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Review

Tumor Necrosis Factor-Alpha Inhibitor Use and Malignancy Risk: A Systematic Review and Patient Level Meta-Analysis

by
Conor B. Driscoll
1,*,
Jordan M. Rich
1,
Dylan Isaacson
1,
Joseph Nicolas
1,
Yu Jiang
2,
Xinlei Mi
3,
Christopher Yang
1,
Victoria Kocsuta
1,
Regine Goh
1,
Niti Patel
4,
Eric Li
1,
Mohammad Rashid Siddiqui
1,
Travis Meyers
5,
John S. Witte
2,6,
Linda Kachuri
2,6,
Hui Zhang
3,
Molly Beestrum
7,
Philip Silberman
8,
Edward M. Schaeffer
1 and
Shilajit D. Kundu
1
1
Department of Urology, Northwestern University Feinberg School of Medicine, 676 North St Clair Street, Suite 2300, Chicago, IL 60611, USA
2
Department of Epidemiology and Population Health, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
3
Division of Biostatistics, Department of Preventative Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr, Suite 1400, Chicago, IL 60611, USA
4
Department of Medicine, Northwestern University Feinberg School of Medicine, 676 North St. Clair Street, Arkes Suite 2330, Chicago, IL 60611, USA
5
Department of Epidemiology and Biostatistics, University of California at San Francisco, 550 16th St., Floor 2, San Francisco, CA 94143, USA
6
Department of Biomedical Data Science, Stanford University, 1265 Welch Road MC5464MSOB West Wing, Third Floor, Stanford, CA 94305, USA
7
Galter Health Sciences Library and Learning Center, Northwestern University, 303 E Chicago Ave #2-212, Chicago, IL 60611, USA
8
Department of Information Technology, Northwestern University Feinberg School of Medicine, 710 N. Lake Shore Dr, Abbott Hall, 4th Floor, Chicago, IL 60611, USA
*
Author to whom correspondence should be addressed.
Cancers 2025, 17(3), 390; https://doi.org/10.3390/cancers17030390
Submission received: 26 November 2024 / Revised: 16 January 2025 / Accepted: 22 January 2025 / Published: 24 January 2025
(This article belongs to the Section Systematic Review or Meta-Analysis in Cancer Research)
Browse Figures
Review Reports Versions Notes

Simple Summary

TNF-α inhibitors are widely prescribed immunosuppressive medications for multiple chronic inflammatory disorders. Chronic immunosuppression can lead to malignancy due to decreased immune surveillance, but TNF-α inhibitors have been found to be largely safe aside from the development of non-melanoma skin cancer. However, these studies all have short-term follow-up, rely on spontaneous reporting systems, and investigate a disease or drug subset. Previous meta-analyses similarly have investigated either interventional or observational studies only. Here, we reviewed over 17,000 abstracts with the most stringent inclusion criteria in the published literature to analyze 46 interventional studies and 10 observational studies for malignancy risk in patients exposed to TNF-α inhibitors. Using patient-level data, our findings for both interventional and observational studies found no statistically significant increased risk of malignancy but with poor quality of adverse event reporting. This is the most comprehensive and up-to-date meta-analysis and underscores the need for high-quality studies with a longer follow-up of these patients.

Abstract

Background/Objectives: Tumor necrosis factor-alpha inhibitors (TNF-I) are widely prescribed for chronic inflammatory conditions. Prior studies of TNF-I use and malignancy risk have not demonstrated consistent associations outside non-melanoma skin cancers. We performed an updated systematic review and meta-analysis of interventional and observational studies to investigate this potential relationship. Methods: We searched MEDLINE, EMBASE, CENTRAL Register, Cochrane Library, and Scopus for studies published between 1996 and 2020. Two observers independently extracted the data; differences were resolved by consensus. Candidate studies were classified as interventional or observational per study design. A random-effects meta-analysis estimated malignancy rate ratios (MRRs) for cancers in TNF-I-exposed patients. Forest plots used a standard rate scale, and funnel plots used a random-effects model to assess for publication bias. Results: 45 interventional studies contributing 22,652 patients and 10 observational studies contributing 34,866 patients met the meta-analysis inclusion criteria. The plurality of these studies focused on adalimumab for both interventional (38%) and observational (50%) studies. TNF-I exposure was not associated with overall cancer risk in interventional (RR = 1.04, 95% CI 0.71–1.51) or observational studies (RR = 1.42, 95% CI 0.72–2.79). In total, 9/45 (20%) interventional studies and 0/10 (0%) observational studies had adequate quality of adverse event reporting, and 52/55 (95%) had either industry or unknown funding sources. Conclusions: Our synthesis of published findings from interventional and observational studies had inconclusive evidence on TNF-I exposure and cancer risk. Given the small number of studies designed to investigate malignancy risk, the low number of studies with adequate adverse event reporting, and the high number of industry-funded studies, it suggests that higher quality prospective studies are warranted to investigate the risk of malignancy for patients on chronic TNF-I.

