Cumulative exposure to tacrolimus is associated with increased risk of malignancy for solid organ transplant recipients

Abstract

Solid organ transplant recipients (SOTR) face elevated cancer risk due to prolonged immunosuppression. While higher tacrolimus exposure has been linked to de novo malignancies, the dose–response relationship remains unclear, prompting the need for population‐level, longitudinal investigations. To quantify the exposure‐response relationship between tacrolimus trough level and post‐transplant malignancy risk, we used a population‐based cohort study using linked administrative healthcare data from Ontario, Canada. All transplant recipients from January 1, 2008 to March 1, 2020 with ≥2 serum tacrolimus levels in the first year post‐transplant were included. Cumulative exposure to tacrolimus was treated as a (1) continuous variable, (2) by quartiles of exposure within the first year and (3) as time‐varying exposure beyond the first year; incidence of de novo malignancy was investigated using cause‐specific and subdistribution Cox regression models. Among the 5178 SOTRs, a total of 318 de novo malignancies (6.1%) and 332 deaths (6.4%) occurred. For every 20% increase in the cumulative tacrolimus trough level in the first year, a heightened risk of malignancy was observed (HR = 1.09, 95% CI: 1.02–1.17). For each 20% increase in cumulative tacrolimus trough level after 1 year, the risk of malignancy increased (HR = 1.08 [95% CI: 1.01–1.15]). Patients in the highest quartile of cumulative exposure (median 9–10 ng/mL) had a 47% greater risk of malignancy compared to those in the lowest quartile (median 5–6 ng/mL; HR = 1.47 [95% CI: 1.03–2.11]). These findings highlight the importance of carefully titrating tacrolimus and avoiding unnecessary prolonged high exposure, particularly during the critical first year post‐transplant.

What's new?

Long‐term immunosuppression in solid organ transplant recipients (SOTRs) is linked to increased cancer risk. Although this risk potentially increases with greater immunosuppressant exposure, the exact dose‐response pattern remains uncertain. Here, the authors used linked administrative healthcare databases in Canada to investigate associations between trough levels of the immunosuppressant tacrolimus and new cancer formation in SOTRs. In the first year of use, each 20 percent increase in cumulative tacrolimus trough level was associated with an elevation in cancer risk. This linear association was present across organ types. The findings support routine monitoring of tacrolimus exposure to reduce cancer risk in SOTRs.

Abbreviations

AUC

area under the curve

CI

confidence interval

CKD‐EPI

Chronic Kidney Disease Epidemiology Collaboration

CNI

Calcineurin inhibitor

CORR

Canadian organ replacement register

HR

hazard ratio

ICES

Institute for Clinical Evaluative Sciences

IQR

interquartile range

mTOR

mammalian target of rapamycin

NNH

number needed to harm

OCR

Ontario cancer registry

ODB

Ontario drug benefit

OLIS

Ontario Laboratories Information System

PTLD

post‐transplant lymphoproliferative disorder

RECORD

reporting of studies conducted using observational routinely collected health data

RPDB

registered persons database

SAS

statistical analysis system

SD

standard deviation

SOTR

solid organ transplant recipient

1. INTRODUCTION

Solid organ transplantation is lifesaving for patients with end‐stage organ disease. While short‐term solid organ transplant recipients (SOTRs) survival has improved, long‐term gains remain limited. ¹ , ² , ³ This disparity is primarily attributed to an increased risk of cancer, cardiometabolic complications, and infectious diseases. ⁴ Among these, cancer is the second‐leading cause of death in SOTRs, with mortality rates exceeding 2.8 times those of the general population. ⁵ , ⁶ , ⁷ , ⁸ Moreover, cancer incidence in SOTRs is projected to rise in the coming decade, driven by an aging recipient population and prolonged exposure to immunosuppression as survival improves. ⁶ , ⁹

The increase in post‐transplant cancer incidence is multifactorial, but immunosuppression plays a central role. ¹⁰ Induction immunosuppression, particularly with lymphocyte‐depleting agents, has been primarily associated with early‐onset post‐transplant lymphoproliferative disorder (PTLD). ¹⁰ However, maintenance immunosuppression, especially with calcineurin inhibitors (CNIs), appears to be the main driver of long‐term cancer risk. ¹⁰ CNIs remain the cornerstone of maintenance immunosuppressive regimens across all solid organ types. Higher trough levels of CNIs such as cyclosporine and tacrolimus have been associated with increased cancer incidence, suggesting a dose–response relationship. ¹¹ , ¹² , ¹³ However, no clear threshold or linear association has been established, making it difficult for clinicians to determine safe immunosuppressive exposure limits that balance rejection risk with oncologic safety.

