Cardiotoxicity Associated with Targeted Therapies in Lung Cancer: A Retrospective Cohort Study of 2,427 Patients at Renji Hospital

Cardiotoxicity Associated with Targeted Therapies in Lung Cancer: A Retrospective Cohort Study of 2,427 Patients at Renji Hospital | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Cardiotoxicity Associated with Targeted Therapies in Lung Cancer: A Retrospective Cohort Study of 2,427 Patients at Renji Hospital View ORCID Profile Zhixuan Zhang doi: https://doi.org/10.1101/2025.11.13.25340140 Zhixuan Zhang a Department of Cardiology, State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai, China b Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital , Shanghai, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zhixuan Zhang For correspondence: 19821838993{at}163.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Targeted therapies are central to lung cancer treatment, yet their cardiotoxic effects across drug classes remain inadequately characterized in real-world populations. The comparative burden of subclinical biomarker-defined injury, cycle-dependent risks, and post-marketing safety signals across therapeutic targets has not been fully evaluated. Methods We conducted a retrospective cohort study of 2,427 lung cancer patients treated with 82 targeted agents at Renji Hospital (2018–2024). Cardiotoxicity was defined as any new elevation in cardiac biomarkers according to the 2022 ESC cardio-oncology guideline criteria. Multivariable Cox models assessed cardiotoxicity risk across drug targets; restricted cubic splines modeled nonlinear associations between PD-1 inhibitor administration frequency and cardiotoxicity; and longitudinal biomarker trajectories were analyzed using generalized additive mixed models. To complement hospital-based findings, we performed independent pharmacovigilance analyses using 4,249 cardiac adverse event reports from the FDA Adverse Event Reporting System (FAERS). Disproportionality signals were quantified using proportional reporting ratios (PRRs). Results Among 2,069 analyzable patients, 326 (15.8%) developed biomarker-defined cardiotoxicity. PD-1 inhibitors showed the highest adjusted risk (HR 1.81; 95% CI 1.43–2.28), followed by VEGF inhibitors (HR 1.33; 95% CI 1.06–1.67), whereas EGFR inhibitors were associated with lower incidence (HR 0.61; 95% CI 0.48–0.78). PD-1–related cardiotoxicity demonstrated a nonlinear exposure–response relationship, with risk rising steeply up to approximately four treatment cycles. Longitudinal analyses showed marked divergence in B-type natriuretic peptide (BNP) and creatine kinase–MB (CK-MB) trajectories among PD-1–treated patients, while troponin levels remained largely unchanged. FAERS analyses corroborated these findings: PD-1 inhibitors exhibited the strongest disproportionality signals for severe cardiac adverse events (PRR 1.84; 95% CI 1.68–2.02), including immune-mediated myocarditis (PRR 28.41) and autoimmune myocarditis (PRR 42.23). Conclusions Across both a real-world clinical cohort and a large post-marketing pharmacovigilance dataset, PD-1 inhibitors were consistently associated with the highest cardiotoxicity burden, characterized by an early cycle-dependent rise in risk and distinct elevations in BNP and CK-MB. Combined hospital-based and FAERS evidence supports target-specific cardiac surveillance strategies during lung cancer targeted therapy. Introduction Lung cancer remains the leading cause of cancer-related death worldwide ( 1 ). Over the past decade, substantial progress in early detection and treatment has been achieved through advances in molecularly targeted and translational research ( 1 ). The introduction of targeted agents such as epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors, vascular endothelial growth factor (VEGF) inhibitors, and immune checkpoint inhibitors particularly programmed death-1 (PD-1) antibodies has transformed the therapeutic landscape, substantially improving progression-free and overall survival ( 2 ). However, as survival improves and treatment duration extends, cardiovascular toxicities have become increasingly recognized as important adverse effects that may offset therapeutic benefits and affect long-term outcomes ( 3 ). Cardiotoxicity in the context of targeted therapy includes a wide spectrum of manifestations, ranging from asymptomatic elevations in cardiac biomarkers to clinically significant heart failure, arrhythmias, and vascular events ( 3 ). The underlying mechanisms are diverse, involving direct myocardial injury ( 4 ), endothelial dysfunction ( 5 ), and immune-mediated inflammation ( 6 ). Previous studies have reported that PD-1 inhibitors can trigger myocarditis, pericardial disease, and biomarker-defined myocardial injury ( 7 ), whereas VEGF inhibition may induce hypertension, thrombosis, and heart failure ( 8 ). Conversely, EGFR inhibitors have been associated with a relatively low incidence of cardiotoxicity, though cases of thromboembolic events, heart failure, cardiomyopathy, arrhythmia, and hypertension have been reported ( 9 ). Nonetheless, most available data arise from small case series or clinical trial populations with stringent inclusion criteria, limiting their generalizability to real-world patients who often present with advanced age, comorbidities, and polypharmacy ( 10 ). Real-world evidence on the comparative cardiotoxic profiles of different targeted therapies in lung cancer remains scarce ( 11 ). Furthermore, the temporal evolution of cardiac biomarkers in patients exposed to immune checkpoint inhibitors is not well characterized, and the cumulative risk associated with repeated drug administration is poorly defined ( 12 ). Given the growing use of PD-1–based regimens and combinations in clinical oncology, understanding the dose–response and temporal dynamics of cardiotoxicity is of immediate clinical relevance. In this retrospective cohort study, we comprehensively evaluated 2,427 lung cancer patients treated with 82 targeted agents at Renji Hospital over a six-year period. We aimed to quantify the incidence of cardiotoxicity, identify associated demographic and clinical risk factors, and compare the cardiotoxic risk among major targeted therapy classes. In addition, we explored nonlinear associations between PD-1 inhibitor exposure frequency and cardiotoxicity risk and modeled longitudinal trajectories of cardiac biomarkers to elucidate distinct temporal patterns of cardiac response. In parallel, we