1. Introduction

Over the last two decades, tumor necrosis factor-alpha inhibitors (TNF-Is) have become standard therapies for chronic inflammatory disorders, with an ongoing expansion of indications and off-label applications [1]. Presently, five TNF-Is have been approved by the U.S. Food and Drug Administration (FDA) for the treatment of inflammatory conditions: Adalimumab (ADA) and golimumab (GLM) are fully humanized monoclonal tumor necrosis factor-alpha (TNF-α) IgG1 antibodies, infliximab (INX) is a mouse/human chimeric monoclonal TNF-α IgG1, certolizumab pegol (CTZ) is a PEGylated Fab’ fragment of a TNF-α monoclonal antibody, and etanercept (ETN) is a fusion protein of the human tumor necrosis factor receptor II extracellular domain and the IgG1 Fc region [2]. The wide-ranging and effective use of these medications is typified by the status of adalimumab—the most profitable drug worldwide for much of the past decade [3,4,5]. Though their structures and exact functions vary [6], these agents all inhibit the binding of soluble TNF-α to its receptor [2], inhibiting the downstream production of pro-inflammatory cytokines and chemokines, the upregulation of adhesion molecules, and the activation of peripheral blood mononuclear cells [1]. Concurrent with the roles of chronic inflammation in promoting carcinogenesis [7] and of immune activation in clearing malignant cells, it remains unclear whether TNF-α exerts tumorigenic or anti-neoplastic activity, or both depending on the host immunologic context [8,9,10].
Multiple groups have evaluated the relationship between TNF-I exposure and malignancies. The strongest data are those associating TNF-I use with non-melanoma skin cancers (NMSCs), for which institutional cohort studies [11], registry-based studies [12,13,14], and meta-analyses [15,16,17] have demonstrated an increased risk. Some reports have suggested a higher incidence of lymphomas in patients taking TNF-I [12,18,19,20,21]; however, these analyses were potentially confounded by associations with underlying disease states and other medication use [10,22], and their findings have largely been outweighed by several studies in diverse populations demonstrating no increased risk [23,24,25,26,27,28,29]. For patients with pre-existing malignancies, current clinical practice states that biologic therapy, such as that with TNF-I, is not absolutely contraindicated and must be approached on a case-by-case basis with the patient [30].
TNF-I use is frequently presumed to have no influence on the development of other malignancies, as demonstrated by the results of multiple clinical trials, registry-based studies, and meta-analyses conducted over the last decade [17,27,31,32,33,34,35]. However, these studies have key limitations—most clinical trials of TNF-I were primarily designed to assess drug efficacy, and many co-administered medications such as methotrexate and were not powered specifically to detect incident malignancies [36]. Studies drawing from administrative databases rely on spontaneous adverse event reporting and algorithms for identifying malignancies, which risks underestimating overall cancer incidence, false positives, and bias from the systematic misclassification of suspected cases [37]. Existing meta-analyses incorporate these limitations of primary studies and have been constrained in evaluating only specific subsets of approved TNF-I [17,38], single malignancy types [16], second malignancies [35] or specific inflammatory disease states [15,16,31,32,33,34,35,38]. These meta-analyses have been further restricted to include only investigator-controlled interventional studies [17,31,32,33,34] or observational registry-based analyses [27]. In parallel, data from the FDA Adverse Events Reporting System describe cancers associated with TNF-I use in excess of the subset reported in the scientific literature [39], suggesting a potentially unappreciated relationship.
Given the limitations of current meta-analyses, we sought to comprehensively evaluate associations between all approved TNF-Is and the development of malignancies. In this work, we systematically review contemporary interventional and observational studies and perform meta-analyses on these two groups of studies. To the extent of our knowledge, this is the broadest attempt to evaluate the relationship between TNF-I and incident malignancies to date.

2. Materials and Methods

2.1. Relevant Publication Search

We searched MEDLINE for studies published between January 1996 and January 2020 evaluating approved TNF-I using the medical subject headings (MeSH) “TNF-alpha Inhibitors”, “adalimumab”, “certolizumab”, “etanercept”, “golimumab”, “infliximab”, “safety”, “side effects”, “toxicity”, “neoplasms”, “cancer”, “malignan*”, “adverse”, “longitudinal studies”, “short term”, and “long term”. We also searched using keywords for TNF-I trade names. We searched ClinicalTrials.gov using the keywords “TNF-alpha Inhibitors” and each of the five individual drug names. We replicated this search process in EMBASE (Elsevier), the CENTRAL Register of Controlled Trials (Wiley), the Cochrane Library (Wiley), Scopus (Elsevier), and the gray literature sources. The full search strategy, using infliximab as an example, is reproduced in the Supplementary Methods.

2.2. Inclusion and Exclusion Criteria

Criteria for inclusion in the meta-analysis were as follows: (1) Clinical trials or population-based observational studies of TNF-I approved for use in the United States, Europe, or Asia. (2) Presence of a safety analysis delineated by individual TNF-I. (3) Use of a TNF-I in a systemically absorbed formulation, such as intravenous or subcutaneous. The two medications administered intravenously are INX (3 mg/kg every 8 weeks) and GLM (100 mg every 4 weeks after induction) are administered intravenously. The three medications administered subcutaneously are ADA (40 mg every other week), ETN (total of 50 mg weekly), and CTZ (total of 400 mg every 4 weeks after induction).
Exclusion criteria were as follows: (1) Non-experimental study design—practice updates, expert opinions, letters, abstracts, case series, or case reports. (2) Publication in a language other than English. (3) Studies conducted on non-human subjects. (4) Analyses focusing on a single non-malignancy adverse event (i.e., tuberculosis). (5) Studies not performed using primary data (i.e., insurance claims databases, meta-analyses). (6) Single-dose studies assessing immediate infusion side effects. (7) Trials in which experimental medications were co-administered with TNF-I. (8) TNF-I withdrawal studies. (9) Studies incorporating a crossover component between a TNF-I and another experimental drug.

