Abstract
Background
The etiology of mantle cell lymphoma (MCL), a distinctive subtype accounting for 2%–10% of all non-Hodgkin lymphoma, is not known.
Methods
We investigated associations with self-reported medical history, lifestyle, family history, and occupational risk factors in a pooled analysis of 557 patients with MCL and 13766 controls from 13 case–control studies in Europe, North America, and Australia. Odds ratios (ORs) and 95% confidence intervals (CIs) associated with each exposure were examined using multivariate logistic regression models.
Results
The median age of the MCL patients was 62 years and 76% were men. Risk of MCL was inversely associated with history of hay fever (OR = 0.63, 95% CI = 0.48 to 0.82), and the association was independent of other atopic diseases and allergies. A hematological malignancy among first-degree relatives was associated with a twofold increased risk of MCL (OR = 1.99, 95% CI = 1.39 to 2.84), which was stronger in men (OR = 2.21, 95% CI = 1.44 to 3.38) than women (OR = 1.61, 95% CI = 0.82 to 3.19). A modestly increased risk of MCL was also observed in association with ever having lived on a farm (OR = 1.40, 95% CI = 1.03 to 1.90). Unlike some other non-Hodgkin lymphoma subtypes, MCL risk was not statistically significantly associated with autoimmune disorders, tobacco smoking, alcohol intake, body mass index, or ultraviolet radiation.
Conclusions
The novel observations of a possible role for atopy and allergy and farm life in risk of MCL, together with confirmatory evidence of a familial link, suggest a multifactorial etiology of immune-related environmental exposures and genetic susceptibility. These findings provide guidance for future research in MCL etiology.
Mantle cell lymphoma (MCL) is one of the more recently identified subtypes of non-Hodgkin lymphoma (NHL), formally recognized in 1992 (1) and subsequently adopted into the Revised European-American Lymphoma (REAL) (2) and World Health Organization (WHO) classifications (3,4). MCL accounts for between 2% and 10% of all NHL (5,6). In part due to its recent recognition and uncommon occurrence, epidemiological studies of MCL are few and no specific causes have been confirmed (7,8). MCL is typically diagnosed in those aged 60 years or older, more than twice as often among men than women, and more often among whites than blacks or Asians (9–12). Unlike most NHL subtypes, patients with MCL respond poorly to traditional chemotherapy regimens and the 5-year survival is less than 50% (13). An increasing incidence of MCL over time has been reported in the United States (9,11,14), which could reflect changes in diagnostic practice and the introduction of immunohistochemical staining for cyclin D1 (9) and/or a true increase.
Biologically and clinically distinct features of MCL that may harbor clues to its etiology include a restricted Ig repertoire of the tumor cells and somatic hypermutation (15–17), as well as the frequent involvement of extranodal tissues such as the gastrointestinal (GI) tract (18). Moderate associations with MCL risk have been reported for certain autoimmune disorders (19), family history of hematopoietic malignancy, specifically male relatives (20), and Borrelia burgdorferi infection (21). However, the number of MCL patients has mostly been low (<150) in previous etiologic investigations, and these findings remain unconfirmed.
To advance our understanding of the etiology of MCL, we investigated associations with medical history, lifestyle, family history, and occupational risk factors in a pooled analysis of 557 cases and 13766 controls from 13 case–control studies from Europe, North America, and Australia as part of the International Lymphoma Epidemiology Consortium (InterLymph) NHL Subtypes Project.
Materials and Methods
Study Population
Detailed methodology for the InterLymph NHL Subtypes Project is provided elsewhere in this issue. Studies eligible for inclusion in this pooled analysis fulfilled the following criteria: 1) case– control design, with incident, histologically confirmed cases of MCL according to the WHO classification; and 2) availability of individual-level data for several risk factors of interest by December 31, 2011. Thirteen studies contributed in total 557 MCL patients and 13766 controls diagnosed and identified from 1995 to 2008. Seven studies were conducted in North America, five in Europe and one in Australia, and eight were population-based, four were hospital- or clinic-based, and one was partly population-based and partly hospital-based. Most studies excluded individuals with a known history of solid organ transplantation or HIV/AIDS. Contributing studies were approved by local ethics review committees, and all participants provided informed consent before interview.
MCL Subtype Ascertainment and Harmonization
Cases were classified according to the WHO classification (3,4) using guidelines from the InterLymph Pathology Working Group (22,23). Most studies had some form of centralized pathology review by at least one expert hematopathologist. Each participating study’s pathology review procedures and rules for MCL classification were reviewed for consistency and comparability by an interdisciplinary team of pathologists and epidemiologists.
