywords are provided in Supplemental Methods S2 and S3 (http://links.lww.com/JCMA/A224). We included phase II or III RCTs of targeted VEGF-TKIs versus placebo, best supportive care, or other VEGF-TKIs for adult patients with cancer. For VEGF-TKIs combined with standard pharmacotherapy, the trial comparators should be the same standard pharmacotherapy with or without placebo (open-label trials). The RCTs should report the number of cardiovascular adverse events. The selection process and exclusion criteria are provided in Fig. 2. We did not exclude trials based on publication language, sample size, or treatment dose.

Fig. 2.

Fig. 2

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Flow diagram of trial identification and selection.

2.2. Data analysis

This study collected data on adverse events comprising heart failure, thromboembolism, and cardiovascular death, as defined by the Common Terminology Criteria for Adverse Events. Heart failure included congestive heart failure, cardiopulmonary failure, left ventricular systolic dysfunction, and pulmonary edema. Thromboembolism included overall thromboembolism, arterial thromboembolism, venous thromboembolism, myocardial infarction, cardiac ischemia, cardiac chest pain, cerebrovascular stroke, transient ischemic attack, pulmonary embolism, and deep vein thrombosis. Cardiovascular death included cardiac arrest, sudden death, and death associated with any cardiovascular adverse event. MACEs were defined as a composite of heart failure, thromboembolism, and cardiovascular death, after excluding repetitive events. If the information was available, we analyzed arterial thromboembolism (myocardial infarction, cardiac ischemia, cardiac chest pain, cerebrovascular stroke, and transient ischemic attack) and venous thromboembolism (pulmonary embolism and deep vein thrombosis).

Two authors (Y.C.C. and F.I.H.) independently assessed trial quality by using the Cochrane Risk-of-Bias tool for randomized trials, version 2. A third author (J.H.C.) resolved any disagreements (Supplemental Method S4 and S5, http://links.lww.com/JCMA/A224). We extracted key information from each RCT, including first author, publication year, trial phase and site(s), cancer type(s) and stage(s), number of participants, median age, male proportion, treatment regimens of experimental and control groups, prior chemotherapy of patients, and cardiovascular adverse events. To assess the credibility of network meta-analyses, we used the Confidence in Network Meta-Analysis (CINeMA) web application.21,22 Within the Grading of Recommendations, Assessment, Development, and Evaluations framework, the CINeMA approach assesses within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence to classify evidence confidence as very low, low, moderate, or high (Supplemental Method S6, http://links.lww.com/JCMA/A224).

2.3. Statistical analysis

We conducted a meta-analysis of direct comparisons of VEGF-TKIs and control treatments to determine their associations with MACEs, heart failure, thromboembolism, and cardiovascular death using R software (version 4.1.1) and its meta package. The controls were placebo, best supportive care, or the same pharmacotherapy but without VEGF-TKIs. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using the Mantel-Haenszel method. Between-study heterogeneity was evaluated using the I2 statistic. We estimated the fixed-effects OR (ORFE) if I2 ≤ 50% (low heterogeneity) and the random-effects OR (ORRE) if the I2 > 50% (high heterogeneity).23 We applied continuity correction with 0.5 if zero events existed in any arm of the RCTs. Funnel plots and Peters’ test were used to examine publication bias.24 Using the Mantel-Haenszel method, subgroup analyses were conducted to assess the difference in risk associated with various cancer types and VEGF-TKIs with or without combined or prior pharmacotherapy. We conducted sensitivity analyses of the cardiovascular risk of VEGF-TKI monotherapy without concurrent pharmacotherapy, of venous thromboembolic risk after excluding vandetanib RCTs, and of publication bias after trials with 95% CIs exceeding the 95% CIs of funnel plots were excluded.

We conducted a network meta-analysis by using the CINeMA web application. Treatment networks were plotted with nodes for VEGF-TKIs and edge sizes for trial sizes. We performed pairwise meta-analyses with the ORRE of MACEs, heart failure, thromboembolism, and cardiovascular death separately. League tables with the ORRE (95% CI) of VEGF-TKIs were constructed. A global test based on a random-effects design-by-treatment interaction was used to investigate inconsistency between direct and indirect evidence, with a significance level of <0.05.21,25,26 We calculated ranked probability with P-scores by using R software (R Foundation for Statistical Computing, Vienna, Austria) version 4.1.1 and its netmeta package.

