Risk of lymphoma in patients exposed to antitumour necrosis factor therapy: results from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis

Abstract

Objectives:
Patients with rheumatoid arthritis (RA) are at increased risk of lymphoma compared with the general population. There are concerns that tumour necrosis factor inhibitors (TNFi) may exacerbate this risk. However, since the excess risk of lymphoma in RA is related to the cumulative burden of inflammation, TNFi may conversely reduce the risk of lymphoma by decreasing the burden of inflammation. The aim of this study was to compare the risk of lymphoma in subjects with RA treated with TNFi with those treated with non-biological therapy.

Methods:
Subjects diagnosed by a rheumatologist with RA enrolled in the British Society for Rheumatology Rheumatoid Arthritis Register (BSRBR-RA), a prospective cohort study, were followed until first lymphoma, death or until 30 November 2013. Rates of lymphoma in the TNFi and non-biological-treated cohorts were compared using Cox regression.

Results:
11 931 TNFi-treated patients were compared with 3367 biological-naive patients. 84 lymphomas (88 (95% CI 70 to 109) per 100 000 person-years) were reported in the TNFi cohort and 30 lymphomas (154 (95% CI 104 to 220)) in the biological-naive cohort. After adjusting for differences in baseline characteristics, there was no difference in the risk of lymphoma for the TNFi versus the biological-naive group: HR 1.00 (95% CI 0.56 to 1.80). No risk differences were observed for individual TNFi.

Conclusions:
In medium-term follow-up, there is no evidence that tumour necrosis factor inhibition influences the risk of lymphoma over the background risk in subjects with RA.

Full text

EXTENDED REPORT
Risk of lymphoma in patients exposed to antitumour
necrosis factor therapy: results from the British
Society for Rheumatology Biologics Register
for Rheumatoid Arthritis
Louise K Mercer,
1
James B Galloway,
2
Mark Lunt,
1
Rebecca Davies,
1
Audrey L S Low,
1
William G Dixon,
1,3
Kath D Watson,
1
BSRBR Control Centre Consortium
1
Deborah P M Symmons,
1,3
Kimme L Hyrich
1,3
Handling editor Tore K Kvien
▸Additional material is
published online only. To view
please visit the journal online
(http://dx.doi.org/10.1136/
annrheumdis-2016-209389).
1
Arthritis Research UK Centre
for Epidemiology, Centre for
Musculoskeletal Research,
Manchester Academic Health
Science Centre, The University
of Manchester, Manchester, UK
2
Department of Rheumatology,
King’s College London,
London, UK
3
NIHR Manchester
Musculoskeletal Biomedical
Research Unit, Central
Manchester University
Hospitals NHS Foundation
Trust and University of
Manchester Partnership,
Manchester, UK
Correspondence to
Professor Kimme L Hyrich,
Arthritis Research UK Centre
for Epidemiology, The
University of Manchester,
Stopford Building, Oxford
Road, Manchester M13 9PT,
UK; Kimme.hyrich@
manchester.ac.uk
Received 16 February 2016
Revised 22 May 2016
Accepted 19 June 2016
To cite: Mercer LK,
Galloway JB, Lunt M, et al.
Ann Rheum Dis Published
Online First: [please include
Day Month Year]
doi:10.1136/annrheumdis-
2016-209389
ABSTRACT
Objectives Patients with rheumatoid arthritis (RA) are
at increased risk of lymphoma compared with the general
population. There are concerns that tumour necrosis
factor inhibitors (TNFi) may exacerbate this risk. However,
since the excess risk of lymphoma in RA is related to the
cumulative burden of inflammation, TNFi may conversely
reduce the risk of lymphoma by decreasing the burden of
inflammation. The aim of this study was to compare the
risk of lymphoma in subjects with RA treated with TNFi
with those treated with non-biological therapy.
Methods Subjects diagnosed by a rheumatologist with
RA enrolled in the British Society for Rheumatology
Rheumatoid Arthritis Register (BSRBR-RA), a prospective
cohort study, were followed until first lymphoma, death or
until 30 November 2013. Rates of lymphoma in the TNFi
and non-biological-treated cohorts were compared using
Cox regression.
Results 11 931 TNFi-treated patients were compared
with 3367 biological-naive patients. 84 lymphomas (88
(95% CI 70 to 109) per 100 000 person-years) were
reported in the TNFi cohort and 30 lymphomas (154
(95% CI 104 to 220)) in the biological-naive cohort. After
adjusting for differences in baseline characteristics, there
was no difference in the risk of lymphoma for the TNFi
versus the biological-naive group: HR 1.00 (95% CI 0.56
to 1.80). No risk differences were observed for individual
TNFi.