2.3. Data Extraction

Candidate studies were classified as interventional if prospectively assessing the effects of a TNF-I prescribed per experimental design, or observational if investigators played no role in dictating TNF-I administration. Studies were evaluated for TNF-I type, dosage and disease indication, study design, inclusion/exclusion criteria, sample size and duration of follow-up, patient demographics, incident malignancy cases, other adverse events, subject withdrawal rate, reported conflicts of interest, and funding sources. Two observers (C.D. plus R.G., N.P., D.I., E.L., or M.R.S.) independently extracted the data; differences were resolved by consensus.

2.4. Quality of Adverse Event and Risk of Bias Classification

We classified the adequacy of blinding as present if explicitly reported, “unknown” if not reported, or “not applicable” for single-arm studies. We scored drug safety reporting as “adequate”, “partially adequate”, or “inadequate” as described by Ioannidis and Lau [40]. Reports explicitly noting no adverse events were scored as “none”. Studies were classified as having “adequate” quality of adverse event reporting if they graded severity using a graduated toxicity scale and reported adverse event incidence in each study arm versus “partially adequate” if they failed to distinguish between moderate and severe toxicities or otherwise did not meet criteria for the “adequate” or “inadequate” categories. Studies were classified as “inadequate” if they did not detail the specific classes of adverse events, provided only generic statements, or omitted comment entirely. The quality of adverse event classifications for studies included in the meta-analyses are listed in Supplementary Table S1.
Risk of bias was assessed with the modified Cochrane analysis tool [41] for randomized studies and the ROBINS-I analysis tool for non-randomized studies [42]. Risk of bias assessments for included studies are detailed in Supplementary Table S2.

2.5. Statistical Analysis

For studies meeting criteria, we estimated malignancy rate ratios (MRRs) for the TNF-I-exposed vs. control groups with direct calculation. For studies evaluating multiple TNF-Is, each individual TNF-I-exposed group was analyzed separately against the control group. MRRs were estimated for overall (aggregate) malignancies excluding NMSC and for individual cancer subtypes [43]. In studies reporting no malignancies in either the TNF-I-exposed or control group, a continuity correction of 0.5 was applied to both groups. Studies reporting no malignancies in both groups were excluded from analysis. We used a random-effects meta-analysis model to account for potential heterogeneity between studies and to estimate pooled effects (95% confidence intervals) for rate ratios. The overall effect estimates are presented as a weighted mean of the study-specific estimates [44]. p values < 0.05 were considered statistically significant throughout the study and determined in a two-sided manner. Some studies involved multiple drugs and utilized shared unexposed controls, and there can be overlap between the contributed patient-years across multiple drug arms, from which dependencies can arise. In the cross-drug meta-analysis of the observational studies, we took such dependencies into account and used the rma.mv function in the metaphor package in R to model the non-independence structure induced by overlapping unexposed controls in the same study [45].
Forest plots were created using a standard rate scale to assess for publication bias. Studies with no incident malignancies other than NMSC were excluded from these plots. Study heterogeneity was evaluated using Cochran’s Q statistic and the I 2 statistic. I 2 > 50 % was taken to represent moderate heterogeneity and I 2 > 75 % to represent high heterogeneity.
Publication bias within studies was assessed with a funnel plot using a random-effects model. Rank correlation tests were used to assess for funnel plot asymmetry for the observational and interventional studies of each TNF-I and for observational and interventional studies aggregating all TNF-Is. Statistical analyses were carried out using R 4.0.2 (R Foundation, Vienna, Austria) and the metafor package [46].

3. Results

3.1. Search Results

We identified 16,150 candidate publications, yielding 55 unique studies for quantitative analysis (Figure 1). In total, 45/55 (82%) of the identified studies were interventional [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91], and 10/55 (18%) were observational [25,92,93,94,95,96,97,98,99,100]. The PRISMA 2020 flowchart is included as Figure 1.