Because of the potential for risk factors to differ by the primary site of disease, we further classified the cases according to primary site. Lymphoma sites were categorized as nodal or extranodal (24), and a primary site in the GI tract was specifically noted. Cases with widespread disease, no known primary site, or the primary site listed as bone marrow, blood, or cerebrospinal fluid, were classified as systemic.
Risk Factor Ascertainment and Harmonization
Each study collected data on putative risk factors using a standardized, structured format by in-person or telephone interviews and/or self-administered questionnaires. Risk factors selected for inclusion were the available medical history, lifestyle, family history, and occupational risk factors with data from at least three studies. Centralized harmonization of individual-level, deidentified data from each study was a key element of the project. Details of the data harmonization rules as well as the number of studies that contributed data for each exposure are provided elsewhere in this issue.
Statistical Analysis
Risk of MCL associated with each exposure variable was examined using unconditional logistic regression models adjusted for age at diagnosis or interview, race/ethnicity, sex, and study (basic model). The significance of each association was evaluated by a likelihood ratio test, comparing models with and without the exposure variable of interest, with P values less than .05 identifying putatively influential factors. Individuals with missing data for the exposure variable of interest were excluded. To evaluate heterogeneity of effect among the 13 studies, we performed a separate logistic regression within each study and then quantified the variability of the coefficients by the H statistic (25).
We then examined the relationship between case–control status and each putative risk factor considering possible effect modification and accounting for other potential confounders. To consider effect modification, we repeated the above logistic regression analyses stratified by age, sex, geographical region, study, and study design (ie, population-based vs hospital or clinic-based). To account for other potential confounders, we conducted two analyses. First, we evaluated the risk estimate for each putative risk factor in a series of basic models that adjusted for one other putative risk factor individually. Second, we conducted a single logistic regression analysis including all putative risk factors, including a separate missing category for each variable to ensure that the whole study population was included in the analysis (ie, not dropped due to missing data). Finally, we conducted a forward step-wise logistic regression with all putative risk factors. Results from these multivariate analyses were generally similar to the results adjusted for age, sex, race/ethnicity, and study only, and thus are not presented.
Because controls for most original studies were chosen to frequency match to the age and sex of all original study cases, rather than just MCL, we conducted sensitivity analyses using a subset of controls that were matched by age and sex to the cases with MCL. The results from these sensitivity analyses were very similar to the results obtained using the full set of controls and, thus, we retained the full set of controls for our main analyses to increase statistical power. Analyses were conducted using SAS software, version 9.2 (SAS Institute Inc, Cary, NC).
Results
The median age of the patients was 62 years (range 22–88 years), 94% were white (N = 523), and 74% (N = 412) were men (Table 1). Controls tended to be slightly younger (median 59 years, range 16–98 years) and were less likely to be male (52%) than cases. The case participants were diagnosed between 1995 and 2008 in North America (45%), Northern Europe (47%), Southern Europe (3%), or Australia (4%). Most participants were included in population-based studies (78% of the cases and 70% of the controls). Among patients with information available on stage and sites of disease, 85% (N = 351/413) were diagnosed with stage III/IV disease and 74% (N = 320/432) had either nodal or systemic involvement, whereas 7% (N = 30/432) had primary disease in the GI tract.
Table 1.
Characteristics of 557 mantle cell lymphoma cases and 13766 controls included in the InterLymph NHL Subtypes Project
| Controls, No. (%) | Cases, No. (%) | |
|---|---|---|
| Total | 13766 (96.1) | 557 (3.9) |
| Age, y | ||
| <30 | 769 (5.6) | 1 (0.2) |
| 30–39 | 1133 (8.2) | 14 (2.5) |
| 40–49 | 1974 (14.3) | 44 (7.9) |
| 50–59 | 3323 (24.1) | 163 (29.3) |
| 60–69 | 3835 (27.9) | 203 (36.4) |
| 70–79 | 2474 (18.0) | 118 (21.2) |
| ≥80 | 258 (1.9) | 11 (2.0) |
| Missing | 0 (0.0) | 3 (0.5) |
| Sex | ||
| Male | 7206 (52.3) | 412 (74.0) |
| Female | 6560 (47.7) | 145 (26.0) |
| Race/ethnicity | ||
| White, non-Hispanic | 12854 (93.4) | 523 (93.9) |
| Black | 199 (1.4) | 3 (0.5) |
| Asian | 189 (1.4) | 10 (1.8) |
| Hispanic | 121 (0.9) | 6 (1.1) |
| Other/unknown/missing | 403 (2.9) | 15 (2.7) |
| Socioeconomic status | ||
| Low | 4771 (34.7) | 208 (37.3) |
| Medium | 4532 (32.9) | 168 (30.2) |
| High | 4216 (30.6) | 166 (29.8) |
| Other/missing | 247 (1.8) | 15 (2.7) |
| Region | ||
| North America | 5060 (36.8) | 253 (45.4) |
| Northern Europe* | 6542 (47.5) | 264 (47.4) |
| Southern Europe† | 1470 (10.7) | 18 (3.2) |
| Australia | 694 (5.0) | 22 (3.9) |
| Design | ||
| Population-based | 9673 (70.3) | 435 (78.1) |
| Hospital-based | 4093 (29.7) | 122 (21.9) |
| Lymphoma stage at diagnosis | ||
| N/A (Controls) | 23096 (100.0) | 0 (0.0) |
| Stages I–II | — | 62 (11.1) |
| Stages III–IV | — | 351 (63.2) |
| Unknown/unclassifiable | — | 143 (25.7) |
* Includes Sweden, Denmark, Germany, United Kingdom, Ireland, France, and Czech Republic.