incorporated post-marketing pharmacovigilance evidence from the FAERS database to characterize targeted-therapy–related cardiac adverse event patterns and to validate class-specific cardiotoxicity signals observed in our real-world cohort. This study provides a detailed, data-driven framework for individualized cardiac monitoring in targeted therapy recipients. Methods Study Design and Population We conducted a retrospective cohort study including all patients diagnosed with lung malignancies with International Classification Disease-10 th Revision (ICD-10) code: C34) who received at least one targeted agent at Renji Hospital between January 1, 2018, and June 26, 2024. A total of 2,427 eligible patients were identified after data cleaning ( Figure 1 ). Patients lacking any cardiac biomarker measurements—creatine kinase-MB (CK-MB), B-type natriuretic peptide (BNP), N-terminal pro-BNP (NT-proBNP), troponin I (TnI), or high-sensitivity troponin I (hs-TnI) were excluded (n = 358), yielding a final analytical cohort of 2,069 individuals. Download figure Open in new tab Figure 1. Study Flow and Analytical Framework Flowchart summarizing cohort selection and analyses. Among 2,427 lung cancer patients treated with targeted therapies, 358 without cardiac biomarker data were excluded, yielding 2,069 for analysis. Patients were stratified into cardiotoxicity (n = 326) and non-cardiotoxicity (n = 1,743) groups. Primary analysis compared cardiotoxicity risk across targeted therapy classes. Secondary analyses included subgroup comparisons, nonlinear PD-1 exposure–response assessment, and longitudinal cardiac biomarker trajectory modeling. Abbreviations: BNP: B-type natriuretic peptide; CK-MB: creatine kinase–myocardial band; PD-1: programmed cell death protein 1; TnI: troponin I; hs-TnI: high-sensitivity troponin I. Cardiotoxicity was defined as a new elevation in any of the 4 cardiac biomarkers (BNP, NT-proBNP, TnI, hs-TnI) following initiation of targeted therapy. The cohort was stratified into patients with cardiotoxicity (n = 326, 15.8%) and without cardiotoxicity (n = 1,743, 84.2%). The primary analysis compared the risk of cardiotoxicity across major targeted therapy classes (EGFR, PD-1, VEGF, ALK, and others) using multivariable Cox proportional hazards models adjusted for age, sex, and cardiovascular comorbidities. The secondary analyses included (a) subgroup analyses by demographic and clinical characteristics, (b) assessment of nonlinear associations between PD-1 inhibitor administration frequency and cardiotoxicity risk using restricted cubic spline models, and (c) evaluation of longitudinal trajectories of cardiac biomarkers via generalized additive mixed models (GAMMs) stratified by PD-1 exposure status. The follow-up period (days from treatment initiation to the first cardiotoxicity event or end of follow-up) was calculated for each patient. Definition of Cardiotoxicity Cardiotoxicity was defined as any new elevation in cardiac biomarkers—troponin I (> 0.04 ng/mL), high-sensitivity troponin (> 0.04 ng/mL), BNP (> 100 pg/mL), CK-MB (> 6.3 ng/mL), or NT-proBNP (> 20 pg/mL)—occurring on or after the first administration of a targeted drug. Cases were further classified per the 2022 ESC cardio-oncology guideline ( 13 ) into: (1) symptomatic CTRCD (heart failure requiring hospitalization or advanced therapy); (2) asymptomatic CTRCD (biomarker elevation only); (3) vascular toxicity; (4) hypertension; and (5) arrhythmia. Drug Classification Eighty-two targeted agents were grouped by their primary molecular targets (EGFR, PD-1, VEGF, ALK, BCR-ABL, HER2, and others; see Supplementary Table 1). Binary exposure variables (yes/no) were created for each target. Drug exposure frequency was calculated as the total number of administration orders documented by treating oncologists. Covariates Baseline variables included age, sex, and pre-existing cardiovascular and metabolic comorbidities diagnosed before initiation of targeted therapy. Comorbidities were identified using ICD-10 codes, and only the earliest diagnosis date was recorded for each condition. Specifically, hypertension was defined by codes I10–I13 and I15 (including hypertensive crisis I16 when applicable). Coronary artery disease and myocardial infarction were identified by I20–I25. Arrhythmias and conduction disorders were defined by I44–I45, I47–I49. Heart failure and cardiomyopathy were captured by I50 and I42 (including drug-induced or secondary cardiomyopathy I42.7), with I51.7 (left ventricular hypertrophy) included when relevant. Thrombotic, atherosclerotic, and cerebrovascular disorders were identified using I26, I61–I64, I65–I67.2, I70–I71, I80–I83, I87.1–I87.2, and I81–I82, encompassing pulmonary embolism, stroke, peripheral arterial disease, and venous thromboembolism. Pulmonary hypertension was defined as I27.2, and valvular heart disease by I34–I37. Diabetes mellitus and dyslipidemia were identified using E10–E14 and E78, respectively. Statistical Analysis Continuous variables were compared using t -tests or Wilcoxon rank-sum tests as appropriate. Categorical variables were analyzed using chi-square tests. Associations between drug exposure and cardiotoxicity were evaluated with multivariable Cox proportional hazards models adjusted for demographic and clinical covariates. Nonlinear dose–response relationships were modeled with restricted cubic spline (RCS) functions. Analyses were performed in R, with two-sided P < 0.05 considered significant. Statistical Modeling of Longitudinal Biomarker Trajectories We used generalized additive mixed models (GAMMs) to evaluate longitudinal trajectories of cardiac biomarkers following treatment initiation, stratified by PD-1 inhibitor exposure status. Biomarkers with sufficient longitudinal data (≥ 30 observations from ≥ 5 patients) were included in the analysis. We focused on four cardiac biomarkers with established clinical relevance for cardiotoxicity surveillance: BNP, CK-MB, myoglobin, and TnI. Two model specifications were compared: ( 1 ) a main-effect model with a single smooth time function shared across exposure groups and ( 2 ) an interaction model with exposure-specific smooth time functions to assess time-by–PD-1 interactions. All models included patient-level random intercepts and were adjusted for age, sex, PD-1 administration frequency, and seven cardiovascular comorbidities (myocardial infarction, hypertension, arrhythmia, thrombosis, diabetes, hyperlipidemia, and heart failure). Model