Conclusions In medium-term follow-up, there is no
evidence that tumour necrosis factor inhibition influences
the risk of lymphoma over the background risk in subjects
with RA.
INTRODUCTION
In the late 1990s, the treatment of rheumatoid
arthritis (RA) and other related autoimmune in-
flammatory conditions underwent a fundamental
shift, away from general immunosuppressive agents
towards an approach that targeted specific compo-
nents of the inflammatory pathway. The first treat-
ments in this therapeutic class, known collectively as
biological agents, were inhibitors of tumour necrosis
factor-alpha (TNF-alpha).
1–3
Tumour necrosis
factor (TNF) plays a pivotal role in inflammation in
RA
4
and tumour necrosis factor inhibitors (TNFi)
are highly effective in treating RA.
5
From early in
their development, there were concerns regarding
the long-term safety of the TNFi with respect to
malignancy, and in particular lymphoma.
67
The
possible effects of TNF inhibition on lymphomagen-
esis are difficult to predict. TNF has pleotropic
effects in the promotion and progression of malig-
nancy, with both tumour-promoting and tumour-
inhibiting actions.
8
One of the main indications
for anti-TNF therapies is RA and RA itself has a
long-recognised established increased risk of lymph-
oma compared with the general population,
910
especially the diffuse large B cell lymphoma
(DLBCL).
11 12
Importantly, a large Swedish nested
case–control study reported that patients in the
highest decile of cumulative RA disease activity had
more than a 60-fold increased risk of lymphoma
compared with those in the lowest decile (OR 61.6
(95% CI 21.0 to 181.1)).
13
A previous publication
from the British Society for Rheumatology
Rheumatoid Arthritis Register (BSRBR-RA) demon-
strated that there remains an increased risk of
lymphoma in biological-naïve patients treated with
non-biological therapy compared with the general
population in the modern era of early and aggressive
treatment.
14
There is some evidence that this
increased risk in RA may be exacerbated further by
immunosuppressive therapy.
15
Therefore, given the
strong association between chronic inflammation
and lymphoma development in RA, it is plausible
that TNFi could reduce the risk of lymphoma by
reducing ongoing inflammation. Nonetheless, the
TNFi carry a black box warning with respect to
lymphoma and the US Food and Drug Administration
have highlighted concerns about the risk of hepatos-
plenic T cell lymphoma, a rare and aggressive cancer,
in children and adolescents.
7
Several European biological registers have been
established over the last 10–15 years to examine the
long-term safety of TNFi.
16
One of the earliest and
largest of these, the BSRBR-RA, was established in
2001 with a primary aim to determine the relation-
ship between exposure to TNFi and lymphoma risk.
17
Here, we report the risk of lymphoma development in
patients with RA exposed to TNFi therapy and
compare that with the risk in patients with RA treated
with non-biological (synthetic) disease-modifying
drug (csDMARD) therapy.
Mercer LK, et al.Ann Rheum Dis 2016;0:1–7. doi:10.1136/annrheumdis-2016-209389 1
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METHODS
Patients
Subjects were participants in the BSRBR-RA, an ongoing
national prospective observational cohort study established in
2001 to monitor the long-term safety of biological therapy in
RA. UK national guidelines from the National Institute for
Health and Care Excellence (NICE) recommend that prescrip-
tion of TNFi is restricted to patients with highly active
disease.
18 19
This is defined as a score >5.1 using the 28-joint
Disease Activity Score (DAS28)
20
—a composite score of swollen
and tender joint counts, erythrocyte sedimentation rate and a
patient’s global assessment of disease—despite treatment with at
least two csDMARDs, one of which should be methotrex-
ate.
18 19
During the time period of recruitment of patients
included in this analysis, three TNFi agents were available in the
UK: etanercept (ETA), infliximab (INF) and adalimumab (ADA).
A comparison cohort of biological-naïve patients with RA was
recruited in parallel and followed in an identical manner to the
TNFi cohort.
17
These patients had active disease at recruitment
(target DAS28≥4.2) despite current treatment with csDMARD.
The subjects’written consent was obtained.