† Includes Spain and Italy.
A history of any atopic condition was inversely associated with risk of MCL (odds ratio [OR] = 0.74, 95% confidence interval [CI] = 0.61 to 0.89) (Table 2). Furthermore, any specific allergy (against insects, plants, etc.) was inversely associated with risk of MCL (OR = 0.79, 95% CI = 0.63 to 0.98), as was hay fever (OR = 0.63, 95% CI = 0.48 to 0.82). Allergy was correlated with atopy (phi coefficient = 0.72, P < .0001) and with hay fever (phi coefficient = 0.45, P < .0001). However, the significant inverse association for hay fever remained for individuals with or without a history of other atopic disorders (OR = 0.66, 95% CI = 0.49 to 0.89; OR = 0.55, 95% CI = 0.33 to 0.93, respectively). In contrast, a history of allergy, asthma, eczema, or food allergies per se were inversely associated with MCL risk only in combination with a history of at least one other atopic disorder (Table 2). In multivariate analyses mutually adjusted for concomitant atopic disorders, hay fever, but not any allergy, remained inversely associated with MCL (OR = 0.67, 95% CI = 0.49 to 0.93). The reduced risk of MCL associated with hay fever was observed in both sexes (males: OR = 0.67, 95% CI = 0.49 to 0.92; females: OR = 0.54, 95% CI = 0.32 to 0.90).
Table 2.
Risk of mantle cell lymphoma associated with a history of selected atopic and autoimmune disorders*
| Controls, No. (%)† | Cases, No. (%)† | OR (95% CI) | P | |
|---|---|---|---|---|
| Atopic disorders | ||||
| Any atopic disorder‡ | ||||
| No | 8201 (59.6) | 366 (65.7) | 1.00 (referent) | .001 |
| Yes | 5336 (38.8) | 174 (31.2) | 0.74 (0.61 to 0.89) | |
| Allergy§ | ||||
| No | 8227 (66.3) | 369 (69.4) | 1.00 (referent) | .031 |
| Yes | 3312 (26.7) | 117 (22.0) | 0.79 (0.63 to 0.98) | |
| Allergy and other atopic conditions | ||||
| No | 8227 (66.3) | 369 (69.4) | 1.00 (referent) | .010 |
| Allergy but no other atopic conditions | 1065 (8.6) | 44 (8.3) | 1.05 (0.75 to 1.45) | |
| Allergy and asthma, hay fever, or eczema | 2247 (18.1) | 73 (13.7) | 0.68 (0.52 to 0.88) | |
| Asthma | ||||
| No | 10939 (80.3) | 436 (79.3) | 1.00 (referent) | .122 |
| Yes | 1197 (8.8) | 36 (6.5) | 0.77 (0.54 to 1.09) | |
| Asthma and other atopic conditions | ||||
| No | 10939 (80.3) | 436 (79.3) | 1.00 (referent) | .225 |
| Asthma but no other atopic conditions | 435 (3.2) | 15 (2.7) | 0.90 (0.53 to 1.54) | |
| Asthma and allergy, hay fever, or eczema | 762 (5.6) | 21 (3.8) | 0.69 (0.44 to 1.08) | |
| Hay fever | ||||
| No | 8157 (62.2) | 351 (64.1) | 1.00 (referent) | <.001 |
| Yes | 2727 (20.8) | 80 (14.6) | 0.63 (0.48 to 0.82) | |
| Hay fever and other atopic conditions | ||||
| No | 8157 (62.2) | 351 (64.1) | 1.00 (referent) | .002 |
| Hay fever but no other atopic conditions | 700 (5.3) | 16 (2.9) | 0.55 (0.33 to 0.93) | |
| Hay fever and asthma, allergy, or eczema | 2027 (15.4) | 64 (11.7) | 0.66 (0.49 to 0.89) | |