3. RESULTS

We identified 8222 articles in PubMed, EMBASE, and other databases (Fig. 2). After removing duplicates and screening article abstracts, we selected 69 phase II or III RCTs for meta-analysis, including 41 phase III RCTs (68.3%) (Table 1). No ponatinib trials were selected because of the lack of appropriate comparators. The participant number totaled 30 180, with a median age of 60.0 (interquartile range 5.63) years and a male proportion of 62.8%. We included articles on 16 types of malignancy; non–small-cell lung carcinoma (13 trials), renal cell carcinoma (12), breast cancer (4), and hepatocellular carcinoma (4), which represented 47.8% of RCTs. Of the RCTs, 94.2% assessed locally advanced, metastatic, extensive, or recurrent malignancy. Thirty-seven RCTs evaluated VEGF-TKI monotherapy (53.6%), and 39 RCTs included ≥50% of participants who had received prior cancer pharmacotherapy (56.5%). The median follow-up was 17.9 months, but 35 RCTs did not specify the follow-up duration. Of the RCTs, 75.7% exhibited low risk of bias (Supplemental Method S5, http://links.lww.com/JCMA/A224).

Table 1.

Characteristics of vascular endothelial growth factor—tyrosine kinase inhibitor randomized controlled trials

Study Cancer Stage Size Age Male (%) Treatment regimen Prior therapy (%) Phase Sites
Heymach (2007) NSCLC IIIB/IV 127 59 57 Vandetanib + docetaxel vs docetaxel + placebo 100 2 Multicenter
Escudier (2007) RCC Advanced 903 58.5 73 Sorafenib vs placebo 82.2 3 Multicenter
Arnold (2007) SCLC Limited/extensive 107 59.7 55 Vandetanib vs placebo 100 2 Multicenter
Llovet (2008) HCC BCLC B/C 602 65.6 87 Sorafenib vs placebo <3 3 Multicenter
Spano (2008) Pancreas Advanced/metastatic 103 63 52 Axitinib + gemcitabine vs gemcitabine 8.7 2 Multicenter
Horti (2009) Prostate IV 86 67 100 Vandetanib + docetaxel/prednisolone vs placebo + docetaxel/prednisolone 100 2 Multicenter
Abou-Alfa (2010) HCC Advanced 96 65.5 76 Sorafenib + doxorubicin vs placebo + doxorubicin 0 3 Multicenter
Scagliotti (2010) NSCLC III/IV 926 62.5 63 Sorafenib + paclitaxel/carboplatin vs placebo + paclitaxel/carboplatin 0 3 Multicenter
Sternberg (2010) RCC Advanced/metastatic 435 59.5 71 Pazopanib vs placebo 46.3 3 Multicenter
Mayer (2010) Breast Metastatic 46 55 0 Sunitinib + paclitaxel/bevacizumab vs paclitaxel/bevacizumab 69.5 2 Multicenter
Herbst (2010) NSCLC IIIB/IV 1391 59 70 Vandetanib + docetaxel vs placebo + docetaxel 100 3 Multicenter
Rini (2011) RCC Advanced 723 61 73 Axitinib vs sorafenib 100 3 Multicenter
Rugo (2011) Breast IV/recurrent 168 55.5 0 Axitinib + docetaxel vs placebo + docetaxel 72 2 Multicenter
Raymond (2011) NET Advanced/metastatic 171 56.5 50 Sunitinib vs placebo 69.0 3 Multicenter
Boer (2011) NSCLC IIIB/IV 534 60 62 Vandetanib + pemetrexed vs placebo pemetrexed 100 3 Multicenter
Wang (2011) NSCLC IIIB/IV 30 55 57 Sorafenib + cisplatin/gemcitabine vs placebo + cisplatin/gemcitabine 0 2 China
Bergh (2012) Breast IV/recurrent 593 55 0 Sunitinib + docetaxel vs docetaxel 82.8 3 Multicenter
Paz-Ares (2012) NSCLC IIIB/IV 904 58.5 62 Sorafenib + cisplatin/gemcitabine vs placebo + cisplatin/gemcitabine 0 3 Multicenter
Van der Graaf (2012) Sarcoma IV/recurrent 369 54.3 42 Pazopanib vs placebo 100 3 Multicenter
Gonçalves (2012) Pancreas Advanced/metastatic 104 62.5 61 Sorafenib + gemcitabine vs placebo + gemcitabine 20.9 3 Multicenter
Scagliotti (2012) NSCLC IIIB/IV 960 61 61 Sunitinib + erlotinib vs placebo + erlotinib 100 3 Multicenter
Wells Jr (2012) Thyroid Advanced/metastatic 331 52.1 58 Vandetanib vs placebo 39.9 3 Multicenter
Lee (2012) NSCLC IIIB/IV 160 60 47 Vandetanib vs placebo 100 3 Multicenter
Gradishar (2012) Breast IIIB/C or IV 237 51.6 0 Sorafenib + placlitaxel vs placebo + placlitaxel 57.8 2b Multicenter
Schwartzberg (2013) Breast IIIB/C or IV 160 53.9 0 Sorafenib + gemcitabine/capecitabine vs placebo + gemcitabine/capecitabine 95.0 2b Multicenter
Hutson (2013) RCC Metastatic 192 58 73 Axitinib vs sorafenib 0 3 Multicenter
Groen (2013) NSCLC IIIB/IV 132 60 66 Sunitinib + erlotinib vs placebo + erlotinib 100 2 Multicenter