BSRBR-RA data collection methods
Baseline data for all patients, collected via rheumatologist/nurse-
completed questionnaire, included demographics, disease dur-
ation, disease activity, current and past csDMARDs, baseline
corticosteroid use, comorbidities and smoking history. Patients
completed a Stanford Health Assessment Questionnaire (HAQ)
adapted for British use
21
to indicate the level of physical disabil-
ity and were asked to select their ethnic group from a list:
white; black-African; black-Caribbean; black-British; black-
other; Indian; Pakistani; Bangladeshi; Chinese or other ( please
specify). All patients were flagged at baseline with the National
Health Service Information Centre or the Northern Ireland
Cancer Registry, who link with the British cancer agencies. The
cancer registries notified the BSRBR-RA of past cancers and pro-
spectively of cancers that occurred after the patient entered the
register. Capture of cancer cases is very high using these
sources, for example, 97% for England cancer statistics pub-
lished for 2009.
22
All patients were continually followed until death or self-
withdrawal, regardless of changes to antirheumatic therapies.
Changes to RA therapy were reported on rheumatologist/nurse
questionnaires completed 6-monthly for 3 years then annually
thereafter. Data on adverse events were captured in three ways:
from rheumatologist/nurse questionnaires; from 6-monthly
patient diaries completed for 3 years on which details of any
new hospital admissions, physician consultations and treatments
were recorded and by flagging with the national death register
and cancer registries which reported malignancies using the
10th edition of the International Classification of Diseases
(ICD-10). Additional information (including histology) was
sought from rheumatologist for all incident lymphomas.
Outcome
The primary outcome measure for this analysis was first verified
lymphoma per subject. Lymphomas were verified if they fulfilled
either of the following criteria: (i) confirmation on histology
report or (ii) reported by a national cancer agency. There were
no lymphomas identified from death certificates alone.
Histology reports and ICD-10 codes reported by the cancer
registries were used to classify lymphomas into subtypes.
Subject selection for the current analysis
Stata (StataCorp., College Station, Texas, USA) V.12.1 was used
for the analyses. Patients were selected from the register if they
had a physician diagnosis of RA and at least one returned
rheumatologist follow-up questionnaire prior to 30 November
2013. Patients with a prior diagnosis of lymphoproliferative or
myeloproliferative malignancy (LPM/MPM) were excluded from
the analysis (figure 1). The TNFi cohort was restricted to
patients who received ETA, INF or ADA as their first biological
therapy and who registered with the BSRBR-RA within
6 months of starting treatment. Patients with prior biological
exposure were excluded. The first 6 months of follow-up time
was excluded from both cohorts, to minimise the risk of in-
cluding prevalent lymphomas in the analysis, and so patients
who did not complete 6 months follow-up were excluded.
Patient-years (pyrs) of follow-up time were calculated from
6 months after the date of starting a TNFi or 6 months after the
date of registration with the BSRBR-RA for the csDMARD
cohort. Follow-up was censored at the date of first lymphoma,
Figure 1 Selection of participants for the analysis. DAS28, 28-joint
Disease Activity Score; LPM, lymphoproliferative malignancy; MPM,
myeloproliferative malignancy; RA, rheumatoid arthritis; TNF, tumour
necrosis factor; TNFi, tumour necrosis factor inhibitors.
2 Mercer LK, et al.Ann Rheum Dis 2016;0:1–7. doi:10.1136/annrheumdis-2016-209389
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death or on 30 November 2013, whichever came first. Patients
in the csDMARD cohort who subsequently started a biological
drug contributed follow-up time up until the date of first dose
biological therapy. Follow-up time after stopping TNFi was
included in the TNFi cohort, irrespective of whether or not the
patient started a second or subsequent biological drug, since it
was hypothesised that the effects of TNFi on lymphoma risk
may be long-lasting.
Statistical analysis
Baseline characteristics of the cohorts were compared using the
χ
2
test for categorical variables and the Wilcoxon rank-sum test
for continuous items and p values were presented. Rates of
lymphoma are presented as total events per 100 000 pyrs with
95% CIs constructed assuming a Poisson distribution of cases.
Risk comparisons were made between the TNFi and csDMARD
cohorts using Cox regression. Potential confounders were iden-
tified a priori and comprised age, sex, smoking status and ethni-
city, since the incidence of lymphoma varies by ethnic group
23
(dichotomised as white or other ethnicity due to the high pro-
portion of white participants); prior cancer (excluding LPM or
MPM); comorbidity and markers of RA severity. The number of
comorbidities was calculated as a composite variable constructed
from the following conditions: hypertension; ischaemic heart
disease; stroke; asthma, bronchitis or emphysema; renal disease;
diabetes mellitus; liver disease and depression. Several baseline
dimensions of RA disease severity were included: DAS28 score;
HAQ score; RA duration; number of previous csDMARDs
(categorised as ≤3or≥4) and current exposure to corticoster-
oids. Date of registration with the BSRBR-RA (dichotomised as
before/after June 2004) was also included to account for
unmeasured confounding relating to temporal changes in the
way that rheumatologists managed patients with RA.