| Eczema | ||||
| No | 11072 (83.5) | 478 (86.1) | 1.00 (referent) | .304 |
| Yes | 1437 (10.8) | 43 (7.7) | 0.85 (0.61 to 1.17) | |
| Eczema and other atopic conditions | ||||
| No | 11072 (83.5) | 478 (86.1) | 1.00 (referent) | .567 |
| Eczema but no other atopic conditions | 528 (4.0) | 16 (2.9) | 0.90 (0.54 to 1.50) | |
| Eczema and allergy, hay fever, or eczema | 909 (6.9) | 27 (4.9) | 0.82 (0.55 to 1.23) | |
| Food allergy | ||||
| No | 9988 (80.5) | 396 (74.4) | 1.00 (referent) | .875 |
| Yes | 997 (8.0) | 33 (6.2) | 0.97 (0.67 to 1.41) | |
| Food allergy and other atopic conditions | ||||
| No | 9988 (80.5) | 396 (74.4) | 1.00 (referent) | .460 |
| Food allergy but no other atopic conditions | 403 (3.2) | 17 (3.2) | 1.23 (0.74 to 2.04) | |
| Food allergy and asthma, hay fever, or eczema | 594 (4.8) | 16 (3.0) | 0.79 (0.47 to 1.33) | |
| Autoimmune disorders | ||||
| History of autoimmune disease|| | ||||
| No autoimmune disease | 13158 (95.6) | 531 (95.3) | 1.00 (referent) | .846 |
| B-cell activation | 125 (0.9) | 5 (0.9) | 1.11 (0.45 to 2.76) | |
| T-cell activation | 471 (3.4) | 21 (3.8) | 1.03 (0.65 to 1.62) | |
| Both | 12 (0.1) | 0 (0.0) | ||
| Systemic lupus erythematosus | ||||
| No | 11065 (98.1) | 452 (97.2) | 1.00 (referent) | .199 |
| Yes | 25 (0.2) | 2 (0.4) | 3.05 (0.69 to 13.42) | |
| Rheumatoid arthritis | ||||
| No | 6368 (89.4) | 297 (90.3) | 1.00 (referent) | .363 |
| Yes | 82 (1.2) | 2 (0.6) | 0.55 (0.13 to 2.27) | |
| Type I diabetes | ||||
| No | 8658 (94.0) | 319 (94.1) | 1.00 (referent) | .617 |
| Yes | 63 (0.7) | 2 (0.6) | 0.70 (0.16 to 3.04) | |
| Psoriasis | ||||
| No | 7333 (96.6) | 245 (95.0) | 1.00 (referent) | .978 |
| Yes | 228 (3.0) | 9 (3.5) | 0.99 (0.50 to 1.97) | |
| Crohn’s disease | ||||
| No | 9924 (97.8) | 444 (96.9) | 1.00 (referent) | .799 |
| Yes | 31 (0.3) | 2 (0.4) | 1.22 (0.28 to 5.31) | |
| Ulcerative colitis | ||||
| No | 7777 (96.5) | 365 (94.6) | 1.00 (referent) | .116 |
| Yes | 81 (1.0) | 8 (2.1) | 1.93 (0.90 to 4.12) | |
* Odds ratio (OR) and 95% confidence interval (CI) adjusted for age, sex, race, and study. Physician-diagnosed autoimmune diseases with at least two exposed cases are presented.
† The counts do not add up to the total number of cases and controls due to data missing by design or report.
‡ Atopic disorders include asthma, eczema, hay fever, or other allergies, excluding drug allergies.
§ History of allergy excluded drug allergies, asthma, eczema, and hay fever.
|| Autoimmune diseases associated with B-cell activation include rheumatoid arthritis, Sjögren syndrome, systemic lupus erythematosus, pernicious anemia, hemolytic anemia, and myasthenia gravis, whereas autoimmune diseases causing T-cell activation include psoriasis, celiac disease, inflammatory bowel disorders, sarcoidosis, multiple sclerosis, polymyositis or dermatomyositis, systemic sclerosis or scleroderma, and type I diabetes.