Motzer (2013) RCC IV/recurrent 517 59 72 Tivozanib vs sorafenib 29.8 3 Multicenter
Elisei (2013) Thyroid Advanced/metastatic 330 55 68 Cabozantinib vs placebo 38.8 3 Multicenter
Johnston (2013) Breast III/IV 190 52 0 Pazopanib + lapatinib vs lapatinib 51.1 2 Multicenter
Demetri (2013) GIST Advanced/metastatic 240 60.5 64 Regorafenib vs placebo 100 3 Multicenter
Grothey (2013) CRC IV 1052 61 62 Regorafenib vs placebo 100 3 Multicenter
Motzer (2013) RCC Advanced/metastatic 1110 61.5 74 Pazopanib vs sunitinib 0 3 Multicenter
Cristofanilli (2013) Breast III/IV 150 52.8 0 Pazopanib + lapatinib vs placebo + lapatinib 100 2 Multicenter
Carrato (2013) CRC Metastatic 768 58.5 56 Sunitinib + FOLFIRI vs placebo + FOLFIRI 78.5 3 Multicenter
Du Bois (2014) Ovary II-IV 940 56.5 0 Pazopanib vs placebo 100 3 Multicenter
Gridelli (2014) NSCLC IIIB/IV 124 75.3 73 Vandetanib + gemcitabine vs placebo + gemcitabine 0 2 Multicenter
Brose (2014) Thyroid Advanced/metastatic 417 63 50 Sorafenib vs placebo 3.1 3 Multicenter
Belani (2014) NSCLC IIIB/IV 170 60.5 64 Axitinib + pemetrexed/cisplatin vs pemetrexed/cisplatin 0 2 Multicenter
Michaelson (2014) Prostate Metastatic 873 68.5 100 Sunitinib + prednisone vs placebo + prednisone 100 3 Multicenter
Schlumberger (2015) Thyroid Metastatic 392 62.5 51 Lenvatinib vs placebo 100 3 Multicenter
Lee (2015) GBM All Stage 114 57 62 Vandetanib + radiotherapy/temozolomide vs radiotherapy/temozolomide 0 2 Multicenter
Bergmann (2015) Pancreas Advanced/metastatic 106 63.9 54 Sunitinib + gemcitabine vs gemcitabine 0 2 Multicenter
Li (2015) CRC Metastatic 243 56.5 58 Regorafenib vs placebo 100 3 Multicenter
O’Brien (2015) NSCLC IIIB/IV 600 64.4 46 Pazopanib vs placebo 100 3 Multicenter
Bruix (2015) HCC Respectable 1114 59 82 Sorafenib vs placebo 0 3 Multicenter
Kang (2015) HCC Advanced/metastatic 202 62 81 Axitinib vs placebo 100 2 Multicenter
Röllig (2015) AML Low-high risk 276 50 50 Sorafenib + daunorubicin/cytarabine vs placebo + daunorubicin/cytarabine 0 2 Multicenter
Moehler (2016) Gastric IV/recurrent 91 59.5 70 Sunitinib + FOLFIRI vs placebo + FOLFIRI 100 2 Multicenter
Ravaud (2016) RCC Locoregional 615 57.5 74 Sunitinib vs placebo 0 3 Multicenter
Mir (2016) Sarcoma Advanced/metastatic 182 60 51 Regorafenib vs placebo 97.8 2 Multicenter
Haas (2016) RCC I–III 1943 56 70 Sunitinib vs sorafenib vs placebo 0 3 Multicenter
Neal (2016) NSCLC IV/recurrent 125 64.9 47 Cabozantinib + erlotinib vs erlotinib 100 2 Multicenter
Sanborn (2017) SCLC Extensive 74 63.5 56 Vandetanib + platinum/etoposide vs placebo + platinum/etoposide 0 2 Multicenter
Middleton (2017) Pancreas Advanced/metastatic 142 67 42 Vandetanib + gemcitabine vs placebo + gemcitabine 0 2 Multicenter
Bruix (2017) HCC BCLC B-C 573 63 88 Regorafenib vs placebo 100 3 Multicenter
Motzer (2017) RCC Localized/advanced 1538 58 71 Pazopanib vs placebo 0 3 Multicenter
Chekerov (2018) Ovary FIGO I–IV 174 58.5 0 Sorafenib + topotecan vs placebo + topotecan 100 2 Multicenter
Kudo (2018) HCC BCLC B-C 954 62.5 85 Lenvatinib vs sorafenib 0 3 Multicenter
Abou-Alfa (2018) HCC Advanced/metastatic 707 69 83 Cabozantinib vs placebo 100 3 Multicenter
Gross-Goupil (2018) RCC Regional 724 58 74 Axitinib vs placebo 0 3 Multicenter
Choueiri (2018) RCC Advanced/metastatic 157 63.5 82 Cabozantinib vs sunitinib 0 2 Multicenter
Sanoff (2018) CRC IV/recurrent 181 62.5 54 Regorafenib vs placebo 100 2 Multicenter
Eisen (2020) RCC Nonmetastatic 1711 58.5 73 Sorafenib vs placebo 0 3 Multicenter
Penel (2020) Sarcoma Metastatic 37 60.5 24 Regorafenib vs placebo 100 2 Multicenter
Blay (2020) GIST Advanced/progression 129 62 57 Ripretinib vs placebo 100 3 Multicenter
Xuan (2020) AML Low-high risk 202 35 50 Sorafenib vs best supportive care 100 3 Multicenter
Sinn (2020) Pancreas Respectable 122 63 58 Sorafenib + gemcitabine vs placebo + gemcitabine 0 2b Multicenter
Brose (2021) Thyroid IV/recurrent 187 65.5 45 Cabozantinib vs placebo 100 3 Multicenter
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References of selected studies were listed in the supplemental Method S3 http://links.lww.com/JCMA/A224.