Adjustment for these covariates was performed by calculating a
propensity score (PS) that reflected the likelihood that an individ-
ual patient was prescribed a TNFi, given the above character-
istics. The PS has a single value for each subject that can be
substituted into the regression model in place of all of the poten-
tially confounding covariates. The PS model was derived and
tested using ‘Prop_sel’commands in Stata. The balance of the
model was tested by examining the expected bias, which is the
likely bias in the treatment estimate due to each confounder. A
maximum bias of 2% in either direction was considered accept-
able (see online supplementary figure S1). The PS was stratified
into deciles of propensity score (PD) for use in adjusted analyses.
Subjects with a baseline DAS28 score ≤3.2 (csDMARD: 290;
TNFi: 56) were subsequently excluded from the analysis due to
an area of non-overlap in the PS between cohorts. Missing base-
line data were replaced using multiple imputation (see online sup-
plementary table S1). The assumptions of the Cox models were
tested using Schoenfeld residuals and met.
Drug exposure models
The primary analysis compared the rate of lymphoma in
patients ever treated with TNFi with the csDMARD cohort (ie,
subjects in the TNFi cohort were considered exposed from first
dose until the end of follow-up—ever exposed analysis).
Additional analyses were performed to test the robustness of the
findings. First, patients actively on TNFi (including a lag period
of 90 days from their first missed dose) were compared with the
csDMARD cohort (on-drug analysis). For on-drug analyses,
time after last received consultant follow-up questionnaire was
excluded for all participants since information about current
drug exposure status came from these questionnaires. Second,
the outcome was restricted to lymphomas reported by the
national cancer agencies, in case there was any bias in reporting of
lymphomas by patients and rheumatologists to the BSRBR-RA.
Lymphoma subgroup-specific analyses were performed for sub-
types with ≥5 lymphomas in each cohort as a secondary outcome.
The risk of first lymphoma for each TNFi agent was com-
pared separately with csDMARD. Propensity models for each
TNFi versus csDAMRD were created using the same method
and confounders as above. Three drug exposure models were
used. First, lymphomas were attributed to the most recently
received TNFi. Second, lymphomas were attributed to the first
TNFi agent received and follow-up was censored when a
second biological drug was started and third, an on-drug model.
RESULTS
In total, 3367 csDMARD and 11 931 TNFi patients were
included (figure 1). The TNFi cohort comprised 4144 (35%)
patients starting ETA; INF 3499 (29%) and ADA 4288 (36%).
The TNFi cohort was younger and comprised more women
(table 1). As might be expected, the TNFi cohort had more
severe disease, of longer duration and greater exposure to corti-
costeroids and prior csDMARD. The median follow-up was
6.5 years (IQR 3.8, 8.0) for csDMARD and 8.6 (6.7, 9.7) for
TNFi.
Seven lymphomas were diagnosed in the first 6 months and
not included (csDMARD 3, TNFi 4). One hundred and four-
teen lymphomas were diagnosed during subsequent follow-up
(30 in 19 473 pyrs in the csDMARD and 84 in 95 126 pyrs in
the TNFi cohort (table 2)). The proportion of lymphomas
Table 1 Baseline characteristics
csDMARD
n=3367
All TNFi
n=11 931 p Value
Mean age: years (SD) 60 (12) 56 (12) <0.001
Females: % 2477 (74) 9115 (76) <0.001
Smoking history n (%)
Current smoker 795 (24) 2595 (22) 0.001
Former smoker 1335 (40) 4530 (38)
Never smoked 1222 (36) 4727 (40)
Not recorded 15 (0) 79 (1)
Ethnicity n (%)
White 2561 (76) 9848 (83) <0.001
Other 64 (2) 410 (3)
Not recorded 742 (22) 1673 (14)
Mean DAS28 (SD) 5.3 (1.1) 6.6 (0.9) <0.001
Mean HAQ (SD) 1.5 (0.7) 2.0 (0.6) <0.001
Median disease duration: years (IQR) 6 (1, 15) 11 (6 19) <0.001
Baseline steroid use: n (%) 759 (23) 5259 (44) <0.001
Number of prior csDMARD: median (IQR) 2 (1, 3) 4 (3, 5) <0.001
Comorbidity*: n (%)
None 1397 (41) 5530 (46) <0.001
1 comorbidity 1163 (35) 4098 (34)
2 comorbidities 560 (17) 1691 (14)
≥3 comorbidities 247 (7) 612 (5)
Prior solid cancer: n (%) 122 (4) 170 (1) <0.001
*Hypertension, ischaemic heart disease (myocardial infarction or angina), stroke, lung
disease (asthma, bronchitis or emphysema), diabetes mellitus, depression, renal disease
and liver disease.
csDMARD, non-biological (synthetic) disease-modifying drug; DAS28, 28-joint Disease
Activity Score; HAQ, Health Assessment Questionnaire; TNFi, tumour necrosis factor
inhibitors.