3.2. Interventional Study Results

The 45 interventional studies incorporated into the meta-analysis contributed a total of 22,652 patients with a median exposure of 41 weeks (IQR of 24–60 weeks) (Table 1). In total, seventeen of these studies reported on ADA (one study contained two separate randomized trials in the manuscript) [61], twelve on INX, four on ETN, six on CTZ, and seven on GLM. The median number of subjects exposed to a TNF-I was 360 (IQR of 216–634 patients). The median length of follow-up was 41 weeks (IQR of 24–64 weeks). These studies contributed a total of 146 malignancies over 15,120 patient-years of exposure to TNF-I: thirteen lymphomas, twelve breast cancers, twelve lung cancers, nine melanomas, eight colorectal cancers, five pancreatic cancers, four head and neck cancers, four ovarian cancers, three gastric cancers, three cervical cancers, two brain cancers, two renal cell carcinomas, two prostate cancers, two unspecified adenocarcinomas, one cholangiocarcinoma, one leukemia, one gastrointestinal carcinoid tumor, one bladder cancer, one testicular cancer, one endometrial cancer, and fifty-nine unspecified cancers (Supplementary Table S1). In total, 62 NMSCs were also recorded but not used in the meta-analysis.
There were 29 malignancies recorded over 4695 patient-years of placebo and/or non-TNF-I drug exposure: four breast cancers, three lymphomas, three prostate cancers, two cervical cancers, two lung cancers, one colorectal cancer, one esophageal cancer, one thyroid cancer, and twelve unspecified cancers (Supplementary Table S1). Nine NMSC were also recorded but not used in the meta-analysis.
The malignancy rate ratio for any malignancy was MRR 1.04 (95% CI 0.71–1.51) (Figure 2). For individual drugs, malignancy rate ratios were as follows: ADA (MRR 1.19, 95% CI 0.44–1.03), INX (MRR 1.35, 95% CI 0.67–2.72), ETN (MRR 0.80, 95% CI 0.22–2.88), CTZ (MRR 1.60, 95% CI 0.72–3.57), and GLM (MRR 1.49, 95% CI 0.59–3.77) (Figure 3A–E). Funnel plots did not demonstrate statistically significant asymmetry for overall TNF-I exposure or for any individual TNF-I exposure (Supplementary Figure S1).
In the interventional studies, only 9/45 (20%) had adequate quality of adverse event reporting according to the Ioannidis and Lau criteria, and 44/45 (98%) had either industry or unknown funding sources (Table 2). In total, 38/45 (84%) of the interventional studies had a low risk of bias due to high-quality randomization per the Cochrane criteria (Supplementary Table S2).

3.3. Observational Study Results

The ten observational studies meeting the criteria for the meta-analysis contributed 34,866 patients with a median of 83 weeks of exposure (IQR 52–171 weeks) (Table 1). Five reported on ADA, five on INX, and five on ETN, with four of those studies reporting on more than one TNF-I. For these studies, the median number of patients exposed to a TNF-I was 1606 patients (IQR 433–3057 patients). The median length of follow-up was 82 weeks (IQR 54–171 weeks). There were 108 malignancies in 28,796 patient-years of TNF-I exposure: thirty-seven lymphomas, ten lung cancers, three head and neck cancers, three prostate cancers, two breast cancers, one esophageal cancer, one brain cancer, one colorectal cancer, one kidney cancer, and forty-nine unspecified cancers (Supplementary Table S1). There were also five NMSCs identified in the exposed subjects not included in analysis. There were 57 malignancies in 26,154 patient-years of placebo and/or non-TNF-I drug exposure: thirty-two lymphomas, five prostate cancers, two lung cancers, and eighteen unspecified cancers (Supplementary Table S1). One NMSC was also identified in these subjects and not included.
The malignancy rate ratio for any malignancy was MRR 1.42 (95% CI 0.72–2.79) (Figure 4). For individual drugs, the rate ratios were as follows: ADA (MRR 1.19, 95% CI 0.44–3.22), INX (MRR 1.69, 95% CI 0.83–3.44), and ETN (MRR 1.85, 95% CI 0.50–6.89) (Figure 5A–C). There were no observational studies of certolizumab or golimumab meeting the inclusion criteria. Funnel plots did not demonstrate statistically significant asymmetry for overall TNF-I exposure or for any individual TNF-I exposure (Supplementary Figure S2).
In the observational studies, 0/10 (0%) had adequate quality of adverse event reporting according to the Ioannidis and Lau criteria, and 8/10 (80%) had either industry or unknown funding sources (Table 2). In total, 2/10 (20%) of the observational studies were unlikely to have bias due to confounding, 10/10 (100%) were unlikely to have bias due to participant selection, 6/10 (60%) were unlikely to have bias due to the classification of interventions or deviation from intended interventions per the ROBINS-I criteria (Supplementary Table S2).