AML = acute myeloid leukemia; CRC = colorectal cancer; FOLFIRI = leucovorin, fluorouracil, irinotecan; GBM = glioblastoma multiforme; GIST = gastrointestinal stromal tumor; HCC = hepatocellular carcinoma; NET = neuroendocrine tumor; NSCLC = non–small-cell lung carcinoma; RCC = renal cell carcinoma; SCLC = small-cell lung carcinoma.

a

Median follow-up duration was 17.9 months, after 35 randomized control trials without information of follow-up duration were excluded.

Conventional meta-analysis revealed that compared with controls, patients receiving VEGF-TKIs had higher risks of MACEs (ORFE: 1.58; 95% CI, 1.33-1.89; I2 = 0%), namely heart failure (ORFE: 2.58; 95% CI, 1.63-4.10; I2 = 0%), overall thromboembolism (ORFE: 1.34; 95% CI, 1.10-1.64; I2 = 0%), and arterial thromboembolism (ORFE: 1.58; 95% CI, 1.10-2.26; I2 = 0%). By contrast, no increased risk of venous thromboembolism (ORFE: 1.20; 95% CI, 0.91-1.60; I2 = 0%) or cardiovascular death (ORFE: 1.52; 95% CI, 0.97-2.37; I2 = 0%) was identified (Supplementary Figs. S1–S7, http://links.lww.com/JCMA/A224). Fig. 3 presents the VEGF-TKI networks for different MACEs. Fig. 4 and Supplementary Fig. S8 (http://links.lww.com/JCMA/A224) display the relative risks of MACEs between specific VEGF-TKIs and control treatments in the network meta-analysis. Fig. 5 presents the P-scores for the ranked probability of MACEs among VEGF-TKIs.

Fig. 3.

Fig. 3

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Network plots of major adverse cardiovascular events. Networks were plotted with nodes for vascular endothelial growth factor tyrosine kinase inhibitors and edge sizes for trial sizes. Sorafenib (SOR), sunitinib (SUN), pazopanib (PAZ), vandetanib (VAN), cabozantinib (CAB), axitinib (AXI), ponatinib (PON), lenvatinib (LEN), regorafenib (REG), tivozanib (TIV), ripretinib (RIP), and control (CTR).