specification Dependent variable : Log-transformed biomarker levels [log (value + 1)] to normalize distributions Primary predictor : Time since treatment initiation (continuous, in days) with exposure-specific smooth functions using penalized thin plate regression splines (k = 8 basis functions) Stratification variable : PD-1 inhibitor exposure status (exposed vs. unexposed) Covariates : age, sex, PD-1 administration frequency, and seven cardiovascular comorbidities (myocardial infarction, hypertension, arrhythmia, thrombosis, diabetes, hyperlipidemia, and heart failure). Random effects : Patient-level intercepts to account for within-subject correlation Models were fitted using the bam() function with fast restricted maximum likelihood (fREML) estimation from the mgcv package in R (version 4.3.1). Predicted biomarker levels with 95% confidence interval (CI) were generated at median covariate values across a grid of time points spanning the observed follow-up period (0–2500 days). Model performance was compared between interaction and main-effect models using likelihood ratio tests and the delta Akaike Information Criterion (ΔAIC), defined as the difference in AIC between competing models. Significant divergence in temporal trajectories was interpreted based on ΔAIC reduction and improved model fit. Marginal R² values were also reported to quantify variance explained by fixed effects; increases in marginal R² indicate improved explanatory power attributable to the interaction term. FDA Adverse Event Reporting System (FAERS) We obtained post-marketing safety data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), a spontaneous reporting database that collects adverse event (AE) and medication error reports submitted by healthcare professionals, consumers, and pharmaceutical manufacturers ( 14 ). FAERS is structured around the FDA’s MedWatch reporting program and contains standardized information on patient demographics, drug exposures, indications, and clinical outcomes. Each submission in FAERS is recorded as an adverse event report (AER), which may contain one or multiple AEs for a given patient. Analyses in the present study were therefore performed at the report level (AER) after harmonizing drug names and consolidating multiple AEs within the same report. To ensure analytical validity, we followed FDA-recommended deduplication procedures and retained only the most recent version of each case. Adverse events in FAERS are coded using Preferred Terms (PTs) from the Medical Dictionary for Regulatory Activities (MedDRA), an internationally standardized terminology system used by regulatory agencies and the biopharmaceutical industry. PTs represent distinct clinically meaningful adverse events and serve as the primary unit for safety signal evaluation. FAERS Cohort Identification and Analytical Procedures We queried the FAERS database from 2004Q1 to 2023Q4 and identified 16,800,135 AERs. Reports involving non–targeted agents were excluded, resulting in 16,240,047 records. The specific agents included within each targeted therapy class are listed in Supplementary Table 2. A total of 560,088 reports containing any targeted therapy exposure were retained for further evaluation ( Figure 5 ). Duplicate reports were removed by matching age, sex, country, indication, suspected drug, event date, and reaction, and only the most recent version was kept. Reports listing secondary suspect drugs and those involving patients aged ≤ 18 years were excluded, yielding 311,491 AERs. Subsequent restriction of indication resulted in 184,689 records, and limiting the cohort to lung cancer produced 63,908 reports. After identifying 4,279 reports of targeted therapy–associated cardiac adverse events (cAEs) in lung cancer, we further excluded cases in which the treatment indication corresponded to primary or secondary cardiac neoplasms. These included “benign cardiac neoplasm”, “cardi ac neoplasm malignant”, “cardiac myxoma”, “pericarditis malignant”, “pericardial mesothelioma malignant”, “pericardial mesothelioma malignant recurrent”, “pericardial effusion malignant”, “metastases to heart”, “cardiac neoplasm unspecified”, “pericardial neoplasm”, “malignant pericardial neoplasm”, “benign pericardium neoplasm”, “cardiac fibroma”, “cardiac haemangioma benign”, “cardiac neurofibroma”, “cardiac teratoma”, “cardiac valve fibroelastoma”, “primary cardiac lymphoma”, “leukemic cardiac infiltration”, “pericardial lipoma”, and “cardiac lipoma”. A total of 30 reports met these exclusion criteria, resulting in a final cohort of 4,249 cardiac adverse event reports. The final analytical cohort consisted of 4,249 reports documenting cAEs. Three analyses were conducted: ( 1 ) comparison of cAEs incidence across targeted therapy classes; ( 2 ) estimation of proportional reporting ratios (PRRs) for severe cAEs (PRR ≥3); and ( 3 ) heatmap-based profiling of PRRs for severe cAEs (PRR ≥ 3) associated with individual targeted therapies. Definition of cAEs For this study, all cardiac adverse events in the overall frequency analysis ( Figure 6A ) were identified based on MedDRA PT codes within the cardiac disorders system organ class. For the disproportionality analysis ( Figure 6B ) and the heatmap analysis ( Figure 6C ), we focused on severe cAEs with documented clinical significance. A total of 25 specific severe cAEs were included: pericardial effusion, myocarditis, cardiac tamponade, immune-mediated myocarditis, atrioventricular block complete, autoimmune myocarditis, pericarditis constrictive, pericardial disease, arrhythmia supraventricular, tachycardia paroxysmal, immune-mediated pericarditis, atrial flutter, cardiac failure acute, cardiac dysfunction, cardiopulmonary failure, long QT syndrome, bradycardia, pericarditis, sinus bradycardia, cardiac fibrillation, acute coronary syndrome, sinus tachycardia, cardiomyopathy, supraventricular tachycardia, and left ventricular dysfunction. These 25 events were selected based on demonstrating a PRR ≥ 3.0 in at least one of the six drug target categories (HER2, EGFR, PD-1/PD-L1, ALK, VEGF, or PD-1 inhibitors), indicating a strong disproportionality signal and clinically meaningful association. Events with fewer than 3 reported cases for a specific drug target were excluded from PRR calculation for that target and designated as "NA" (not available) in the heatmap to ensure statistical reliability. Descriptive characterization of targeted therapy–related cAEs We conducted a descriptive analysis of cAEs