Mercer LK, et al.Ann Rheum Dis 2016;0:1–7. doi:10.1136/annrheumdis-2016-209389 3
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reported by the national cancer registries was 90% in the
csDMARD and TNFi cohorts (table 2). The unadjusted HR for
lymphoma for TNFi compared with csDMARD was 0.61 (95%
CI 0.40 to 0.92) (table 3). Age and male gender were associated
with the risk of lymphoma in univariate analysis (see online sup-
plementary table S2). After fully adjusting, using PD, there was
no difference in the risk of lymphoma for TNFi compared with
csDMARD; HR 1.00 (95% CI 0.56 to 1.80) (table 3). The
PD-adjusted HR for the ‘on TNFi’analysis was 1.17 (95% CI
0.60 to 2.26). Excluding time after starting second biological
drug or limiting the analysis to cancer registry-only reported
lymphomas did not materially alter the findings (table 3). There
were five Hodgkin’s lymphomas (HL; 17%) and 25 non-Hodgkin’s
lymphomas (NHL; 83%) in the csDMARD cohort; and 12 HL
(14%) and 72 NHL (86%) in the TNFi cohort (table 2). The
most frequently reported subtype of NHL was DLBCL (table 2).
No significant differences in the proportion of HL, NHL or
DLBCL were seen between cohorts (table 3). Five T cell lymph-
omas were reported, none of which were the hepatosplenic
subtype.
Thirty-four lymphomas were reported in patients most
recently exposed to ADA, 29 to ETA and 21 to INF. No differ-
ence in the relative risk of lymphoma was seen for any TNFi
compared with csDAMRD using each of the exposure models
(table 4).
DISCUSSION
This national prospective study, with over 120 000 pyrs of
exposure to anti-TNF or csDMARD, did not identify any differ-
ence in lymphoma risk for up to 8 years after the addition of
TNFi to the standard treatment regimen of patients with RA.
This study represents one of the largest and most detailed
analyses of TNFi and lymphoma risk published until now
and mirrors the results of previous cohort studies
24–27
and
meta-analyses.
28–30
The question of whether or not anti-TNF
influences the risk of lymphoma is of particular concern to rheu-
matologists due to the known association between severity of
RA and lymphoma
13
and a signal for increased cancer risk fol-
lowing TNF inhibition in an early meta-analysis.
31
A clinically
meaningful increased risk of lymphoma associated with TNFi
was excluded from this analysis: the analysis had 98% power to
detect a twofold relative increased risk for TNFi compared with
the rate in the csDMARD cohort.
A further strength of the current study was that only incident
users of TNFi as their first biological drug for RA were included.
Furthermore, data on lymphomas in our study were collected
by flagging all participants with the UK cancer agencies that
have near-complete capture of cases, thus minimising potential
for bias in reporting between cohorts. Further information was
requested, including histology reports, for all reported cancers,
ensuring that a standard data set was received for each cancer.
The selection of an ‘ever exposed’to TNFi drug model
reflects the hypothesis that any effect of TNFi on lymphoma
risk would be long-lasting and may operate in the latent period
of a cancer. Alternative exposure models were constructed,
which did not materially change the findings.
Other strengths of this study include careful consideration of
potential biases. For example, the first 6 months of follow-up
were excluded to reduce the probability of prevalent lymphomas
being included. In addition, prevalent users of TNFi at baseline
were excluded. The analysis adjusted for a wider range of con-
founders than previous studies, as confounding by indication is
likely to be present given the way these agents are currently pre-
scribed for RA. Differences in the baseline characteristics of the
cohorts reflect this to some degree. The csDMARD cohort was
older and comprised more men than the TNFi cohort; both risk
factors for cancer, which may explain in part the lower crude risk
of lymphoma in the TNFi cohort. Despite lymphoma being a
relatively uncommon outcome, adjustment for multiple confoun-
ders was possible by using PS methods. Stratifying the PS into
deciles reduced the expected bias in the analysis to less than 5%.