Fig. 4.

Fig. 4

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Forest plots of vascular endothelial growth factor tyrosine kinase inhibitors (VEGF-TKIs) versus controls for major adverse cardiovascular events. Light blue: high potency and high selectivity against vascular endothelial growth factor receptor (VEGFR). Dark blue: high potency and low selectivity against VEGFR. Light red: low-potency and intermediate-selectivity against VEGFR. Dark red: low potency and low selectivity against VEGFR. The odds ratios represent VEGF-TKI versus placebo, best supportive care, and the same pharmacotherapy without VEGF-TKIs. The forest plots reveal joint effects of direct and indirect comparisons with random-effect odds ratios and their confidence intervals. Dashed line indicates odds ratio equal 1. CV death = cardiovascular death; MACE = major adverse cardiovascular events; TE = thromboembolism.

Fig. 5.

Fig. 5

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P-score bar graph for the ranked probability of major adverse cardiovascular events among vascular endothelial growth factor tyrosine kinase inhibitors (VEGF-TKIs). Light blue indicates high potency and high selectivity against vascular endothelial growth factor receptor (VEGFR). Dark blue indicates high potency and low selectivity against VEGFR. Light red indicates low potency and intermediate selectivity against VEGFR. Dark red indicates low potency and low selectivity against VEGFR. Dashed line indicates the ranked probability of placebo, best supportive care, and the same pharmacotherapy without VEGF-TKIs. CV death = cardiovascular death; MACE = major adverse cardiovascular events; TE = thromboembolism.

3.1. Risk of major cardiovascular events

High-potency VEGF-TKIs, namely tivazonib (ORRE: 3.34; 95% CI, 1.25-8.92) and lenvatinib (ORRE: 3.26; 95% CI, 1.19-8.90), were associated with the highest risk of MACEs, followed by low-potency VEGF-TKIs, namely pazopanib (ORRE: 1.79; 95% CI, 1.18-2.71), sorafenib (ORRE: 1.77; 95% CI, 1.31-2.39), and sunitinib (ORRE: 1.66; 95% CI, 1.15-2.38). Relative to other VEGF-TKIs, vandetanib, a low-potency and intermediate-selectivity VEGF-TKI, was associated with a significantly lower risk (Supplementary Table S1, http://links.lww.com/JCMA/A224). High-potency VEGF-TKIs, namely tivozanib, lenvatinib, and axitinib, were associated with the highest probabilities (top 3 ranked) of MACEs.

3.2. Risk of heart failure

Low-potency, less-selective VEGF-TKIs were significantly associated with an increased risk of heart failure; these included sorafenib (ORRE: 3.53; 95% CI, 1.37-9.10), pazopanib (ORRE: 3.10; 95% CI, 1.49-6.46), and sunitinib (ORRE: 2.65; 95% CI, 1.26-5.58). Among the 10 VEGF-TKIs, no significant difference in risk was observed (Supplementary Table S2, http://links.lww.com/JCMA/A224), but the highest probability of heart failure was observed for high-potency, selective tivozanib, followed by low-potency and less-selective VEGF-TKIs (sorafenib, vandetanib, and pazopanib).

3.3. Risk of thromboembolism

The highest risk of thromboembolism was observed for three low-selectivity VEGF-TKIs, namely lenvatinib (ORRE: 3.12; 95% CI, 1.13-8.61), sorafenib (ORRE: 1.54; 95% CI, 1.10-2.16), and sunitinib (ORRE: 1.53; 95% CI, 1.00-2.33). The highest probability of thromboembolism was found for high-potency and low-selectivity VEGF-TKIs, namely lenvatinib and cabozantinib. The lowest risk and ranked probability was observed for low-potency, intermediate-selectivity vandetanib (ORRE: 0.55; 95% CI, 0.33-0.92) (Supplementary Table S3 http://links.lww.com/JCMA/A224).