Linked administrative healthcare data and national registries offer a unique opportunity to study the impact of cumulative CNI exposure on cancer incidence across different types of SOTRs. These datasets provide comprehensive longitudinal outcomes, population‐wide sample sizes, and aggregated patient information from multiple clinical encounters. By including multiple solid organ transplant groups, we can better assess differences in cumulative tacrolimus exposure, as varying organ types require different levels and intensities of immunosuppression. In this study, we conducted a population‐based cohort analysis using linked administrative healthcare databases to examine the association between cumulative tacrolimus trough levels and the incidence of post‐transplant de novo malignancies in SOTRs. Our findings may inform tailored immunosuppression strategies.

2. METHODS

2.1. Design and setting

We conducted a population‐based cohort study using linked administrative healthcare databases held at the Institute for Clinical Evaluative Sciences (ICES; ices.on.ca/) in Ontario, Canada, on first‐time SOTRs from January 1, 2008 to March 1, 2020, inclusive of kidney, liver, heart, lung, pancreas and multi‐organ transplant patients. These datasets were linked using unique encoded identifiers and analyzed at ICES. Reporting of this study followed the Reporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement (Table S1).

2.2. Data sources

Several linked administrative databases were used for this analysis. SOTRs were identified via the Canadian Organ Replacement Register (CORR), a national registry that contains information on approximately 98% of all Canadian SOTRs since 1981. We linked death and demographic information from the Registered Persons Database (RPDB). To determine tacrolimus trough levels, we used the Ontario Laboratories Information System (OLIS), an information repository that contains laboratory test orders and results from hospitals, and community and public health labs in Ontario. Because healthcare coverage in Ontario is universal and both public and private laboratories report results to OLIS, this database provides comprehensive capture of outpatient laboratory data across the province. To ensure a higher level of ascertainment, we restricted tacrolimus level measurements to outpatient settings, which achieved full provincial coverage earlier. ¹⁴ Type of immunosuppressant medication used was obtained from the Ontario Drug Benefit (ODB) database, which provides information on medical drug claims submitted to this program by those over 65 years of age or those who have special drug coverage, a benefit available to all SOTRs. Additionally, comorbidity data were obtained from the Ontario Health Insurance Plan database (physician diagnostic and billing codes), Canadian Institute for Health Information Discharge Abstract Database (inpatient diagnostic and procedural codes), and the National Ambulatory Care Reporting System (emergency room visits). Neighbourhood income quintile was obtained from Canadian census data, using values closest to the index transplant date, then linked to postal forward sortation areas, and categorized into community‐specific quintiles by census region.

We used the Ontario Cancer Registry (OCR) to capture diagnoses of de novo malignancies. This registry contains information on all incident cancers (other than non‐melanoma skin cancer) in Ontario since 1964 and has been estimated to be over 95% complete. ¹⁵ The OCR contains only the first incident cancer for a cancer site. Because the OCR does not capture tumor recurrence, all malignancies identified in this study represent post‐transplant de novo cancers; patients transplanted for hepatocellular carcinoma or other malignancies were excluded to avoid confounding from recurrence risk and differences in immunosuppression intensity.