The results of this study align with those of previous analyses
of lymphoma in users of anti-TNF. Wolfe and Michaud
25
used
data from the National Data Bank and reported an OR for
Table 2 Characteristics of lymphomas
csDMARD
N=3367
TNFi
N=11 931
Most recent TNFi
Adalimumab
N=4288
Etanercept
N=4144
Infliximab
N=3499
Total follow-up time (pyrs) 19 473 95 126 33 354 40 619 21 150
Median follow-up per subject (IQR) 6.5 (3.8, 8.0) 8.6 (6.7, 9.7) 6.2 (3.5, 8.0) 5.9 (1.4, 9.1) 8.1 (3.8, 9.2)
Lymphomas 30 84 34 29 21
Sources of reporting of lymphomas
Cancer registry (%) 27 (90) 76 (90) 30 (88) 25 (86) 21 (100)
Subtypes of lymphoma: N (%)
HL 5 (17) 12 (14) 4 (12) 5 (17) 3 (14)
NHL
DLBCL 10 (33) 31 (37) 19 (56) 5 (17) 7 (33)
FL 1 (3) 18 (21) 5 (15) 6 (21) 7 (33)
CLL/small lymphocytic 3 (10) 8 (9) 2 (6) 4 (14) 2 (10)
MALToma 0 4 (5) 1 (3) 3 (10) 0
Mantle cell 3 (10) 0 0 0 0
Burkitt 1 (3) 0 0 0 0
B cell NHL NOS 5 (17) 8 (9) 2 (6) 4 (14) 2 (10)
T cell 2 (7) 3 (4) 1 (3) 2 (7) 0
CLL, chronic lymphocytic leukaemia; DLBCL, diffuse large B cell lymphoma; FL, follicular lymphoma; HL, Hodgkin’s lymphoma; MALToma, mucosal-associated lymphoid tissue lymphoma;
NHL, non-Hodgkin’s lymphoma; NOS, not otherwise specified; pyrs, patient-years; TNFi, tumour necrosis factor inhibitors.
4 Mercer LK, et al.Ann Rheum Dis 2016;0:1–7. doi:10.1136/annrheumdis-2016-209389
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exposure to TNFi of 1.0 (95% CI 0.6 to 1.8). However, this
study included prevalent users of TNFi, leading to possible left
censorship, unlike our study. The Swedish Biologics Register
(Anti-Rheumatic Therapies in Sweden; ARTIS) reported a rela-
tive risk of 1.35 (95% CI 0.82 to 2.11) in their most recent
publication.
24
However, despite having more than 350 000 pyrs
of follow-up in their csDMARD comparator cohort, they lacked
precision in their estimate of drug effect since they observed
only 26 lymphomas in 30 000 pyrs in the TNFi cohort.
The French Research Axed on Tolerance of bIOtherapies
(RATIO) registry reported that exposure to ADA or INF versus
ETA was a risk factor for lymphoma over a 3-year period (OR
4.7 (1.3 to 17.7) and 4.1 (1.4 to 12.5), respectively).
32
Conversely, this current study did not observe a difference in
the risk of lymphoma for any individual TNFi versus
csDMARD. Direct comparisons were not made in this study
since more than 50% of the cohort were exposed to multiple
TNFi during follow-up. Such patients who switch drugs, com-
monly for treatment inefficacy, may have a different underlying
risk of lymphoma.
The most frequently reported subtype in this study was
DLBCL, with follicular lymphoma being the next most frequent
subtype in the TNFi cohort, in line with previous studies of
biological-naive cohorts.
11 13
The distribution of lymphoma sub-
types varies by sex and age at diagnosis and so the difference in
age between the TNFi and csDMARD cohorts may have led to a
difference in the expected distribution of subtypes between
groups. The proportion of follicular lymphomas was lower in the
csDMARD cohort than expected but the numbers of individual
subtypes were low, highlighting the fact that this study was not
powered to study at the relative risk of individual subtypes.
Reporting of lymphoma subtypes was based on external histology
reports rather than rereview of lymphoma specimens and mis-
classification between lymphoma subtypes may have occurred.
A weakness of our study was that it was not possible to adjust
for, or explore the influence of, cumulative RA disease activity.
However, HAQ, a marker of cumulative damage, baseline
DAS28 and RA duration were included in the PS model.