4. Discussion

We conducted a systematic review and meta-analyses of interventional and observational studies to evaluate the relationship between TNF-I exposure and the development of malignancies. This meta-analysis of 45 interventional studies did not demonstrate a statistically significant difference in cancer incidence for patients on a TNF-I (RR 1.04, 95% CI 0.71–1.51) (Figure 2). This meta-analysis of 10 observational studies also did not show a statistically increased risk of cancers in patients taking TNF-I (RR 1.42, 95% CI 0.72–2.79) (Figure 4) but did suggest a trend toward significance with both longer exposure to TNF-I and longer follow-up. This meta-analyses did not find differences in risk for patients taking specific TNF-I (Figure 3 and Figure 5), which is likely due to limited power when dividing the cohort up by individual drug exposure. Given its contemporary nature and scale, we believe the results presented here represent the broadest effort to date to define this relationship.
The most important finding of this study, however, is that the vast majority of the trials used to approve TNF-I indications, and the vast majority of long-term post-marketing studies, are inadequate in their quality of adverse event follow-up and have a significant risk for conflicts of interest and bias in reporting, with only 9 out of 55 studies (16.4%) demonstrating adequate adverse event reporting according to the Ioannidis and Lau criteria, only 3 out of 55 studies (5.5%) demonstrating clear non-industry funding, and only 40/55 (72.7%) demonstrating a low risk of bias per the Cochrane (interventional studies) and ROBINS-I (observational) criteria. Our meta-analysis is the first to report on these quality metrics for TNF-I studies and use the most stringent criteria for study inclusion. Many of the foundational efficacy randomized control trials were excluded due to the co-administration of drugs such as methotrexate or a trial design containing a drug crossover component. Another strength of our study is using patient-level data to collate and analyze individual and overall malignancy data.
The results of our meta-analyses are consistent with the antecedent literature and the findings of several prior meta-analyses, which did not demonstrate an effect of TNF-I use on the development of malignancies other than NMSC [15,17,30,31,32,33,34]. Several factors complicate the interpretation of these results. Most interventional studies of TNF-I were designed and powered to determine drug efficacy and rarely have follow-up greater than one year—as evidenced by the median length of follow-up of 41 weeks (IQR of 24–64 weeks) in our meta-analysis of the interventional studies. This may be an insufficient length of observation to define a difference in long-term cancer rates. The meta-analysis of observational studies incorporated datasets with a longer median follow-up of 82 weeks (IQR of 54–171 weeks) and indicated an increased risk of malignancies with TNF-I exposure, although not a statistically significant one. The meta-analyses of observational studies are challenging due to issues of publication bias and study and patient-level heterogeneity [27]. This was reflected in our quality of adverse events and risk of bias assessments (Table 2 and Supplementary Table S2) and could have potentially influenced our results despite attempts to restrict heterogeneity. Furthermore, few observational studies included in the meta-analysis directly compared a TNF-I to a placebo, with the comparator group usually being an anti-rheumatic medication other than a TNF-I (Table 1). Furthermore, there is no consensus in the literature as to what would be a biologically plausible timeframe post-drug exposure for malignancy development, which led to our decision to use 6 months post-exposure as a cutoff for biologic plausibility.
Our meta-analysis is also the first to our knowledge to employ this time-dependent follow-up analysis for drug exposure, which allowed us to meaningfully stratify patient cohorts into a model with significantly higher statistical power.

5. Conclusions

Our systematic review and meta-analyses of interventional and observational studies showed evidence of a relationship between TNF-I use and reported malignancies without reaching conventional thresholds for statistical significance. There were only a small number of high-quality studies with adequate adverse event reporting and an even smaller subset of those designed to investigate malignancy risk. These findings warrant further investigations over longer timeframes and the continued examination of the relationship between chronic inflammation, TNF-α signaling, and tumorigenesis.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers17030390/s1, Methods S1: PubMed MEDLINE Search Strategy; Figure S1: Random-effect funnel plots of interventional studies for (A) all TNF-I; (B) adalimumab (ADA); (C) infliximab (INX); (D) etanercept (ETN); (E) certolizumab (CTZ); (F) golimumab (GOL); Figure S2: Random-effect funnel plots of observational studies for (A) all TNF-I; (B) adalimumab (ADA); (C) infliximab (INX); (D) etanercept (ETN). Table S1: Malignancy Incidence of Interventional and Observational Studies of Patients Exposed to TNF-I; Table S2: Risk of bias assessments: (A) Cochrane (randomized) and (B) ROBINS-I (non-randomized).

Author Contributions

(CRediT author statement): C.B.D.: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Data Curation, Writing—Original Draft, Writing—Review and Editing; J.M.R.: Investigation, Data Curation, Writing—Review and Editing; D.I.: Validation, Investigation, Data Curation, Writing—Review and Editing; J.N.: Investigation, Data Curation; Y.J.: Methodology, Validation, Formal Analysis; X.M.: Methodology, Validation, Formal Analysis; C.Y.: Investigation, Data Curation; V.K.: Investigation, Data Curation; R.G.: Investigation, Data Curation; N.P.: Investigation, Data Curation, Writing—Review and Editing; E.L.: Investigation, Data Curation; M.R.S.: Investigation, Data Curation; T.M.: Methodology, Validation, Formal Analysis; J.S.W.: Methodology, Validation, Formal Analysis, Writing—Review and Editing; L.K.: Methodology, Validation, Formal Analysis, Writing—Review and Editing; H.Z.: Methodology, Validation, Formal Analysis, Writing—Review and Editing; M.B.: Methodology, Investigation; P.S.: Methodology, Investigation; E.M.S.: Conceptualization, Supervision, Project Administration, Funding Acquisition; S.D.K.: Conceptualization, Methodology, Validation, Resources, Supervision, Project Administration, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Polsky Urologic Cancer Institute and the Department of Urology at Northwestern University Feinberg School of Medicine. The funder did not play a role in the design of this study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset supporting the conclusions of this article is available in the GitHub repository, https://github.com/conordriscoll/TNF-Alpha-Meta-Analysis.git (accessed on 15 July 2020).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

ADAAdalimumab
ASAnkylosing spondylitis
CDCrohn’s disease
COPDChronic obstructive pulmonary disease
CTZCertolizumab
DMARDDisease-modifying anti-rheumatic drug(s)
ETNEtanercept
FAERS[United States] Food and Drug Administration Adverse Events Reporting System
FDA[United States] Food and Drug Administration
GBMGlioblastoma multiforme
GLMGolimumab
HLHodgkin’s Lymphoma(s)
HRHazard ratio(s)
HSHidradenitis suppurativa
INXInfliximab
IQRInterquartile range
IRBInstitutional Review Board
JIAJuvenile idiopathic arthritis
MeSH[National Library of Medicine] Medical subject heading(s)
MTXMethotrexate
NHLNon-Hodgkin’s Lymphoma(s)
NMSCNon-melanoma skin cancer(s)
OAOsteoarthritis
PSPsoriasis
PSAPsoriatic arthritis
RARheumatoid arthritis
RRRate ratio(s)
SCCSquamous cell carcinoma
SDStandard deviation(s)
SPASpondyloarthropathy/spondyloarthropathies
SSSjogren’s syndrome
TNF-α Tumor necrosis factor-alpha
TNF-iTumor necrosis factor-alpha inhibitor(s)
TNF-RTumor necrosis factor receptor
UCUlcerative colitis
95% CI95% confidence interval