Few MCL cases reported a history of any physician-diagnosed autoimmune disorder (Table 2). The most frequently reported autoimmune disorders were psoriasis (3.5%, N = 9) and ulcerative colitis (2.1%, N = 8). Perhaps in part as a result of this low frequency, we did not observe statistically significant risks of MCL with any specific autoimmune disorders, including systemic lupus erythematosus, rheumatoid arthritis, type 1 diabetes, Crohn’s disease, ulcerative colitis, or psoriasis, or with autoimmune disorders grouped into B-cell or T-cell–activating diseases (Table 2).
A hematological malignancy among one or more first-degree relatives was associated with a twofold increased risk of MCL (OR = 1.99, 95% CI = 1.39 to 2.84, N = 39) (Table 3). The risk was more pronounced among men (OR = 2.21, 95% CI = 1.44 to 3.38) than women (OR = 1.61, 95% CI = 0.82 to 3.19), and if the relative was male (OR = 2.33, 95% CI = 1.46 to 3.72) rather than female (OR = 1.69, 95% CI = 1.00 to 2.84). The greatest difference in risk estimates were where the patient and the relative were male (OR = 2.48, 95% CI = 1.41 to 4.34) as opposed to both being female (OR = 1.37, 95% CI = 0.49 to 3.84), although none of these differences was statistically significant (P heterogeneity = .41 by sex of the patient; P heterogeneity = .12 by sex of the relative; P heterogeneity= 0.28 by sex of the patient and relative). In an analysis of type of hematological malignancy (restricted to subtypes reported by 10 or more cases), risk of MCL was increased approximately twofold both for those with a family history of NHL or of leukemia.
Table 3.
Risk of mantle cell lymphoma associated with a history of hematological malignancy in a first-degree relative, overall and by sex of the patient and the relative*
| Total | P | Females | P | Males | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| First-degree family history | Controls†, No. (%) | Cases†, No. (%) | OR (95% CI) | Controls†, No. (%) | Cases†, No. (%) | OR (95% CI) | Controls†, No. (%) | Cases†, No. (%) | OR (95% CI) | P | ||
| Any hematologic malignancy | ||||||||||||
| No | 8519 (86.2) | 298 (77.0) | 1.00 (referent) | <.001 | 4039 (84.8) | 84 (79.2) | 1.00 (referent) | .194 | 4081 (86.2) | 214 (76.2) | 1.00 (referent) | <.001 |
| Yes | 492 (5.0) | 39 (10.1) | 1.99 (1.39 to 2.84) | 267 (5.6) | 10 (9.4) | 1.61 (0.82 to 3.19) | 216 (4.6) | 29 (10.3) | 2.21 (1.44 to 3.38) | |||
| Any hematologic malignancy, male relative | ||||||||||||
| No | 7940 (88.0) | 283 (79.7) | 1.00 (referent) | .001 | 3907 (87.1) | 84 (82.4) | 1.00 (referent) | .164 | 3696 (88.1) | 199 (78.7) | 1.00 (referent) | .004 |
| Yes | 230 (2.5) | 22 (6.2) | 2.33 (1.46 to 3.72) | 122 (2.7) | 6 (5.9) | 1.96 (0.82 to 4.66) | 104 (2.5) | 16 (6.3) | 2.48 (1.41 to 4.34) | |||
| Any hematologic malignancy, female relative | ||||||||||||
| No | 7936 (87.9) | 288 (81.1) | 1.00 (referent) | .064 | 3903 (87.0) | 86 (84.3) | 1.00 (referent) | .571 | 3695 (88.1) | 202 (79.8) | 1.00 (referent) | .051 |
| Yes | 234 (2.6) | 17 (4.8) | 1.69 (1.00 to 2.84) | 126 (2.8) | 4 (3.9) | 1.37 (0.49 to 3.84) | 105 (2.5) | 13 (5.1) | 1.92 (1.04 to 3.53) | |||
| NHL† | ||||||||||||