The highest risk of arterial thromboembolism was observed for low-potency, low-selectivity sorafenib (ORRE: 1.81; 95% CI, 1.06-3.10) (Supplementary Table S4 http://links.lww.com/JCMA/A224). The 3 VEGF-TKIs most strongly associated with arterial thromboembolism were low-selectivity lenvatinib, sorafenib, and sunitinib. The highest risk of venous thromboembolism was observed for high-potency, low-selectivity cabozantinib (ORRE: 5.46; 95% CI, 1.00-29.68). The strongest risk of venous thromboembolism was observed for high-potency, low-selectivity cabozantinib and lenvatinib. Low-potency, intermediate-selectivity vandetanib was associated with a reduced risk (ORRE: 0.51; 95% CI, 0.30-0.88) (Supplementary Table S5, http://links.lww.com/JCMA/A224). Vandetanib was also associated with the lowest probability of venous thromboembolism. After vandetanib trials were removed, VEGF-TKIs were associated with an increased risk of venous thromboembolism in the conventional meta-analysis (ORFE: 1.73; 95% CI, 1.21-2.47; I2 = 0%, Supplementary Fig. S7, http://links.lww.com/JCMA/A224).

3.4. Risk of cardiovascular death

No significantly increased risk of cardiovascular death was identified among VEGF-TKIs (Supplementary Table S6, http://links.lww.com/JCMA/A224). The highest probability of cardiovascular death was observed for high-potency, high-selectivity tivozanib and low-potency, low-selectivity sunitinib.

3.5. Sensitivity analysis and subgroup analysis

After VEGF-TKI-based combination trials were removed, VEGF-TKI monotherapy was associated with similarly increased risks of MACEs (ORFE: 1.73; 95% CI, 1.35-2.22; I2 = 0%), namely heart failure (ORFE: 1.91; 95% CI, 1.05-3.48; I2 = 0%) and thromboembolism (ORFE: 1.62; 95% CI, 1.22-2.15; I2 = 13%) but no increased risk of cardiovascular death (ORFE: 1.35; 95% CI, 0.75-2.41; I2 = 0%) (Supplementary Figs. S9-S12, http://links.lww.com/JCMA/A224). In subgroup analysis (Supplementary Tables S7-S10, http://links.lww.com/JCMA/A224), no difference in risks was observed between treatment regimens (VEGF-TKI monotherapy versus combination therapy: subgroup differences [PSD] = 0.31 for MACEs, 0.15 for heart failure,.07 for thromboembolism, and 0.53 for cardiovascular death; prior pharmacotherapy versus no prior pharmacotherapy: PSD = 0.73 for MACE, 0.28 for heart failure, 0.91 for thromboembolism, and 0.82 for cardiovascular death). Breast cancer (ORFE: 3.68; 95% CI, 1.65-8.17; I2 = 0%), thyroid cancer (ORFE: 3.40; 95% CI, 1.62-7.14; I2 = 0%), hepatocellular carcinoma (ORFE: 2.42; 95% CI, 1.17-4.98; I2 = 2.8%), sarcoma (ORFE: 2.07; 95% CI, 1.00-4.28; I2 = 1.4%), and renal cell carcinoma (ORFE: 1.65; 95% CI, 1.12-2.41; I2 = 14.3%) were associated with a higher risk of MACEs (PSD = 0.03), but the cancer type did not significantly modify the risk of heart failure (PSD = 0.35), thromboembolism (PSD = 0.15), or cardiovascular death (PSD = 0.71).

3.6. Publication bias and evidence quality

Funnel plots and Peters’ test revealed no significant publication bias (Supplementary Figs. S13-S16, http://links.lww.com/JCMA/A224). After excluding two RCTs with 95% CIs exceeding the 95% CI of the funnel plots for MACEs and thromboembolism, the sensitivity analysis revealed no significant publication bias (p = 0.87 for MACEs and 0.34 for thromboembolism). The global test based on the random-effects design-by-treatment interaction test revealed no significant inconsistency between direct and indirect evidence (p = 0.05 for MACEs, 0.80 for heart failure, 0.16 for thromboembolism, 0.53 for cardiovascular death, 0.11 for arterial thromboembolism). The risks of venous thromboembolism came mainly from indirect evidence because only one head-to-head VEGF-TKI comparison trial was selected (Fig. 3). The credibility for most pairwise meta-analyses was rated as low based on the CINeMA “imprecision” criteria because of wide 95% CIs (Supplemental Method S6 and Tables S11-S16, http://links.lww.com/JCMA/A224).