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Cancers 17 00390 g001
Figure 1. PRISMA flow diagram (2020).
Figure 1. PRISMA flow diagram (2020).
Cancers 17 00390 g001
Cancers 17 00390 g002
Figure 2. Malignancy rate ratio in patients exposed to any TNF-I excluding NMSC in interventional studies [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91].
Figure 2. Malignancy rate ratio in patients exposed to any TNF-I excluding NMSC in interventional studies [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91].
Cancers 17 00390 g002
Cancers 17 00390 g003aCancers 17 00390 g003bCancers 17 00390 g003c
Figure 3. Calculated malignancy rate ratio in patients exposed to any TNF-alpha inhibitor (TNF-I) using cancers excluding non-melanoma skin cancer (NMSC) in 45 interventional studies for (A) adalimumab (ADA); (B) infliximab (INX); (C) etanercept (ETN); (D) certolizumab (CTZ); (E) golimumab (GOL) [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91].
Figure 3. Calculated malignancy rate ratio in patients exposed to any TNF-alpha inhibitor (TNF-I) using cancers excluding non-melanoma skin cancer (NMSC) in 45 interventional studies for (A) adalimumab (ADA); (B) infliximab (INX); (C) etanercept (ETN); (D) certolizumab (CTZ); (E) golimumab (GOL) [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91].
Cancers 17 00390 g003aCancers 17 00390 g003bCancers 17 00390 g003c
Cancers 17 00390 g004
Figure 4. Malignancy rate ratio in patients exposed to any TNF-I excluding NMSC in observational studies [25,92,93,94,95,96,97,98,99,100].
Figure 4. Malignancy rate ratio in patients exposed to any TNF-I excluding NMSC in observational studies [25,92,93,94,95,96,97,98,99,100].
Cancers 17 00390 g004
Cancers 17 00390 g005aCancers 17 00390 g005b
Figure 5. Calculated malignancy rate ratio in patients exposed to any TNF-alpha inhibitor (TNF-I) using cancers excluding non-melanoma skin cancer (NMSC) in 10 observational studies for (A) adalimumab (ADA); (B) infliximab (INX); (C) etanercept (ETN) [25,92,93,94,95,96,97,98,99,100].
Figure 5. Calculated malignancy rate ratio in patients exposed to any TNF-alpha inhibitor (TNF-I) using cancers excluding non-melanoma skin cancer (NMSC) in 10 observational studies for (A) adalimumab (ADA); (B) infliximab (INX); (C) etanercept (ETN) [25,92,93,94,95,96,97,98,99,100].
Cancers 17 00390 g005aCancers 17 00390 g005b
Table 1. Interventional and observational study characteristics of patients exposed to TNF-I.
Table 1. Interventional and observational study characteristics of patients exposed to TNF-I.
Author, YearIndex Disease ^Patients, Total No.Patients, No. on TNF-a InhibitorPatients, No. Unexposed to TNF-a InihibitorComparator ArmDuration of Exposure, wk
Interventional
ADA ^
Bissonnette, 2017 [47]PS10710753Placebo 52
Cai, 2016 [48]PS42533387Placebo24
Colombel, 2014 [49]UC1093101054Placebo52
Furst, 2003 [50]RA636318318Placebo24
Genovese, 2007 [51]PSA100100 *49 *Placebo24
Jaffe, 2016 [52]Noninfectious uveitis217110107Placebo24
Kamm, 2011 [53]CD778519107Placebo164
Kimball, 2016 [54]HS633361 252Placebo36
Leonardi, 2011 [55]PS7272 * 23 *Placebo28
Menter, 2010 [56]PS1212783398Placebo52
Miyasaka, 2008 [57]RA352265 *87 *Placebo24
Mostafa, 2017 [58]PS1212809398Placebo52
Reinisch, 2011 [59]UC576353223Placebo8
Suzuki, 2017 [60]UC274266 *96 *Placebo196
van de Putte, 2004 [61]RA544434 *110 *Placebo26
van der Heijde, 2009 [62]AS315311107Placebo104
Gordon, 2006 [63]PS14714752Placebo60
INX ^
Kavanaugh, 2007 [64]PSA200100100Placebo52
Koninckx, 2008 [65]Endometriosis21137Placebo12
Mariette, 2004 [66]SS1035449Placebo6
Menter, 2007 [67]PS835627208Placebo27.5
Rennard, 2007 [68]COPD234157 *77 *Placebo24
Rutgeerts, 1999 [69]CD733736Placebo48
Reinisch, 2012 [70]UC291229 *62 *Placebo103.5
Rutgeerts, 2005 [71]UC364243 *121Placebo46
Rutgeerts, 2005 [71]UC364241 *123Placebo22
Regueiro, 2016 [72]CD297147150Placebo74.3
Sandborn, 2004 [73]CD396263132Placebo28
Braun, 2008 [74]AS27920178Placebo102
ETN ^
Dijkmans, 2009 [75]AS8481 *39 *Placebo108
Kloppenburg, 2018 [76]OA904545Placebo52
Tyring, 2007 [77]PS591464 *307 *Placebo96
Weisman, 2007 [78]RA535266269Placebo16
GLM ^
Bao, 2014 [79]AS213203105Placebo24
Deodhar, 2015 [80]AS35625878Placebo252
Husni, 2020 [81]PSA480480 *239 *Placebo52
Takeuchi, 2017 [82]RA316154 *105 *Placebo120
Wenzel, 2009 [83]Asthma309231 *78 *Placebo52
Emery, 2013 [84]RA637159160Placebo + MTX104
Kremer, 2010 [85]RA643257129Placebo + MTX48
CTZ ^
Mease, 2014 [86]PSA409159136Placebo24
Sandborn, 2007 [87]CD662202329Placebo26
Weinblatt, 2015 [88]RA1063809 *212 *Placebo28
Sandborn, 2011 [89]CD439215209Placebo6
Loftus Jr, 2016 [90]CD34452570875Placebo36.29
Yamamoto, 2014 [91]RA230116114Placebo24
Observational
ADA ^
Klotsche, 2016 [92]JIA31893201455Methotrexate, ETN80
Mercer, 2017 [25]RA15,29842883367DMARD, ETA, IFX182
Lunder, 2019 [93]PS1606750713Ixekizumab, Apremilast, Secukinumab, Ustekinumab171.4
Viguier, 2012 [94]PS2875Ustekinumab, Efalizumab, IFX, ETN24
Mason, 2018 [95]PS853336671518Ustekinumab52
INX ^
Lunder, 2019 [93]PS160677713Ixekizumab, Apremilast, Secukinumab, Ustekinumab171.4
Mok, 2014 [96]RA1769671290Abatacept, Belimumab, Rituximab, Tocilizumab128.6