2.3. Population and study size

All adults who underwent solid organ transplant with at least two outpatient tacrolimus trough levels within the first year post‐transplant were included. We excluded recipients who died in the first year post‐transplant, paediatric patients (<18 years), recipients whose maintenance immunosuppressant was not tacrolimus (e.g., cyclosporine) within the first year post‐transplant, previous SOTRs, those with graft failure within the first year post‐transplant (defined as return to chronic dialysis in kidney transplant recipients and re‐transplant for all organ types), or patients who had cancer within the first year post‐transplant, as these were deemed likely to be pre‐existing or donor‐derived cancers (Figure S1). ¹⁶ , ¹⁷

2.4. Variables and measurement

2.4.1. Exposure

Our primary exposure of interest, cumulative tacrolimus exposure within the first‐year post‐transplant, was defined using Logical Observation Identifiers Names and Codes for serum/plasma tacrolimus trough lab measurements in OLIS (Table S2). We used all tacrolimus trough measurements (serum/plasma values in ng/mL) in the first‐year post‐transplant to calculate an area under the curve (AUC), which is used to define the cumulative tacrolimus exposure (ng*day/mL) at one‐year post‐transplant. The AUC was derived using a modified Wagner–Nelson approach, in which consecutive trough concentrations were connected by linear segments, and the area between each pair of measurements was calculated as a trapezoid (with the initial segment approximated as a triangle). The sum of all segmental areas represented the total exposure. This time‐weighted method, previously applied in pharmacokinetic studies of tacrolimus after liver transplantation, accounts for both concentration and timing of trough measurements while accommodating variable sampling frequencies. ¹⁸ The median (IQR) number of tacrolimus trough levels in the first year was 14 (8–26), mean 18.5 (SD 13.6). Exposure to other immunosuppressants (induction or maintenance) was not included in the analysis. Because pre‐transplant tacrolimus exposure is uncommon (largely limited to ABO‐incompatible living‐donor kidney protocols and select second line autoimmune indications) and we excluded prior transplant recipients, any misclassification from pre‐transplant use is expected to be minimal and unlikely to materially affect our estimates.

The index date (start of study follow‐up) was at 1 year following transplant. Some individuals had their most recent tacrolimus trough a considerable time before their index date (e.g., more than 180 days prior to the index date). For these individuals, we used their most recent measure to calculate the cumulative exposure. We only included outpatient tacrolimus levels and excluded values measured during a hospitalization or emergency department visit given that tacrolimus levels can fluctuate drastically during periods of acute illness. ¹⁹

2.4.2. Baseline

The cohort was described by sociodemographic characteristics (age, sex), socioeconomic (income, drug coverage eligibility), geography (rural vs. urban), preexisting comorbidities, tacrolimus therapy received (mono vs. multiple therapy, cumulative exposure), prior healthcare utilization (hospitalizations, ER visit, ICU admission), and transplant information (transplant organ type, donor type, donor age). Preexisting comorbidities were further described by the Charlson Comorbidity Index, a tool designed to measure long‐term mortality risk and burden of disease. ²⁰

2.4.3. Outcomes

Our outcome of interest was incidence of any post‐transplant de novo malignancy. Subgroup analyses including solid and haematological malignancies were defined using relevant OCR codes (Table S2). Outcome assessment started at the index date and continued until the date of death, graft failure (i.e., return to maintenance dialysis [kidney only], or re‐transplantation), change in immunosuppression strategy to non‐tacrolimus‐based regimen (any evidence of cyclosporine serum/plasma lab measurement in OLIS) or end of follow‐up (March 31, 2021).

2.5. Statistical analysis

Descriptive analyses of baseline variables were performed. Counts (proportions) were provided for categorical variables and mean ± SD or median (interquartile range [IQR]) were generated for continuous variables. We determined the cumulative incidence and incidence rates (events per 1000 person‐years) of cancer throughout follow‐up as well as 0–1, 1–3, 3–5, 5–10, and >10 years intervals (index date as one‐year post‐transplant). This population‐based study quantifies cumulative tacrolimus exposure using AUC from serial trough levels and shows a linear, time‐varying association with post‐transplant de novo malignancy across organ types. Findings support routine monitoring and minimizing unnecessary tacrolimus exposure to reduce cancer risk while informing risk‐stratified dosing and surveillance.

The first‐year cumulative tacrolimus trough level was assessed both as a continuous variable (log‐transformed, quantifying the association per 20% increase in cumulative tacrolimus trough level) and as a categorical variable (by quartiles of cumulative tacrolimus trough level). To allow for tacrolimus trough levels to change beyond the first‐year post‐transplant, we updated cumulative tacrolimus AUC of trough concentrations at intervals of 3 months (90 days).