associated with targeted therapies in lung cancer in the FAERS database. Clinical and reporting characteristics were summarized across six major targeted drug classes (PD-1, PD-L1, EGFR, HER2, ALK, and VEGF inhibitors). Variables extracted included patient age, sex, and time to onset of cAEs, which was calculated as the interval between therapy initiation and event start date. Annual reporting frequencies were tabulated to reflect temporal adoption patterns of each therapeutic class. Continuous variables are presented as median values with interquartile ranges, and categorical variables are reported as counts and percentages. Statistical Analysis Disproportionality analysis was performed using the proportional reporting ratio (PRR) to assess the association between drug targets and severe cAEs ( 15 , 16 ). PRR and 95% CI was calculated as: where a represents the number of reports with the specific cAE for the drug target of interest, b represents the total number of reports for that drug target, c represents the number of reports with the specific cAE for all other drugs, and d represents the total number of reports for all other drugs. A cAE signal was considered valid when the number of reported cases was at least three and the lower bound of the 95% CI of the PRR exceeded 1.0, indicating a strong association with the corresponding targeted therapy. For each PT, PRR estimates and their 95% CIs were calculated, and statistical significance was further assessed using chi-square tests, with P < 0.01 regarded as significant. Only PTs with at least three reports were retained for the heatmap analysis to ensure signal stability and data reliability. Results Baseline Characteristics All baseline demographic, clinical, and treatment characteristics for both groups are summarized in Table 1 . Of the 2,069 patients included in the analysis, 326 (15.8%) developed cardiotoxicity after targeted therapy. The median follow-up period was 527 days (IQR 192–974). The mean age did not differ significantly between groups ( P = 0.096). Cardiotoxicity was more common in males (67.8% vs. 61.6%, P = 0.038). View this table: View inline View popup Table 1. Baseline characteristics. Pre-existing cardiovascular comorbidities were significantly more prevalent among patients who developed cardiotoxicity, including hypertension (37.7% vs. 19.4%, P < 0.001), myocardial infarction (11.0% vs. 3.1%, P < 0.001), heart failure (4.9% vs. 0.7%, P < 0.001), and diabetes (15.0% vs. 10.2%, P = 0.014). No significant differences were observed in the baseline prevalence of hyperlipidemia or thrombosis. In terms of oncological treatment, patients in the cardiotoxicity group were more likely to receive PD-1 inhibitors (40.5% vs. 30.1%, P < 0.001) and VEGF inhibitors (54.0% vs. 41.3%, P < 0.001), whereas EGFR inhibitor use was lower (34.0% vs. 43.1%, P = 0.003). The cardiotoxicity group also received a higher median number of treatment administrations, including PD-1 inhibitors (5.5 vs. 3.0, P < 0.001), EGFR inhibitors (17.0 vs. 7.0, P < 0.001), VEGF inhibitors (5.0 vs. 4.0, P = 0.002), ALK inhibitors (14.0 vs. 6.0, P = 0.002), and other targeted agents (5.0 vs. 2.0, P < 0.001). Monitoring of cardiac biomarkers was significantly more frequent in patients who developed cardiotoxicity (all P < 0.001). Testing rates were higher for BNP (99.4% vs. 85.5%), CK-MB (98.5% vs. 91.2%), Myoglobin (96.3% vs. 78.9%), and Troponin (94.5% vs. 83.1%), with similar trends observed for hs-TnT (43.6% vs. 19.5%) and NT-proBNP (39.3% vs. 19.9%). Among those tested, the median number of biomarker assessments was also significantly higher in the cardiotoxicity group (all P < 0.003), particularly for CK-MB (10.0 [IQR 6.0–17.0] vs. 4.0 [IQR 2.0–9.0], P < 0.001), BNP (8.0 [IQR 4.0–15.0] vs. 3.0 [IQR 1.0–6.0], P < 0.001), hs-TnT (8.0 [IQR 4.0–16.8] vs. 4.0 [IQR 2.0–9.0], P < 0.001), Troponin (6.0 [IQR 3.0–10.0] vs. 2.0 [IQR 1.0–5.0], P < 0.001), and Myoglobin (6.5 [IQR 4.0–12.0] vs. 3.0 [IQR 1.0–6.0], P < 0.001). Drug Class Associations Multivariable Cox regression analysis was performed to evaluate the association between specific targeted therapies and the risk of cardiotoxicity, adjusting for age, gender, and baseline cardiovascular comorbidities (including hypertension, myocardial infarction, arrhythmia, heart failure, thrombosis, diabetes, and hyperlipidemia) ( Figure 2A ). Download figure Open in new tab Figure 2. Cardiovascular Risk Associated with Different Targeted Therapies Overall and by Patient Subgroups (A) shows overall hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular events associated with different targeted therapies. (B) presents subgroup analyses stratified by baseline comorbidities, age, sex, and cardiovascular disease (CVD) history. All analyses were adjusted for age, sex, baseline CVD, diabetes, hypertension, hyperlipidemia, and cumulative treatment cycles using multivariable Cox proportional hazards models. The vertical dashed line represents HR = 1.0 (no difference in risk). P for interaction tests whether treatment effects differ significantly across subgroups. Abbreviations: PD-1, programmed cell death protein 1; EGFR, epidermal growth factor receptor; VEGF, vascular endothelial growth factor; BCR-ABL, breakpoint cluster region-Abelson; ALK, anaplastic lymphoma kinase; HER2, human epidermal growth factor receptor 2; CVD, cardiovascular disease. After adjustment, PD-1 inhibitor use was associated with the highest risk of cardiotoxicity (HR = 1.81, 95% CI 1.43–2.28), followed by Other targeted therapy (HR = 1.71, 95% CI 1.24–2.36), VEGF inhibitors (HR = 1.33, 95% CI 1.06–1.67), and ALK inhibitors (HR = 1.38, 95% CI 0.85–2.22). In contrast, EGFR inhibitor use was associated with a significantly lower risk of cardiotoxicity (HR = 0.61, 95% CI 0.48–0.78). Subgroup analysis revealed heterogeneous treatment effects across different targeted therapies and patient characteristics ( Figure 2B ). For PD-1 inhibitors, increased cardiovascular risk was consistently observed across most subgroups (all P < 0.01), except in patients aged < 65 years (HR = 1.38, 95% CI 0.87-2.18, P = 0.18). A significant interaction was detected between age and PD-1 therapy ( P for interaction = 0.03), with older patients (≥ 65 years) demonstrating higher risk (HR = 2.39, 95% CI 1.81-3.16) compared to younger patients. In contrast, EGFR inhibitors consistently showed protective cardiovascular effects across all subgroups examined, with hazard ratios ranging from 0.47 to 