4. DISCUSSION

Our analysis revealed that VEGF-TKIs may increase the risks of MACEs, namely heart failure, overall thromboembolism, and arterial thromboembolism, but not the risks of cardiovascular death and venous thromboembolism. Compared with control treatments, high-potency VEGF-TKIs (tivazonib and lenvatinib) were associated with a high risk of MACEs than were low-potency VEGF-TKIs (pazopanib, sorafenib, and sunitinib). A high risk of heart failure was observed for low-potency, less-selective VEGF-TKIs (sorafenib, pazopanib, and sunitinib). A high risk of thromboembolism, including arterial thromboembolism (sorafenib) and venous thromboembolism (cabozantinib), was evident for low-selectivity VEGF-TKIs (lenvatinib, sorafenib, and sunitinib). A reduced risk of venous thromboembolism was observed for low-potency, intermediate-selectivity vandetanib. Higher potency (tivozanib and axitinib) and lower selectivity (sorafenib, vandetanib, pazopanib, and sunitinib) VEGF-TKIs were associated with higher probabilities of heart failure. Low-selectivity (lenvatinib, cabozantinib, sorafenib, and sunitinib) VEGF-TKIs were associated with higher probabilities of thromboembolism. The cancer type may modify the risk of MACEs, but treatment regimens did not affect the risk of MACEs.

RCT meta-analyses have demonstrated that VEGF-TKIs increase the risks of heart failure and arterial thromboembolism,5–7 but not that of venous thromboembolism.8,9 One Bayesian network meta-analysis revealed that cabozantinib, lenvatinib, and vandetanib were associated with higher risks of cardiovascular events and hypertension, and that axitinib, pazopanib, sorafenib, sunitinib, and vandetanib were associated with a higher probability of cardiac toxicity.11 Our analysis also revealed an increased MACE risk for lenvatinib, pazopanib, sorafenib, and sunitinib. However, in our analysis, vandetanib reduced the MACE risk, with this effect being driven by the impact on venous thromboembolism. This discrepancy is probably attributable to different outcome definitions: cardiotoxicity was defined as heart failure, cardiac ischemia, arrhythmia, and QT prolongation in the Bayesian analysis.11 Vandetanib may induce QT prolongation, but without the observed risks of sudden cardiac death or ventricular arrhythmia.27 In our analysis, MACEs were defined as heart failure, thromboembolism, and cardiovascular death, and patients receiving combined or prior pharmacotherapy were selected in this study. Thus, our analysis adopted a different perspective to understand VEGF-TKI cardiovascular risks.

VEGF-TKIs possess varying potency against VEGF receptors, and different selectivity for off-target kinases,1 and such variance is probably responsible for different cardiovascular risk patterns. Axitinib, a selective VEGF-TKI, can increase blood pressure more than less-selective VEGF-TKIs.10 By contrast, one RCT meta-analysis revealed that the risk of heart failure was similar between selective TKIs (axitinib) and less selective TKIs (sunitinib, sorafenib, vandetanib, and pazopanib).6 Another retrospective study that examined the US Food and Drug Administration (FDA) Adverse Event Reporting System database identified that increased signals of heart failure or cardiomyopathy were associated with most VEGF-TKIs, except cabozantinib and regorafenib.28 Our analysis indicated that VEGF-TKIs with higher potency (axitinib and tivozanib) or with lower selectivity (sunitinib, pazopanib, vandetanib, and sorafenib) increased the risks or probabilities of heart failure, suggesting that VEGF-TKIs induce heart failure through multiple mechanisms: For instance, VEGF inhibition leads to dysfunction of endothelial regulation, angiogenesis, and vascular homeostasis.2,4 Genetic polymorphisms on the regulatory protein of platelet-derived growth factor receptor (PDGFR)α were associated with heart failure.29 The mitogen-activated protein kinase pathway through rat sarcoma virus (RAS)-rapidly accelerated fibrosarcoma (RAF)-mitogen-activated protein kinase (MEK)-extracellular signal-regulated kinase (ERK) signaling regulates cardiac remodeling and apoptosis under stress.30 FLT3 signaling provides a protective effect on postinfarction remodeling.31 Plausibly, VEGF-TKIs against PDGFRα (axitinib, sunitinib, and pazopanib), RAF (sorafenib), or FLT3 (sunitinib and sorafenib) promote heart failure, especially under cardiac stress.