Additionally, median (IQR) was reported for cumulative tacrolimus levels (ng*day/mL) and trough concentrations (ng/mL) at 3, 6, and 12 months. Analyses were carried out for all SOTRs combined and separately by transplanted organ. We also calculated the number/proportion of events and incidence rate for cancer and death (expressed as event rate per 1000 patient‐years) based on the one‐year post‐transplant landmark.

For both continuous and categorical exposure, we used cause‐specific Cox regression models to assess the association (hazard ratios [HR] and 95% confidence intervals [CI]) between cumulative tacrolimus exposure and cancer incidence. No significant violation of proportional hazards was noted. To examine the effect beyond one year post‐transplant, time‐varying Cox models were used to examine the association between cumulative tacrolimus trough level as a continuous time‐varying exposure and the risk of cancer in the subsequent 90‐day interval. Prior variation of trough level within the first year post‐transplant was accounted for as a baseline variable. All models were adjusted for pre‐selected confounders at baseline, including age (per 5‐year increase), sex (male vs. female), cancer prior to transplant, diabetes, Charlson Comorbidity Index, transplanted organ, neighbourhood income quintile, transplant era (2008 to 2011 vs. 2012 to 2015 vs. 2016 to 2020), estimated glomerular filtration rate (per 10 mL/min/1.73 m², 2021 Race‐Free CKD‐EPI ²¹ ), standard deviation of tacrolimus trough level over the first 12 months post‐transplant (continuous).

To assess the clinical significance of the observed hazard ratios, we calculated the Number Needed to Harm (NNH) for malignancy risk among solid organ transplant recipients (SOTRs) exposed to varying levels of cumulative tacrolimus trough concentrations. NNH was calculated using a baseline incidence of malignancy of 14.25 per 1000 person‐years based on observed cancer incidence in the first year post‐transplant. We performed the NNH calculations for three key scenarios: (1) A 20% increase in cumulative tacrolimus trough level during the first year post‐transplant; (2) Patients in the highest quartile of cumulative tacrolimus exposure compared to the lowest quartile within the first year; and (3) A 20% increase in cumulative tacrolimus trough level beyond the first year.

Visual assessment of potential non‐linear effects of the log‐transformed continuous tacrolimus exposure on cancer using Martingale residual plots and restricted cubic splines (log hazard vs. log‐cumulative tacrolimus exposure) did not indicate a significant departure from linearity. For categorical tacrolimus cumulative exposure, we used the lowest quartile of cumulative exposure as the reference group. Missing baseline characteristics were handled using missing categories or imputation using the middle value. Two‐sided tests were used and p < 0.05 was considered statistically significant. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC).

3. RESULTS

3.1. Baseline characteristics

We included 5178 first time solid organ transplant recipients with a median post‐transplant follow‐up of 3.02 years and 18,919.31 person‐years. There were 3418 kidney transplant recipients (66.0%), 1117 (21.6%) liver transplant recipients, 296 (5.7%) heart transplant recipients, 270 (5.2%) simultaneous kidney pancreas transplant recipients, and 77 (0.7%) lung transplant recipients. On average, the patients were 53.52 years old (mean ± SD: 53.52 ± 13.25), 36.3% were females, 90.2% had an urban residence, and there were no substantial differences in neighbourhood income indices. Comorbidities were common amongst transplant recipients: 44.2% of patients had moderate to severe comorbidities (Charlson Comorbidity Index >4), 16% had history of cancer, 49.7% had diabetes, 53.8% had coronary artery disease and 83.4% had hypertension prior to transplant (Table S3).

3.2. Tacrolimus exposure

By quartile, the median (IQR) of cumulative tacrolimus exposure at 1 year was 1972 (1720.6–2172.5) ng*day/ml for the lowest quartile, 2467.2 (2354.2–2580.5) ng*day/ml for the second quartile, 2990.7 (2827.6–3149.7) ng*day/ml for the third quartile, and 3927.9 (3590.8–4510.8) ng*day/ml for the highest quartile (Table S3). Median (IQR) trough concentration decreased over the first year, from 7.6 (5.9–9.9) ng/ml at 3 months, 7.4 (5.8–9.5) ng/ml at 6 months, and 7.2 (5.6–9.2) ng/ml at 12 months (Figure 1). Median (IQR) cumulative tacrolimus exposure was 688.2 (554.2–834.9) ng*day/ml at 3 months, 1385.6 (1134.5–1684.4) ng*day/ml at 6 months, and 2704.8 (2242.7–3347.3) ng*day/ml at 12 months. Tacrolimus levels in the first‐year post‐transplant varied by organ transplant (Figure 1), reflecting known clinical variation in target troughs across transplant types. These quartiles reflect a broad distribution of cumulative tacrolimus exposure, with median levels ranging from 5 to 6 ng/mL in the lowest quartile to 9–10 ng/mL in the highest.