0.61 (all P 0.05). VEGF inhibitors demonstrated increased cardiovascular risk in most subgroups, though the effect was not significant among patients with pre-existing CVD history (HR = 1.18, 95% CI 0.85-1.65, P = 0.32). For other targeted agents, a significant interaction with comorbidity burden was observed ( P for interaction < 0.01), with the highest risk noted in patients without comorbidities (HR = 2.76, 95% CI 1.81-4.19, P < 0.01). Additionally, cardiovascular history significantly modified the effect of other targeted therapies ( P for interaction = 0.02), with attenuated risk observed in patients with pre-existing CVD (HR = 1.35, 95% CI 0.78-2.36, P = 0.29) compared to those without CVD history (HR = 2.68, 95% CI 1.80-3.99, P < 0.01). Non-linear association between the number of PD-1 administrations and cardiotoxicity risk Restricted cubic spline analysis demonstrated a significant non-linear association between the number of PD-1 administrations and cardiotoxicity risk ( Figure 3 , P for nonlinearity = 0.002). Risk rose rapidly up to around 4 administrations (HR at 4 = 2.0, 95% CI = 1.50–2.65), then increased more slowly thereafter, indicating attenuation of the marginal risk at higher exposure counts. Download figure Open in new tab Figure 3. Non-linear association between PD-1 inhibitor exposure frequency and cardiotoxicity risk The hazard ratio and 95% confidence intervals are plotted on the y-axis against the cumulative number of PD-1 doses on the x-axis. A significant non-linear association was observed ( P for nonlinearity = 0.002). Abbreviations: PD-1, programmed cell death protein 1. Temporal Trajectories of Cardiac Biomarkers by PD-1 Exposure Figure 4 shows the modeled longitudinal trajectories of four cardiac biomarkers according to PD-1 inhibitor exposure over a follow-up of up to 2500 days after treatment initiation. All generalized additive models converged with satisfactory fit. Download figure Open in new tab Figure 4. Temporal trajectories of cardiac biomarkers by PD-1 inhibitor exposure. Predicted longitudinal trajectories over 2500 days after treatment initiation are shown for BNP, CK-MB, myoglobin, and troponin. PD-1–exposed patients exhibited marked late-phase increases in BNP and myoglobin, moderate elevations in CK-MB, and minimal changes in troponin. Divergence between exposure groups emerged after approximately 1500 days, indicating potential cumulative cardiotoxic effects associated with prolonged PD-1 inhibitor therapy. Shaded areas represent 95% CI. Abbreviations: BNP: B-type natriuretic peptide; CI: Confidence Interval; CK-MB: creatine kinase–myocardial band; PD-1: programmed cell death protein 1. For BNP, PD-1–exposed patients displayed a distinct late-phase rise, with both groups remaining stable around 50 pg/mL during the first 1500 days, after which the exposed group showed an exponential increase exceeding 200 pg/mL by day 2500, whereas the unexposed group maintained levels around 50–100 pg/mL. CK-MB levels increased gradually in both groups, with a steeper slope in the PD-1–exposed group emerging by 1000 days and becoming more pronounced after 1500 days, reaching 4 ng/mL by day 2500 versus 3.5 ng/mL in the unexposed group. Myoglobin followed a similar pattern to BNP, remaining stable until about 1500 days, then rising sharply in the exposed group to over 75 ng/mL by day 2500, compared with decreases to around 25 ng/mL in the unexposed group. In contrast, troponin trajectories were largely comparable between groups, showing only minor fluctuations (1.05 to 1.10 ng/mL in exposed; 1.10 to 1.00 ng/mL in unexposed) with overlapping confidence intervals throughout the follow-up. Overall, BNP and myoglobin exhibited the most pronounced delayed elevations in the PD-1–exposed cohort, suggesting potential cumulative cardiotoxic effects associated with prolonged PD-1 inhibitor exposure, whereas CK-MB showed moderate sensitivity and troponin remained largely stable. Comparison of temporal trajectories between PD-1 – treated and untreated groups Temporal trajectories of cardiac biomarkers were compared between PD-1–exposed and unexposed patients using generalized additive mixed models including smooth time-by-exposure interaction terms ( Table 2 ). Model comparison by likelihood ratio tests revealed significant heterogeneity in temporal patterns for two biomarkers ( Table 2 ). BNP showed the strongest exposure-related divergence (ΔAIC = 10.36, P = 0.002; R 2 (interaction) = 0.131 vs R 2 (main) = 0.128; 6,934 observations from 1,399 patients), followed by CK-MB (ΔAIC = 5.04, P = 0.019; R 2 (interaction) = 0.042 vs R 2 (main) = 0.041; 9,799 observations from 1,574 patients). In contrast, Myoglobin and TnI showed comparable temporal trajectories regardless of PD-1 exposure after adjustment for covariates. View this table: View inline View popup Download powerpoint Table 2. Model comparison results assessing the association between PD-1 inhibitor exposure and longitudinal trajectories of four cardiac biomarkers. Δ AIC represents the reduction in Akaike Information Criterion when including the PD-1 × time interaction term, indicating model improvement. R² (interaction) and R² (main) denote the proportion of variance explained by the interaction and main-effect models, respectively. No. of observations indicates total biomarker measurements; No. of patients with cardiotoxicity refers to individuals who developed cardiotoxicity and contributed at least one biomarker record. Significance levels: P < 0.05 (*), P < 0.01 (**), ns = not significant. The marginal increase in R 2 values between the interaction and main-effect models indicates that incorporating time-by-exposure interactions modestly improved model fit, particularly for BNP, reflecting a distinct exposure-dependent evolution over time. Baseline characteristics of targeted therapy–related cAEs in FAERS A total of 4,249 adverse event reports involving targeted therapies and cardiac adverse events were identified in patients with lung cancer. Table 3 summarizes the demographic and reporting characteristics across drug classes. Median age varied across therapies, ranging from 64 years in ALK and VEGF inhibitor reports to 71 years among EGFR inhibitor reports. Sex distribution differed substantially by target: EGFR, HER2, and ALK inhibitors were associated with a predominance of female reports, whereas PD-1 and PD-L1 inhibitors were predominantly reported in males. Time to onset of cAEs also varied, with the longest delays