Our analysis suggested that less-selective VEGF-TKIs (sorafenib, sunitinib, cabozantinib, and lenvatinib) promote thromboembolism, but vandetanib reduced venous thromboembolism and was associated with a reduced probability of arterial thromboembolism. Kinase inhibitors can increase overall thromboembolism through endothelial damage and coagulation dysregulation or induce arterial thromboembolism through accelerated atherosclerosis or thrombotic microangiopathy.2,4,32–35 One RCT meta-analysis revealed that vandetanib can reduce venous thromboembolism among patients with non–small-cell lung carcinoma.9 Vandetanib is a unique dual inhibitor against VEGF and epidermal growth factor receptor (EGFR).36 EGFR inhibition induces antiangiogenesis,37 possibly contributing to the probability of heart failure from vandetanib, but most oral EGFR-TKIs demonstrated no increased thromboembolic risk.38 Because EGFR inhibitors can attenuate atherosclerosis by reducing T cell-regulated inflammation,32 the lower risk of vandetanib may result from an anti-inflammatory effect or procoagulant malignancy improvement. By contrast, cabozantinib was associated with an increased risk of venous thromboembolism. Cabozantinib inhibits multiple kinases involving anticoagulation (VEGF, TIE2), procoagulation (c-MET), and platelet activation (AXL),33–35 possibly causing coagulation dysregulation. Furthermore, lenvatinib, sorafenib, and sunitinib exhibited the highest probabilities of arterial thromboembolism, and sorafenib significantly increased the risk. VEGF inhibition can induce thrombotic microangiopathy; sorafenib and sunitinib also inhibit FLT3, a kinase that regulates atherosclerosis.39 Our analysis indicated that pooled data may cancel out clinically relevant information, and further studies are needed to clarify complex kinase interaction between MACEs.

Our subgroup analysis demonstrated that cancer types, but not treatment regimens, may modify overall MACE risks. This finding should be interpreted cautiously because of the small event number. In our analysis, malignancies with high thromboembolic risk, such as lung cancer and pancreatic cancer,40 did not exhibit higher MACE risks. The risk interaction between treatment regimen and cancer type still needs to be clarified.

We acknowledge some limitations in our analysis. First, evidence credibility was low because of small event numbers and wide 95% CI for pairwise comparisons. In addition, some comparisons were based solely on indirect evidence because head-to-head RCTs were limited, especially for newly approved VEGF-TKIs. However, our results are compatible with those of earlier studies, and no significant discrepancies between indirect and direct evidence were found. Our results provide a general understanding of major cardiovascular risks among VEGF-TKIs. Second, different from cardiovascular outcome trials, cancer trials defined cardiovascular adverse events with the Common Terminology Criteria for Adverse Events.2 One prospective study indicated that 9.7% of patients experienced asymptomatic reduced cardiac function,41 but most cancer RCTs did not monitor heart function. Furthermore, cancer trials often exclude participants with cardiovascular comorbidities.2,42 Thus, the cardiovascular risks may be underestimated. Third, the heterogeneity of trial designs, treatment regimens, and cancer types was high, although most I2 statistics were <50%. Furthermore, 35 trials did not specify the follow-up periods. However, our subgroup analyses and sensitivity analyses revealed no differences in risks between treatment regimens and cancer types. Fourth, our analysis evaluated trial-level summary data without time information for individual events, so competing risks among events could not be addressed. Further study with patient-level data is needed. Five, our analysis did not include observational data, reducing the study’s generalizability and limiting the event numbers. However, events from observational data are less valid and have potential bias. Adding observational studies would further increase heterogeneity. Thus, we sacrificed event sizes and used more valid data from RCTs. Future studies incorporating available real-world data for more homogeneous patients are needed to validate our results.

In conclusion, our analysis revealed that VEGF-TKIs with higher potency and lower selectivity may contribute to different risk patterns of MACEs, heart failure, and thromboembolism. Further studies are needed to understand the potential interactions of anti-VEGF potency and off-target inhibition on cardiovascular risks.

ACKNOWLEDGMENTS

This work was supported by Wan Fang Hospital (grant 110wf-phd-01). We thank Chih-Yu Liao, Hsuan-Hui Chen, Meng-Chieh Tsao, Ssu-Yin Chen, Tzu-Chi Ou, Yi-Zi Lu, You-Gang Li from the School of Public Health, College of Public Health, Taipei Medical University for their assistance and inputs in trial quality assessment. This article was edited by Wallace Academic Editing.

APPENDIX A. SUPPLEMENTARY DATA

Supplementary data related to this article can be found at http://links.lww.com/JCMA/A224.

Supplementary Material

ca9-87-048-s001.pdf (4.3MB, pdf)

Footnotes

Author contributions: Dr. Fang-I Hsieh and Dr. Jin-Hua Chen contributed equally to this work.

Conflicts of interest: The authors declare that they have no conflicts of interest related to the subject matter or materials discussed in this article.

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Supplementary Materials

ca9-87-048-s001.pdf (4.3MB, pdf)

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