FIGURE 1.

Tacrolimus trough concentrations (A) and cumulative exposure (B) in the first year post transplant, by transplanted organ.

3.3. Outcome

Overall, 318 SOTRs (6.14%) developed cancer during follow‐up, 332 (6.41%) died, 223 (4.31%) developed graft failure, and 151 (2.92%) were censored when they transitioned to cyclosporine. The incidence rate of cancer was 16.81 per 1000 person‐years (95% CI: 15.06–18.76). Based on 1‐year landmark analysis with index date as 1‐year post transplant, the incidence rates for follow‐up periods (index date as 1‐year post‐transplant) 0–1 years, 1–3 years, 3–5 years, 5–10 years, >19 years were 14.25 (11.22–18.11), 16.53 (13.74–19.90), 17.42 (13.74–22.10), 19.12 (14.97–24.43), and 36.17 (17.25–75.88), respectively (Figure 2 and Table S4). Amongst those who developed cancer on follow‐up, 256 (80.5%) had solid cancers, 53 (16.7%) had haematological cancers, and 9 (2.8%) were missing cancer site data. A detailed breakdown by cancer site is provided in Table S5.

FIGURE 2.

Incidence rate of post‐transplant de novo malignancy per 1000 person‐years, excluding non‐melanoma skin cancer.

For every 20% increase in the cumulative tacrolimus trough level in the first‐year post‐transplant, a significant increase in the risk of malignancy was observed (HR 1.09, 95% CI: 1.02–1.17). The corresponding NNH was approximately 780, implying that for every 780 patients exposed to a 20% higher tacrolimus trough level in the first year, one additional case of post‐transplant malignancy would occur. Although the NNH is relatively high, the cumulative impact could be substantial given the size of transplant populations.

Similarly, the risk of cancer was increased (HR 1.47, 95% CI: 1.03–2.11) in SOTRs in the highest quartile of tacrolimus exposure for the first year post‐transplant vs. the lowest quartile (Figure 3). Patients in the highest quartile of tacrolimus exposure within the first year had an NNH of 149. This suggests that for every 149 SOTRs in the highest exposure group, one additional malignancy would be attributable to the elevated tacrolimus levels.

FIGURE 3.

Forest plot comparing hazard ratios from different models. All models adjusted for the following variables: Age (per 5‐year increase), sex (male vs. female), history of cancer, diabetes, Charlson comorbidity index 11, solid organ transplant type (kidney, liver, heart, lung, pancreas, multi‐organ), neighbourhood income quintile, transplant era (2008 to 2011 vs. 2012 to 2015 vs. 2016 to 2020), estimated glomerular filtration rate (per 10 mL/min/1.73 m², 2021 Race‐Free CKD‐EPI ²¹ ), standard deviation of tacrolimus trough level over the first 12 months post‐transplant (continuous). For the last model, time‐varying cumulative tacrolimus exposure was defined as log‐transformed AUC of cumulative tacrolimus exposure over follow up.

The increased risk of malignancy persisted with cumulative exposure beyond the first year. For each 20% increase in the cumulative tacrolimus trough level over time, a significantly increased risk of post‐transplant malignancies was observed (HR 1.08, 95% CI: 1.01–1.15; Figure 2 and Table S6). The NNH was approximately 877, indicating that one additional case of malignancy would develop for every 877 patients exposed to this level of increased trough concentration. This reflects a lower relative risk compared to the early post‐transplant period but still underscores the importance of long‐term monitoring.