observed for VEGF (median 60 days) and EGFR inhibitors (median 41 days), and the shortest for HER2 inhibitors (median 15 days). Reporting-year distributions demonstrated clear temporal adoption patterns across drug classes. PD-L1 inhibitors showed no reports before 2016 and demonstrated rapid growth from 2019 onward, with annual counts increasing from 77 in 2019 to 93 in 2020, followed by 82 in 2021, 99 in 2022, and 128 in 2023. In contrast, EGFR-related cAEs were reported continuously from 2004 through 2023, beginning with 47–37–32 reports in 2004–2006 and remaining consistently represented in later years (e.g., 104 in 2017, 115 in 2019, 105 in 2020). VEGF inhibitors likewise exhibited steady reporting throughout the entire period, with early contributions in 2004–2007 (2–13 reports) and persistent signals in later years (e.g., 21 in 2017, 25 in 2022, 29 in 2023). View this table: View inline View popup Download powerpoint Table 3. Characteristics of cardiac adverse event reports associated with targeted therapies in lung cancer (FAERS database, 2004–2023) Continuous variables are summarized as median [interquartile range], and categorical variables are expressed as counts with corresponding percentages (n [%]). Abbreviations: AER, Adverse Event Report; cAE, Cardiac Adverse Event; FAERS, FDA Adverse Event Reporting System; PRR, Proportional Reporting Ratio; EGFR, Epidermal Growth Factor Receptor; HER2, Human Epidermal Growth Factor Receptor 2; ALK, Anaplastic Lymphoma Kinase; VEGF, Vascular Endothelial Growth Factor; PD-1, Programmed Cell Death Protein 1; PD-L1, Programmed Death-Ligand 1. Targeted data processing and construction of the final analytical cohort From a total of 16,800,135 reports in the FAERS database (2004Q1–2023Q4), 560,088 entries involved targeted therapies after excluding all non–targeted agents. Following removal of reports without lung cancer as the primary indication, 63,908 lung-cancer–related targeted-therapy reports were retained. After deduplication based on demographics, indication, suspected drug, event date, and reaction, and after excluding secondary suspect drugs and patients aged ≤ 18 years, 311,491 unique reports were retained. Subsequently, 59,629 reports without any cardiac adverse events (cAEs) were excluded. Among the 4,279 reports containing cAEs, an additional 30 cases with cardiac tumor–related indications (see Methods for detailed indication definitions) were removed. The final analytical cohort therefore consisted of 4,249 targeted-therapy–related cAEs in patients with lung cancer meeting all predefined inclusion criteria ( Figure 5 ). Download figure Open in new tab Figure 5. Flowchart of study analysis for cardiac adverse events in targeted drug therapy using FAERS database. This flowchart outlines the study’s analytical process in assessing cardiac adverse events (cAEs) related to targeted drug therapies using the FAERS database (2004Q1-2023Q4). The analysis includes three steps: ( 1 ) comparison of cAE incidence across different targeted therapy classes, ( 2 ) assessment of the PRR for severe cAEs (PRR ≥ 3) across targeted drug classes, and ( 3 ) PRR heatmap visualization for severe cAEs (PRR ≥ 3) associated with specific targeted therapies in lung cancer patients. The final cohort includes 4,249 reports of severe cAEs after data cleaning, exclusion of non-lung cancer cases, and deduplication. Abbreviations: AER, Adverse Event Report; cAE, Cardiac Adverse Event; FAERS, FDA Adverse Event Reporting System; PRR, Proportional Reporting Ratio. In the final analytical cohort, the incidence of cAEs differed markedly across targeted therapy classes, as the primary analysis. In secondary analyses, we characterized the proportional reporting ratios (PRRs) for severe cAEs (PRR ≥ 3) across targeted therapy classes and visualized drug-specific PRR patterns using a heatmap. Cardiovascular safety profile of different drug targets in lung cancer treatment in FAERS database Overall cAEs frequency by drug target The frequency of cAEs differed substantially across therapeutic targets ( Figure 6A ). PD-1 inhibitors exhibited the highest incidence of cAEs, with 8.09% of PD-1–treated patients affected. This was followed by VEGF inhibitors (7.93%) and ALK inhibitors (6.98%). EGFR inhibitors showed a cAE incidence of 5.71%, PD-L1 inhibitors 6.34%, and HER2 inhibitors had the lowest incidence at 4.23%. Download figure Open in new tab Figure 6. Cardiovascular safety profile of targeted therapies and immune checkpoint inhibitors in lung cancer patients: FAERS database analysis (A) The line graph depicts the frequency distribution of cardiovascular adverse events (cAEs) across six drug target categories (HER2, EGFR, PD-1/L1, ALK, VEGF, and PD-1), with the percentage increase indicated above each data point. Cardiovascular event incidence rates (red line) and absolute event counts (gray bars) stratified by drug target in lung cancer patients. n represents the total number of AERs per drug target. (B) Forest plot displaying the proportional reporting ratio (PRR) with 95% confidence intervals (CI) for severe cAEs associated with each drug target. The vertical dashed line represents PRR = 1.0. (C) Heatmap illustrating the PRR values for specific severe cardiac adverse events (with ≥3 reported cases) stratified by drug target and disease indication. Color intensity represents the magnitude of PRR values (scale: 0-70), with darker blue indicating stronger disproportionality signals. NA indicates insufficient data (fewer than 3 cases) for PRR calculation. Abbreviations: ALK, Anaplastic lymphoma kinase; cAE: Cardiovascular adverse event; CI, Confidence interval; EGFR, Epidermal growth factor receptor; FAERS, FDA Adverse Event Reporting System; HER2, Human epidermal growth factor receptor 2; PD-1, Programmed cell death protein 1; PD-L1, Programmed death-ligand 1; PRR: Proportional reporting ratio; VEGF, Vascular endothelial growth factor. Disproportionality analysis of severe cAEs Forest plot analysis demonstrated significant disproportionality signals for several drug targets ( Figure 6B ). PD-1 inhibitors exhibited the strongest association with severe cAEs, with a PRR of 1.84 (95% CI: 1.68-2.02, P < 0.01). PD-L1 inhibitors also showed a significant signal with a PRR of 1.40 (95% CI: 1.23-1.58, P < 0.01). EGFR inhibitors were associated with a reduced cAE reporting signal (PRR 0.42; 95% CI 0.38–0.47; P < 0.01), while HER2 inhibitors exhibited an even lower signal strength (PRR 0.23; 95% CI 