| No | 8085 (88.2) | 296 (81.8) | 1.00 (referent) | .024 | 3913 (87.2) | 88 (86.3) | 1.00 (referent) | .65 | 3767 (88.5) | 208 (80.0) | 1.00 (referent) | .005 |
| Yes | 205 (2.2) | 16 (4.4) | 1.95 (1.14 to 3.34) | 116 (2.6) | 2 (2.0) | 0.73 (0.18 to 3.04) | 86 (2.0) | 14 (5.4) | 2.58 (1.41 to 4.71) | |||
| NHL, male relative | ||||||||||||
| No | 8077 (89.5) | 295 (83.1) | 1.00 (referent) | .009 | 3977 (88.6) | 88 (86.3) | 1.00 (referent) | .536 | 3137 (87.9) | 176 (79.3) | 1.00 (referent) | .013 |
| Yes | 93 (1.0) | 10 (2.8) | 2.78 (1.40 to 5.52) | 52 (1.2) | 2 (2.0) | 1.64 (0.38 to 7.01) | 40 (1.1) | 8 (3.6) | 3.11 (1.40 to 6.93) | |||
| NHL, female relative | ||||||||||||
| No | 8061 (89.3) | 301 (84.8) | 1.00 (referent) | .775 | 3966 (88.4) | 90 (88.2) | 1.00 (referent) | .09 | 3754 (89.5) | 211 (83.4) | 1.00 (referent) | .607 |
| Yes | 109 (1.2) | 4 (1.1) | 0.87 (0.31 to 2.39) | 63 (1.4) | 0 (0.0) | — | 46 (1.1) | 4 (1.6) | 1.33 (0.47 to 3.81) | |||
| Leukemia†,‡ | ||||||||||||
| No | 7930 (87.9) | 286 (80.6) | 1.00 (referent) | .012 | 3903 (87.0) | 84 (82.4) | 1.00 (referent) | .116 | 3692 (88.0) | 202 (79.8) | 1.00 (referent) | .036 |
| Yes | 240 (2.6) | 19 (5.1) | 2.01 (1.10 to 3.68) | 126 (2.8) | 6 (5.9) | 2.14 (0.90 to 5.09) | 108 (2.6) | 13 (5.1) | 2.01 (1.10 to 3.68) | |||
| Leukemia, male relative | ||||||||||||
| No | 3743 (89.2) | 210 (83.0) | 1.00 (referent) | .498 | 3958 (88.2) | 87 (85.3) | 1.00 (referent) | .398 | 3743 (89.2) | 210 (83.0) | 1.00 (referent) | .498 |
| Yes | 131 (1.4) | 8 (2.0) | 1.40 (0.55 to 3.59) | 71 (1.6) | 3 (2.9) | 1.75 (0.53 to 5.82) | 57 (1.4) | 5 (2.0) | 1.40 (0.55 to 3.59) | |||
| Leukemia, female relative | ||||||||||||
| No | 3750 (89.4) | 207 (81.8) | 1.00 (referent) | .026 | 3303 (86.7) | 76 (83.5) | 1.00 (referent) | .128 | 3750 (89.4) | 207 (81.8) | 1.00 (referent) | .026 |
| Yes | 103 (1.2) | 11 (3.2) | 2.66 (1.22 to 5.81) | 50 (1.3). | 3 (3.3) | 2.92 (0.87 to 9.80) | 50 (1.2) | 8 (3.2) | 2.66 (1.22 to 5.81) | |||
* Odds ratio (OR) and 95% confidence interval (CI) adjusted for age, sex, race, and study. NHL = non-Hodgkin lymphoma.
† The counts do not add up to the total number of cases and controls due to data missing by design or report. Also, male and female counts do not always add up to the total as all studies did not contribute to every analysis.
‡ Based on International Statistical Classification of Diseases and Related Health Problems (ICD)- 9 and ICD10 classification.
For farming-related exposures, we observed a 40% increased risk of MCL with ever having lived on a farm (OR = 1.40, 95% CI = 1.03 to 1.90) (Table 4), whereas the risk for working on a farm was not significant (OR = 1.17, 95% CI = 0.85 to 1.61). In the eight studies that contributed complete occupational histories, we observed no association with having worked on a farm regardless of the type of farming (Table 4). Concerning other occupations, an increased risk was noted for electrical and electronics workers (OR = 1.63, 95% CI = 1.09 to 2.44, N = 30) and for drivers that were material-handling equipment operators (OR = 3.05, 95% CI = 1.47 to 6.31, N = 9), although the latter was based on a small number of exposed cases (Table 4). There were also suggestive bordeline increased risks among all drivers and cleaners (Table 4).
Table 4.