4. DISCUSSION

Cancer remains a leading cause of mortality among SOTRs, affecting over 20% of this population over time. ⁶ In our population‐based study, we found that 19.7% of SOTRs developed de novo malignancies (excluding non‐melanoma skin cancer) after a median follow‐up of 3.0 years. We found a significant association between cumulative, time‐varying tacrolimus exposure and cancer risk, with higher risk beyond 1 year among patients in the highest exposure quartile. A 20% increase in cumulative trough levels over time was linked to greater incidence of both solid and hematologic malignancies. The relationship appeared linear, without a threshold, suggesting even modest increases may elevate risk and highlighting the need for ongoing reassessment of immunosuppressive intensity.

Clinically, modest increases in trough levels within the first year (e.g., 7 to 8.4 ng/mL) were associated with a 9% higher malignancy risk; similar increases beyond the first year corresponded to an 8% higher risk. Patients in the highest quartile (median 9–10 ng/mL) had a 47% greater risk than those in the lowest (5–6 ng/mL). Our findings align with prior studies demonstrating that lower calcineurin inhibitor exposure is associated with a reduced risk of post‐transplant malignancy. In liver and kidney recipients, minimizing cumulative tacrolimus exposure, when clinically appropriate, has been shown to decrease cancer incidence without compromising graft outcomes. Regular monitoring and prompt dose adjustment to maintain the lowest effective exposure may therefore help mitigate long‐term oncologic risk without compromising graft survival. ¹² , ¹³ , ²²

Our findings build on prior literature showing a dose–response relationship between CNIs and cancer. ²³ We extend this by identifying a first‐year cumulative exposure level (3927.9 ng*day/mL) where risk becomes pronounced. Inclusion of multiple organ types captured the range of immunosuppressive practices, supporting individualized, risk‐adapted strategies. Minimizing tacrolimus is ideal but must be balanced against rejection risk; our results provide a basis for risk stratification and hypothesis generation rather than prescriptive reduction. Future work should evaluate CNI minimization in high‐risk groups (e.g., older recipients, prior cancer) and tailor cancer screening for those with the highest exposures, such as heart and lung recipients or patients with recurrent rejection. ²⁴ , ²⁵

This study has several limitations. Tacrolimus trough levels are practical surrogates of systemic exposure but imperfectly reflect true AUC, as correlations vary with metabolism, concomitant drugs, and formulation differences. ²⁶ We mitigated this by using all serial trough measurements to estimate cumulative exposure, though the absence of dosing data prevented dose‐normalized analyses that could further clarify inter‐individual variability and cancer risk. Although OLIS captures results from both public and private laboratories across Ontario, its implementation was gradual, and hospital data coverage was incomplete early in the study period. To mitigate this limitation, we restricted tacrolimus trough level measurements to outpatient settings. 2374 SOTRs were excluded due to insufficient outpatient trough data, which may limit the generalizability of our findings. Additionally, inpatient tacrolimus levels immediately following transplantation were not available and therefore not included in the calculation of cumulative exposure. Early post‐transplant exposure may influence long‐term outcomes, including malignancy risk, and our estimates likely reflect cumulative exposure during the maintenance phase rather than total peri‐transplant exposure. Additionally, because no universally accepted organ‐agnostic thresholds exist for defining low, conventional, or high cumulative tacrolimus exposure, we derived exposure categories empirically using quartiles of the observed distribution. While pragmatic, this approach may limit clinical interpretability and comparability across studies.

Second, our analysis did not include other CNIs or immunosuppressive agents. The exclusion of cyclosporine was deliberate, as our objective was to isolate the effect of tacrolimus, the most widely used CNI in contemporary transplant practice. Patients receiving cyclosporine were censored to minimize confounding. The effect of mTOR inhibitors was partially assessed as these medications would have been likely used in CNI minimization strategies leading to lower cumulative tacrolimus trough levels. However, their impact was not explicitly assessed in this study. Other maintenance immunosuppressants, such as antimetabolites and corticosteroids, were not evaluated, given their limited or inconsistent association with post‐transplant malignancy risk. Third, data on induction immunosuppression were unavailable. Although these agents are primarily associated with post‐transplant lymphoproliferative disorder (PTLD), we performed stratified analyses by cancer type (solid vs. hematologic), which did not reveal patterns suggestive of induction‐related malignancies. More detailed stratification by specific cancer subtypes was limited by small sample sizes, and key baseline risk factors, such as smoking status, were not captured. Finally, the relatively short median follow‐up of 3 years may underestimate the true long‐term incidence of post‐transplant malignancies, which often emerge over extended periods. Non‐melanoma skin cancers were excluded due to inconsistent reporting within the OCR.