0.16–0.33; P < 0.01). ALK inhibitors showed a modest increased association with a PRR of 1.13 (95% CI: 1.01-1.27, P = 0.03), while VEGF inhibitors demonstrated no significant disproportionality with a PRR of 0.88 (95% CI: 0.72-1.08, P = 0.21). Specific cAEs pattern across drug targets The heatmap analysis revealed distinct patterns of specific cAEs associated with different drug targets ( Figure 6C ). Immune-mediated myocarditis showed the strongest disproportionality signal with PD-1 inhibitors (PRR = 28.41) and PD-L1 inhibitors (PRR = 11.42), highlighting the unique cardiotoxicity profile of immune checkpoint inhibitors. Autoimmune myocarditis exhibited an even higher disproportionality, with a PRR of 42.23 for PD-1 inhibitors and 23.57 for PD-L1 inhibitors. In addition, immune-mediated pericarditis demonstrated an exceptionally strong signal with PD-1 inhibitors (PRR = 75.53), further supporting the heightened susceptibility of immune-related cardiac complications under checkpoint blockade. Pericardial effusion was significantly associated with ALK inhibitors (PRR = 10.14), consistent with known class effects of this therapeutic category. HER2 inhibitors demonstrated elevated signals for cardiac failure (PRR = 2.27) and cardiac tamponade (PRR = 6.46). VEGF inhibitors showed notable associations with acute coronary syndrome (PRR = 6.09) and supraventricular tachycardia (PRR = 3.44). Autoimmune myocarditis was predominantly associated with PD-1 inhibitors (PRR = 23.57). Discussion Our study, leveraging a large real-world cohort of 2,427 lung cancer patients exposed to 82 targeted agents, provides one of the most comprehensive assessments to date of cardiotoxicity profiles across major therapeutic classes. Approximately 13% of patients experienced biomarker-defined cardiotoxicity. Among therapeutic classes, PD-1 inhibitors conferred the highest cardiotoxic risk, whereas EGFR inhibitors were associated with a comparatively lower incidence of cardiac events. The results further suggest that BNP and CK-MB, rather than TnI alone, may serve as sensitive early indicators of immune-related myocardial stress during PD-1 therapy. While previous studies have highlighted PD-1–related myocarditis ( 17 ), our data reveal a broader spectrum of subclinical myocardial stress, captured notably by BNP and CK-MB rather than troponin alone. Consistently, analyses of 4,249 targeted-therapy–related cardiac adverse event reports in the FAERS database showed that PD-1 inhibitors exhibited the strongest disproportionality signals for severe cardiotoxicity, whereas EGFR inhibitors demonstrated the lowest signal strength, reinforcing the class-specific risk patterns observed in our real-world cohort. Clinically, PD-1–based immune checkpoint inhibitors are among the agents most frequently implicated in immune-mediated myocarditis and are associated with substantial morbidity and mortality despite their low absolute incidence ( 18 ). Immune checkpoint blockade can trigger systemic immune activation that extends to cardiac tissues ( 19 ). PD-1 signaling is essential for restraining autoreactive T-cell responses and preserving myocardial immune tolerance; both cardiomyocytes and cardiac-resident immune cells rely on PD-1/PD-L1 interactions to limit cytotoxic activity ( 19 ). Inhibition of this pathway removes a key peripheral tolerance mechanism, enabling T-cell infiltration, proinflammatory cytokine release, and direct myocyte injury ( 20 ). Experimental models further support this biology: PD-1–deficient mice spontaneously develop autoimmune myocarditis, underscoring its nonredundant role in cardiac homeostasis ( 21 ). However, across the broad spectrum of targeted therapies, comparative risk estimates remain limited because most available evidence derives from case series or selected clinical-trial populations ( 22 ). Large real-world analyses are therefore needed to contextualize cardiotoxicity profiles across different therapeutic classes. Our study addresses this gap by systematically comparing cardiotoxicity across major targeted therapy categories and demonstrating that PD-1 inhibitors confer the highest overall cardiotoxic burden. Notably, the cardiotoxicity we identified is not restricted to clinically overt myocarditis; instead, it predominantly reflects a broader spectrum of subclinical or evolving myocardial stress captured by BNP and CK-MB—phenotypes that prior work has largely overlooked due to its narrow focus on fulminant myocarditis. Although prior studies indicate that clinically overt ICI-associated myocarditis most commonly presents within the first 1–2 treatment cycles ( 23 ), our restricted cubic spline analysis revealed a nonlinear pattern with a steep rise in biomarker-defined cardiotoxicity up to approximately four administrations. This apparent shift likely reflects differences in case definition and patient populations. Unlike earlier reports focused on clinically adjudicated myocarditis—typically characterized by abrupt, early presentations ( 23 )—our real-world cohort captures a broader spectrum of myocardial injury, including subclinical or attenuated phenotypes that may require multiple cycles of exposure before surpassing biomarker thresholds. The more heterogeneous clinical characteristics of routine oncology populations, including older age, multimorbidity, and polypharmacy, may also contribute to the delayed inflection in risk ( 24 ). Therefore, the cycle-dependent pattern observed in our study should not be interpreted as contradicting prior evidence but rather as complementary, highlighting that early-onset toxicity dominates overt myocarditis, whereas cumulative or subclinical injury may manifest over several administrations. This pattern is consistent with an early phase of immune activation reported in mechanistic studies, in which PD-1 blockade enables rapid T-cell activation and clonal expansion ( 25 ); however, direct evidence linking cycle number to clonal kinetics is limited, and the observed inflection point more likely reflects cumulative subclinical injury in real-world settings. The longitudinal biomarker trajectories further clarify the temporal heterogeneity of cardiac injury. BNP and CK-MB showed significant exposure-related divergence, whereas TnI remained largely stable over time. These differences likely reflect distinct biological pathways and timing of myocardial stress. BNP, secreted by ventricular myocytes in response to wall stretch, is an early and sensitive indicator