Risk of mantle cell lymphoma associated with a history of farming and selected occupations*
| Controls, No. (%)† | Cases, No. (%)† | OR (95% CI) | P | |
|---|---|---|---|---|
| Farming | ||||
| Ever lived or worked on a farm‡ | ||||
| No | 6488 (65.0) | 211 (61.3) | 1.00 (referent) | .254 |
| Yes | 3232 (32.4) | 120 (34.9) | 1.17 (0.90 to 1.53) | |
| Ever lived on a farm‡ | ||||
| No | 3230 (53.1) | 99 (46.3) | 1.00 (referent) | .031 |
| Yes | 2617 (43.0) | 102 (47.7) | 1.40 (1.03 to 1.90) | |
| Ever worked on a farm‡ | ||||
| No | 7791 (84.1) | 260 (79.3) | 1.00 (referent) | .334 |
| Yes | 1272 (13.7) | 57 (17.4) | 1.17 (0.85 to 1.61) | |
| Ever worked in animal farming | ||||
| No | 7946 (97.3) | 279 (97.6) | 1.00 (referent) | .683 |
| Yes | 204 (2.5) | 7 (2.4) | 0.85 (0.39 to 1.86) | |
| Ever worked in crop farming | ||||
| No | 7857 (96.2) | 276 (96.5) | 1.00 (referent) | .695 |
| Yes | 293 (3.6) | 10 (3.5) | 0.87 (0.44 to 1.73) | |
| Ever worked in field crop and vegetable farming | ||||
| No | 6639 (97.8) | 219 (99.5) | 1.00 (referent) | .062 |
| Yes | 122 (1.8) | 1 (0.5) | 0.23 (0.03 to 1.69) | |
| Ever worked in mixed and unspecified farming | ||||
| No | 7135 (93.5) | 245 (90.7) | 1.00 (referent) | .457 |
| Yes | 482 (6.3) | 25 (9.3) | 1.19 (0.76 to 1.84) | |
| Other occupations | ||||
| Cleaners | ||||
| No | 7746 (94.9) | 269 (94.1) | 1.00 (referent) | .088 |
| Yes | 404 (4.9) | 17 (5.9) | 1.61 (0.96 to 2.69) | |
| Drivers | ||||
| No | 7538 (92.3) | 248 (86.7) | 1.00 (referent) | .086 |
| Yes | 612 (7.5) | 38 (13.3) | 1.40 (0.97 to 2.03) | |
| Drivers: material-handling equipment operators | ||||
| No | 8069 (98.8) | 277 (96.9) | 1.00 (referent) | .008 |
| Yes | 70 (0.9) | 9 (3.1) | 3.05 (1.47 to 6.31) | |
| Electrical and electronics workers | ||||
| No | 7690 (94.2) | 256 (89.5) | 1.00 (referent) | .023 |
| Yes | 460 (5.6) | 30 (10.5) | 1.63 (1.09 to 2.44) | |
| Woodworkers | ||||
| No | 7909 (96.9) | 271 (94.8) | 1.00 (referent) | .240 |
| Yes | 241 (3.0) | 15 (5.2) | 1.41 (0.81 to 2.43) | |
| Welders and flamecutters | ||||
| No | 8017 (98.2) | 280 (97.9) | 1.00 (referent) | .898 |
| Yes | 133 (1.6) | 6 (2.1) | 1.06 (0.46 to 2.44) | |
| Textile worker | ||||
| No | 7724 (94.6) | 275 (96.2) | 1.00 (referent) | .953 |
| Yes | 426 (5.2) | 11 (3.8) | 1.02 (0.54 to 1.91) | |
| Painter | ||||
| No | 8015 (98.2) | 278 (97.2) | 1.00 (referent) | .396 |
| Yes | 135 (1.7) | 8 (2.8) | 1.39 (0.67 to 2.91) | |
| Metal worker | ||||
| No | 7693 (94.2) | 267 (93.4) | 1.00 (referent) | .709 |
| Yes | 457 (5.6) | 19 (6.6) | 0.91 (0.55 to 1.50) | |
* Odds ratio (OR) and 95% confidence interval (95% CI) adjusted for age, sex, race, and study.
† The counts do not add up to the total number of cases and controls due to data missing by design or report.
‡ Indicates self-reported history of living and/or working on a farm, based on specific questions during interview. All other occupational data were ascertained through detailed occupational histories, coded according to the International Standard Classification of Occupations (ISCO), Revised Edition 1968.
Risk of MCL was not associated with height, weight or body mass index, tobacco smoking or alcohol intake among both sexes, or use of hair dyes, oral contraceptives, or hormone replacement therapy, or number of children among women (Table 5). High recreational sun exposure was associated with a statistically significant reduced risk but there was no trend in risk over exposure categories (Table 5). Potential associations with physical activity or young adult body mass index were difficult to evaluate due to a relatively high degree of missing data both by design and report (data not shown).
Table 5.