Nossent, 2001 [97]Polyarthritis19910Methylprednisolone12
Sakai, 2012 [98]RA1028412168Tocilizumab78
Rennard, 2012 [99]COPD23415777Placebo252
ETN ^
Driessen, 2009 [100]PS1189414Efalizumab83.2
Lunder, 2019 [93]PS160666713Apremilast, Ixekizumab, Secukinumab, Ustekinumab171.4
Mason, 2018 [95]PS853313641518Ustekinumab52
Mok, 2014 [96]RA1769603290Abatacept, Belimumab, Rituximab, Tocilizumab128.6
Sakai, 2012 [98]RA1028442168Tocilizumab78
* manually calculated due to initial time spent in placebo arm. ^ adalimumab (ADA), infliximab (INX), etanercept (ETN), certolizumab (CTZ), golimumab (GLM), psoriasis (PS), psoriatic arthritis (PSA), ulcerative colitis (US), Crohn’s disease (CD), hidradenitis suppurativa (HS), rheumatoid arthritis (RA), Sjogren’s syndrome (SS), chronic obstructive pulmonary disease (COPD), ankylosing spondylitis (AS), osteoarthritis (OA), juvenile idiopathic arthritis (JIA).
Table 2. Quality of adverse event reporting (Ioannidis and Lau criteria) [33]: Interventional studies and observational studies.
Table 2. Quality of adverse event reporting (Ioannidis and Lau criteria) [33]: Interventional studies and observational studies.
Quality of Adverse Event ReportingAdequacy of BlindingConflicts of InterestFunding Source
ADA *
Bissonnette, 2017 [47]Partially adequateYesYesIndustry
Cai, 2016 [48]Partially adequateYesNoIndustry
Colombel, 2014 [49]Partially adequateNot ApplicableUnknownUnknown
Furst, 2003 [50]AdequateYesYesIndustry (Abbott)
Genovese, 2007 [51]Partially adequateUnknownUnknownUnknown
Jaffe, 2016 [52]Partially adequateUnknownUnknownIndustry
Kamm, 2011 [53]Partially adequateYesYesIndustry
Kimball, 2016 [54]AdequateYesUnknownUnknown
Leonardi, 2011 [55]InadequateYesUnknownIndustry
Menter, 2010 [56]Partially adequateNot ApplicableUnknownIndustry
Miyasaka, 2008 [57]InadequateYesYesIndustry (Abbott)
Mostafa, 2017 [58]Partially adequateYesUnknownIndustry
Reinisch, 2011 [59]Partially adequateYesUnknownIndustry
Suzuki, 2017 [60]Partially adequateNot ApplicableUnknownIndustry
van de Putte, 2004 [61]Partially adequateYesYesIndustry (Abbott)
van der Heijde, 2009 [62]AdequateYesUnknownIndustry
Gordon, 2006 [63]AdequateNot ApplicableUnknownIndustry
INX *
Kavanaugh, 2007 [64]InadequateYesYesIndustry
Koninckx, 2008 [65]InadequateYesUnknownUniversity grant
Mariette, 2004 [66]InadequateYesUnknownUnknown
Menter, 2007 [67]AdequateYesUnknownUnknown
Rennard, 2007 [68]Partially adequateNot ApplicableUnknownIndustry
Rutgeerts, 1999 [69]Partially adequateYesUnknownUnknown
Reinisch, 2012 [70]Partially adequateNot ApplicableUnknownUnknown
Rutgeerts, 2005 [71]AdequateYesYesIndustry
Rutgeerts, 2005 [71]AdequateYesYesIndustry
Regueiro, 2016 [72]Partially adequateNot ApplicableYesIndustry
Sandborn, 2004 [73]AdequateYesYesIndustry
Braun, 2008 [74]AdequateYesYesIndustry
ETN *
Dijkmans, 2009 [75]Partially adequateYesUnknownIndustry
Kloppenburg, 2018 [76]Partially adequateYesUnknownIndustry
Tyring, 2007 [77]Partially adequateYesYesIndustry
Weisman, 2007 [78]Partially adequateYesYesIndustry (Amgen)
GOL *
Bao, 2014 [79]Partially adequateYesNoIndustry
Deodhar, 2015 [80]Partially adequateYesUnknownIndustry
Husni, 2020 [81]Partially adequateYesUnknownUnknown
Takeuchi, 2017 [82]Partially adequateYesUnknownUnknown
Wenzel, 2009 [83]Partially adequateYesUnknownIndustry
Emery, 2013 [84]Partially adequateNot ApplicableUnknownUnknown
Kremer, 2010 [85]Partially adequateYesUnknownIndustry
CTZ *
Mease, 2014 [86]Partially adequateYesUnknownUnknown
Sandborn, 2007 [87]Partially adequateYesUnknownUnknown
Weinblatt, 2015 [88]Partially adequateYesUnknownIndustry
Sandborn, 2011 [89]AdequateYesUnknownIndustry
Loftus Jr, 2016 [90]Partially adequateYesUnknownIndustry
Yamamoto, 2014 [91]Partially adequateYesUnknownIndustry
ADA ^
Klotsche, 2016 [92]Partially adequateNot ApplicableUnknownIndustry
Mercer, 2017 [25]Partially adequateNot ApplicableUnknownGovernment grant
Lunder, 2019 [93]Partially adequateNot ApplicableUnknownIndustry
Viguier, 2012 [94]InadequateNot ApplicableYesUnknown
Mason, 2018 [95]Partially adequateUnknownUnknownUnknown
INX ^
Lunder, 2019 [93]Partially adequateNot ApplicableUnknownIndustry
Mok, 2014 [96]Partially adequateNot ApplicableUnknownUnknown
Nossent, 2001 [97]Partially adequateNot ApplicableUnknownUnknown
Sakai, 2012 [98]Partially adequateNot ApplicableUnknownGovernment grant
Rennard, 2012 [99]Partially adequateNot ApplicableUnknownIndustry
ETN ^
Driessen, 2009 [100]Partially adequateNot ApplicableYesIndustry
Lunder, 2019 [93]Partially adequateNot ApplicableUnknownIndustry
Mason, 2018 [95]Partially adequateUnknownUnknownUnknown
Mok, 2014 [96]Partially adequateNot ApplicableUnknownUnknown
Sakai, 2012 [98]Partially adequateNot ApplicableUnknownGovernment grant
* Interventional study; ^ Observational study.
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Driscoll, C.B.; Rich, J.M.; Isaacson, D.; Nicolas, J.; Jiang, Y.; Mi, X.; Yang, C.; Kocsuta, V.; Goh, R.; Patel, N.; et al. Tumor Necrosis Factor-Alpha Inhibitor Use and Malignancy Risk: A Systematic Review and Patient Level Meta-Analysis. Cancers 2025, 17, 390. https://doi.org/10.3390/cancers17030390