of hemodynamic overload or diastolic dysfunction, often preceding structural injury ( 26 ). CK-MB, a cytosolic enzyme released during myocyte membrane permeability changes, represents a transitional stage of subclinical injury ( 27 ). In contrast, TnI elevation signifies frank myocyte necrosis and tends to appear later and transiently during acute myocarditis episodes ( 28 ). Although clinically overt ICI-associated myocarditis is characterized by acute necrotic injury and marked troponin release, such cases represent only a small and highly selected subset of the overall cardiotoxicity spectrum captured in published case series ( 29 ). In routine practice—particularly when systematic biomarker surveillance is applied—many PD-1–related cardiac abnormalities manifest instead as mild or asymptomatic elevations in markers of myocardial stress or membrane permeability rather than fulminant necrosis ( 30 ). Therefore, in a real-world population enriched for low-grade or evolving myocardial stress rather than catastrophic necrotic injury, sustained BNP and CK-MB changes with minimal troponin release are pathophysiologically consistent. Consequently, integrating multiple biomarkers with complementary temporal sensitivity may improve early detection of PD-1-related cardiac effects ( 13 ). Limitations Several limitations should be acknowledged. As a single-center retrospective analysis, residual confounding cannot be fully excluded ( 31 ). Cardiotoxicity was defined biochemically rather than by imaging, potentially overestimating mild or transient injury; however, biomarker-based definitions improve sensitivity for early stress detection and align with current cardio-oncology recommendations. Surveillance intensity differed between groups, which may introduce detection bias ( 32 ). Conclusions PD-1 inhibitors showed the strongest and most consistent cardiotoxicity signal across both the Renji real-world cohort and the FAERS database. The cardiotoxic risk of PD-1 therapy peaks around four treatment cycles. BNP and CK-MB trajectories capture early subclinical myocardial stress, whereas troponin remains stable, reflecting distinct temporal phases of cardiac injury. Data Availability Statement All data produced in the present study are available upon reasonable request to the authors. Funding All authors received support from the National Natural Science Foundation of China (Nos. U21A20341 and 82470394); the Shanghai Municipality Science and Technology Commission (Nos. 25Y12800500, 24DZ2202700, and 20Y11910500); the Shanghai Municipal Health Commission (No. 202440156); the Shanghai Academic/Technology Leader Program (No. 21XD1432100); the Basic–Clinical Collaborative Innovation Project from the Shanghai Immune Therapy Institute; and the Basic–Clinical Collaborative Innovation Project from the Shanghai Key Laboratory of Computational Chemistry and Nanomedicine (No. 2025ZYB-007). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Consent for publication Not applicable. Conflict of Interest Statement No potential conflicts of interest were disclosed by the authors. Clinical trial number Not applicable. Footnotes The authors declare no potential conflicts of interest. To complement the single-center real-world cohort at Renji Hospital, we incorporated an independent post-marketing pharmacovigilance analysis using the FDA Adverse Event Reporting System (FAERS). FAERS is a large spontaneous reporting database that captures adverse event reports (AERs) submitted by healthcare professionals, consumers, and manufacturers. Each FAERS entry represents an AER that may include one or more adverse events related to a specific drug exposure. For this study, we queried FAERS from 2004Q1 to 2023Q4, identifying 16.8 million reports. After excluding non-targeted therapies, performing FDA-recommended deduplication, restricting to adult patients, removing secondary suspect drugs, and limiting indications to lung cancer, a final cohort of 4,249 targeted-therapy-related cardiac adverse event (cAE) reports was obtained. This analytical dataset enabled systematic evaluation of post-marketing cardiotoxicity signals across major targeted therapy classes. We quantified cAE incidence by drug target and performed disproportionality analysis using proportional reporting ratios (PRRs), focusing on 25 clinically significant severe cAEs. Severe events were included only when reported at least three times and when the lower bound of the 95% confidence interval of the PRR exceeded 1.0, ensuring statistical reliability. Heatmap visualizations were generated to identify target-specific cardiotoxicity patterns. FAERS results revealed that PD-1 inhibitors had the highest cAE frequency (8.09%) and demonstrated the strongest disproportionality signals for severe cardiac events, including immune-mediated myocarditis (PRR 28.41), autoimmune myocarditis (PRR 42.23), and immune-mediated pericarditis (PRR 75.53). PD-L1 inhibitors showed similarly elevated immune-related cardiac signals. In contrast, EGFR inhibitors exhibited the lowest overall cardiotoxicity signal (PRR 0.42). 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Share Cardiotoxicity Associated with Targeted Therapies in Lung Cancer: A Retrospective Cohort Study of 2,427 Patients at Renji Hospital Zhixuan Zhang medRxiv 2025.11.13.25340140; doi: https://doi.org/10.1101/2025.11.13.25340140 Share This Article: Copy Citation Tools Cardiotoxicity Associated with Targeted Therapies in Lung Cancer: A Retrospective Cohort Study of 2,427 Patients at Renji Hospital Zhixuan Zhang medRxiv 2025.11.13.25340140; doi: https://doi.org/10.1101/2025.11.13.25340140 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4440) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1510) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6605) Geriatric Medicine (668) Health Economics (998) Health Informatics (4541) Health Policy (1369) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1265) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6604) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1145) Occupational and Environmental Health (957) Oncology (3334) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (711) Psychiatry and Clinical Psychology (5448) Public and Global Health (9235) Radiology and Imaging (2199) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (594) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0170367eadcfff4',t:'MTc3OTczODA0MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();