Missing data can adversely affect all studies, particularly
Table 3 Association between exposure to TNFi and lymphoma
csDMARD
N=3367
TNFi
N=11 931
Total follow-up time (pyrs) 19 473 95 126
Lymphomas 30 84
Incidence rate per 100 000 pyrs (95% CI) 154 (104 to
220)
88 (70 to 109)
Unadjusted HR (95% CI) Referent 0.61 (0.40 to 0.92)
Age-adjusted and sex-adjusted HR (95% CI) Referent 0.75 (0.49 to 1.15)
PD-adjusted HR (95% CI) Referent 1.00 (0.56 to 1.80)
On TNFi (plus 90 days)*
Follow-up time ( pyrs) 15 167 57 949
Lymphomas 25 63
PD-adjusted HR (95% CI) Referent 1.17 (0.60 to 2.26)
Excluded time after switched to second biological drug*
Follow-up time ( pyrs) 15 167 55 167
Lymphomas 25 52
PD-adjusted HR (95% CI) Referent 1.12 (0.58 to 2.18)
Cancer registry-only reported lymphomas
Follow-up time ( pyrs) 19 473 95 126
Lymphomas 27 76
PD-adjusted HR (95% CI) Referent 1.02 (0.55 to 1.90)
Hodgkin’s lymphomas (HL)
Incidence rate of HL per 100 000 pyrs
(95% CI)
26 (8 to 60) 13 (7 to 22)
PD-adjusted HR for HL (95% CI) Referent 0.54 (0.12 to 2.50)
Non-Hodgkin’s lymphomas (NHL)
Incidence rate of NHL per 100 000 pyrs
(95% CI)
128 (83 to
190)
75 (58 to 94
PD-adjusted HR for NHL (95% CI) Referent 1.10 (0.58 to 2.08)
DLBCL
Incidence rate of DLBCL per 100 000 pyrs
(95% CI)
67 (36 to
114)
56 (42 to 73)
PD-adjusted HR for DLBCL (95% CI) Referent 1.54 (0.60 to 3.95)
*Time after last received consultant follow-up form excluded from this analysis.
DLBCL, diffuse large B cell lymphoma; pyrs, patient-years; TNFi, tumour necrosis factor
inhibitors.
Table 4 Association between exposure to adalimumab (ADA), etanercept (ETA) or infliximab (INF) and lymphoma
ADAN=4288
ETA
N=4144
INF
N=3499
First TNFi received (censored when second biological drug started)*
Total follow-up time (pyrs) 22 361 26 838 17 688
Number of lymphomas 20 20 18
Incidence rate per 100 000 pyrs (95% CI) 89 (55 to 138) 75 (45 to 115) 102 (60 to 161)
PD-adjusted HR (95% CI) (csDMARD referent) 1.00 (0.49 to 2.03) 1.02 (0.45 to 2.33) 0.91 (0.39 to 2.13)
Most recently received TNFi
Follow-up time ( pyrs) 33 354 40 618 21 149
Number of lymphomas 34 29 21
Incidence rate per 100 000 pyrs (95% CI) 102 (71 to 143) 71 (48 to 103) 99 (62 to 152)
PD-adjusted HR (95% CI) (csDMARD referent) 0.99 (0.52 to 1.88) 0.78 (0.37 to 1.66) 0.82 (0.37 to 1.82)
On drug (plus 90 days)*†
Follow-up time ( pyrs) 18 818 24 984 12 328
Number of lymphomas 23 10 10
Incidence rate per 100 000 pyrs (95% CI) 122 (77 to 183) 40 (19 to 74) 81 (39 to 149)
PD-adjusted HR (95% CI) (csDMARD referent) 0.77 (0.37 to 1.61) 0.41 (0.14 to 1.19) 0.75 (0.27 to 2.09)
*Time after last received consultant follow-up form excluded from these analyses.
†Includes both first and subsequent exposures to the drug.
pyrs, patient-years; TNFi, tumour necrosis factor inhibitors.
Mercer LK, et al.Ann Rheum Dis 2016;0:1–7. doi:10.1136/annrheumdis-2016-209389 5
Clinical and epidemiological research
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observational studies. Overall, the proportion of missing base-
line data was low in the BSRBR-RA (see online supplementary
material). Response rates to follow-up questionnaires were
excellent; less than 1% of patients in each cohort had no
returned consultant follow-up. To minimise bias introduced by
missing baseline data, multiple imputation was used.
In conclusion, this study has ruled out an important risk of
lymphoma in patients with RA exposed to TNFi over the back-
ground risk associated with RA for up to 5 years after treatment
initiation. This is consistent with other published data and the
biological expectation that disease activity is the primary driver
for lymphoma in RA. Further follow-up of significantly larger
populations is now needed to determine whether longer-term
exposure or cumulative drug exposure influences risk, given the
overall low absolute risk of lymphoma in TNFi-treated patients.