AMA Style

Driscoll CB, Rich JM, Isaacson D, Nicolas J, Jiang Y, Mi X, Yang C, Kocsuta V, Goh R, Patel N, et al. Tumor Necrosis Factor-Alpha Inhibitor Use and Malignancy Risk: A Systematic Review and Patient Level Meta-Analysis. Cancers. 2025; 17(3):390. https://doi.org/10.3390/cancers17030390

Chicago/Turabian Style

Driscoll, Conor B., Jordan M. Rich, Dylan Isaacson, Joseph Nicolas, Yu Jiang, Xinlei Mi, Christopher Yang, Victoria Kocsuta, Regine Goh, Niti Patel, and et al. 2025. "Tumor Necrosis Factor-Alpha Inhibitor Use and Malignancy Risk: A Systematic Review and Patient Level Meta-Analysis" Cancers 17, no. 3: 390. https://doi.org/10.3390/cancers17030390

APA Style

Driscoll, C. B., Rich, J. M., Isaacson, D., Nicolas, J., Jiang, Y., Mi, X., Yang, C., Kocsuta, V., Goh, R., Patel, N., Li, E., Siddiqui, M. R., Meyers, T., Witte, J. S., Kachuri, L., Zhang, H., Beestrum, M., Silberman, P., Schaeffer, E. M., & Kundu, S. D. (2025). Tumor Necrosis Factor-Alpha Inhibitor Use and Malignancy Risk: A Systematic Review and Patient Level Meta-Analysis. Cancers, 17(3), 390. https://doi.org/10.3390/cancers17030390

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