In conclusion, our population‐based study demonstrates a linear association between higher cumulative tacrolimus trough levels and increased risk of solid and hematologic malignancies in solid organ transplant recipients. By including multiple organ types, we captured the real‐world diversity of immunosuppressive practices and demonstrated that even modest increases in cumulative exposure are associated with elevated cancer risk. These findings provide a critical foundation for risk stratification and future investigation into tailored immunosuppressive strategies. While immediate clinical changes may be premature, this study underscores the importance of integrating cancer risk into long‐term immunosuppression planning and post‐transplant surveillance.

AUTHOR CONTRIBUTIONS

Sergio A. Acuna: Conceptualization; methodology; supervision; project administration; writing – original draft; writing – review and editing; visualization. Xun Zhao: Methodology; supervision; writing – original draft; writing – review and editing. Maggie Jones‐Carr: Writing – original draft; writing – review and editing; visualization. Graham Smith: Formal analysis; methodology; writing – review and editing; validation; visualization; data curation; project administration. Kyla Naylor: Methodology; validation; formal analysis; data curation; project administration; writing – review and editing. Bima J. Hasjim: Writing – review and editing; visualization; project administration. Shiyi Chen: Writing – review and editing; visualization; project administration. An‐Wen Chan: Conceptualization; methodology; writing – review and editing. Nancy Baxter: Conceptualization; methodology; visualization; writing – review and editing. S. Joseph Kim: Writing – review and editing; methodology; conceptualization. Mamatha Bhat: Funding acquisition; writing – review and editing; conceptualization; project administration.

FUNDING INFORMATION

This project is supported by University of Toronto Department of Medicine Network Grant and University Health Network Foundation. Dr. Bhat has received investigator‐initiated grants from Novo Nordisk, Merck, Paladin, Transplant Genomics, Oncoustics, Roche. Dr. Zhao was supported by Canadian Association for the Study of Liver Fellowship (CASL) fellowship in 2023. Dr. Jones‐Carr is supported by U2C/TL1 Deep South KUH PRIME U2C DK133422 & TL1 DK139566 from the NIH/NIDDK and has received travel grants from the American Society of Transplantation, the University of Alabama at Birmingham, the Association for Academic Surgery, and the American College of Surgeons. Dr. Naylor is supported by a Health System Impact Embedded Early Career Researcher Award from the Canadian Institutes of Health Research. This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long‐Term Care (MLTC). The study was completed at the ICES Western site. This document used data adapted from the Statistics Canada Postal Code^OM Conversion File, which is based on data licensed from Canada Post Corporation, and/or data adapted from the Ontario Ministry of Health Postal Code Conversion File, which contains data copied under license from ©Canada Post Corporation and Statistics Canada. Parts of this material are based on data and/or information compiled and provided by: the Ontario Ministry of Health, Canadian Institute for Health Information, and Ontario Health. The opinions, results, view, and conclusions reported in this paper are those of the authors and do not necessarily reflect those of the funding or data sources. No endorsement is intended or should be inferred. We thank IQVIA Solutions Canada Inc. for use of their Drug Information File. The sources of funding had no role in study design, the analysis, collection, or interpretation of the data, the writing of the report, or the decision to submit the paper for publication.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflicts of interest relevant to this work.

Supporting information

DATA S1. Supporting Information.

Acuna SA, Zhao X, Jones‐Carr M, et al. Cumulative exposure to tacrolimus is associated with increased risk of malignancy for solid organ transplant recipients. Int J Cancer. 2026;158(12):3152‐3160. doi: 10.1002/ijc.70343

DATA AVAILABILITY STATEMENT

The dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre‐specified criteria for confidential access, available at www.ices.on.ca/DAS (email: das@ices.on.ca). The full dataset creation plan and underlying analytic code are available from the corresponding author upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification. Further details and other data that support the findings of this study are available from the corresponding author upon request.