Twitter Follow William Dixon at @WGDixon
Collaborators BSRBR Control Centre Consortium: The BSRBR Control Centre
Consortium consists of the following institutions (all in the UK): Antrim Area
Hospital, Antrim (Dr Nicola Maiden), Cannock Chase Hospital, Cannock Chase
(Dr Tom Price), Christchurch Hospital, Christchurch (Dr Neil Hopkinson), Royal Derby
Hospital, Derby (Dr Sheila O’Reilly), Dewsbury and District Hospital, Dewsbury
(Dr Lesley Hordon), Freeman Hospital, Newcastle-upon-Tyne (Dr Ian Griffiths),
Gartnavel General Hospital, Glasgow (Dr Duncan Porter), Glasgow Royal Infirmary,
Glasgow (Dr R Madhok), Haywood Hospital, Stoke-on-Trent (Dr Andy Hassell), Hope
Hospital, Salford (Dr R G Cooper), King’s College Hospital, London (Dr Ernest Choy),
Kings Mill Centre, Sutton-In Ashfield (Dr David Walsh), Chapel Allerton Hospital,
Leeds (Professor Paul Emery), Macclesfield District General Hospital, Macclesfield
(Dr Susan Knight), Manchester Royal Infirmary, Manchester (Dr Ian Bruce), Musgrave
Park Hospital, Belfast (Dr Allister Taggart), Norfolk and Norwich University Hospital,
Norwich (Professor David Scott), Poole Hospital, Poole (Dr Paul Thompson), Queen
Alexandra Hospital, Portsmouth (Dr Fiona McCrae), Royal Glamorgan Hospital,
Glamorgan (Dr Rhian Goodfellow), Russells Hall Hospital, Dudley (Professor George
Kitas), Selly Oak Hospital, Selly Oak (Dr Ronald Jubb), St Helens Hospital, St Helens
(Dr Rikki Abernethy), Weston General Hospital, Weston-super-Mare (Dr Sandra
Green), University Hospital of South Manchester/Withington Community Hospital,
(Dr Paul Sanders), Withybush General Hospital, Haverfordwest (Dr Amanda Coulson),
North Manchester General Hospital (Dr Bev Harrison), Royal Lancaster Infirmary
(Dr Marwan Bukhari) and The Royal Oldham Hospital (Dr Peter Klimiuk).
Contributors Study concept and design: All. Acquisition of data: BSRBR Control
Centre Consortium. Analysis and interpretation of data: All. Statistical analysis:
LKM, ML, RD, KLH. Drafting of manuscript: LKM, DPMS, KLH. Critical revision of
manuscript for important intellectual content: All. LKM and KLH had full access to
all the data in the study and take responsibility for the integrity of the data and the
accuracy of the data analysis.
Funding The BSR commissioned the BSRBR-RA as a UK-wide national project to
investigate the safety of biological agents in routine medical practice. BSR receives
restricted income from UK pharmaceutical companies, presently AbbVie (previously
Abbott Laboratories), Merck, Pfizer, Roche, Union Chimique Belge Pharma Ltd (UCB)
and Swedish Orphan Biovitrum AB (SOBI). This income finances a wholly separate
contract between the BSR and the University of Manchester who provide and oversee
the BSRBR-RA data collection, management and analysis service. The principal
investigators and their team have full academic freedom and are able to work
independently of pharmaceutical industry influence. All decisions concerning analyses,
interpretation and publication are made autonomously of any industrial contribution.
LM was funded by the Medical Research Council, UK (Grant Number G0701380/1).
Competing interests KH has received honoraria from AbbVie and Pfizer.
Ethics approval UK North-West Multicentre research ethics committee.
Provenance and peer review Not commissioned; externally peer reviewed.
Open Access This is an Open Access article distributed in accordance with the
terms of the Creative Commons Attribution (CC BY 4.0) license, which permits
others to distribute, remix, adapt and build upon this work, for commercial use,
provided the original work is properly cited. See: http://creativecommons.org/licenses/
by/4.0/
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Mercer LK, et al.Ann Rheum Dis 2016;0:1–7. doi:10.1136/annrheumdis-2016-209389 7
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Biologics Register for Rheumatoid Arthritis
from the British Society for Rheumatology
antitumour necrosis factor therapy: results
Risk of lymphoma in patients exposed to
Consortium, Deborah P M Symmons and Kimme L Hyrich
L S Low, William G Dixon, Kath D Watson, BSRBR Control Centre
Louise K Mercer, James B Galloway, Mark Lunt, Rebecca Davies, Audrey
published online August 8, 2016Ann Rheum Dis
89
http://ard.bmj.com/content/early/2016/08/08/annrheumdis-2016-2093
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