Risk of mantle cell lymphoma associated with lifestyle factors*
| Controls, No. (%)† | Cases, No. (%)† | OR (95% CI) | P | |
|---|---|---|---|---|
| Usual adult BMI, kg/m2 | ||||
| 15–<18.5 | 197 (1.5) | 4 (0.7) | 0.87 (0.31 to 2.44) | |
| 18.5–<22.5 | 2348 (18.0) | 70 (13.1) | 1.00 (referent) | .993 |
| 22.5–<25 | 3024 (23.1) | 123 (23.0) | 1.03 (0.76 to 1.40) | |
| 25–<30 | 4755 (36.4) | 228 (42.6) | 1.03 (0.78 to 1.37) | |
| 30–<35 | 1564 (12.0) | 66 (12.3) | 0.95 (0.67 to 1.36) | |
| 35–50 | 582 (4.5) | 24 (4.5) | 1.05 (0.65 to 1.71) | |
| Usual adult weight, kg‡ | ||||
| Quartile 1 (low) | 2775 (21.2) | 103 (19.3) | 1.00 (referent) | .565 |
| Quartile 2 | 2713 (20.8) | 99 (18.5) | 0.85 (0.64 to 1.14) | |
| Quartile 3 | 3391 (25.9) | 150 (28.0) | 1.02 (0.78 to 1.32) | |
| Quartile 4 (high) | 3591 (27.5) | 163 (30.5) | 0.98 (0.76 to 1.28) | |
| Usual adult height, cm‡ | ||||
| Quartile 1 (low) | 3090 (23.6) | 120 (22.4) | 1.00 (referent) | .292 |
| Quartile 2 | 3044 (23.3) | 107 (20.0) | 0.90 (0.68 to 1.18) | |
| Quartile 3 | 3134 (24.0) | 149 (27.9) | 1.12 (0.86 to 1.44) | |
| Quartile 4 (high) | 3202 (24.5) | 139 (26.0) | 1.12 (0.86 to 1.46) | |
| History of cigarette smoking§ | ||||
| No | 5463 (42.3) | 183 (36.1) | 1.00 (referent) | .598 |
| Yes | 6531 (50.5) | 273 (53.8) | 1.06 (0.87 to 1.29) | |
| Frequency of cigarette smoking | ||||
| Nonsmoker | 5463 (42.3) | 183 (36.1) | 1.00 (referent) | .489 |
| Smoker, 1–10 cigarettes/d | 2458 (19.0) | 88 (17.4) | 1.00 (0.77 to 1.30) | |
| Smoker, 11–20 cigarettes/d | 2647 (20.5) | 127 (25.0) | 1.17 (0.92 to 1.48) | |
| Smoker, 21–30 cigarettes/d | 622 (4.8) | 28 (5.5) | 1.14 (0.75 to 1.74) | |
| Smoker, >30 cigarettes/d | 573 (4.4) | 21 (4.1) | 0.75 (0.47 to 1.20) | |
| Smoker, cigarettes/d unknown | 231 (1.8) | 9 (1.8) | 0.98 (0.49 to 1.96) | |
| History of alcohol consumption | ||||
| Nondrinker | 2121 (17.3) | 62 (12.8) | 1.00 (referent) | .965 |
| Drinker (at least 1 drink per month) | 5717 (46.8) | 221 (45.6) | 0.99 (0.72 to 1.36) | |
| Servings of alcohol per week as an adult | ||||
| Nondrinker | 2121 (17.3) | 62 (12.8) | 1.00 (referent) | .226 |
| <1 drink/wk | 815 (6.7) | 18 (3.7) | 0.6 (0.35 to 1.04) | |
| 1–6 drinks/wk | 2181 (17.8) | 85 (17.5) | 1.03 (0.72 to 1.47) | |
| 7–13 drinks/wk | 1109 (9.1) | 53 (10.9) | 1.21 (0.81 to 1.82) | |
| 14–27 drinks/wk | 947 (7.7) | 45 (9.3) | 1.14 (0.74 to 1.75) | |
| 28+ drinks/wk or binge drinkers | 636 (5.2) | 20 (4.1) | 0.92 (0.53 to 1.61) | |
| Drinker, drinks/wk unknown | 29 (0.2) | 0 (0.0) | — | |
| Total sun exposure (h/wk)† | ||||
| Quartile 1 (low) | 1508 (18.7) | 44 (16.1) | 1.00 (referent) | .428 |
| Quartile 2 | 1594 (19.8) | 42 (15.3) | 0.79 (0.51 to 1.22) | |
| Quartile 3 | 1633 (20.3) | 44 (16.1) | 0.73 (0.47 to 1.13) | |
| Quartile 4 (high) | 1714 (21.3) | 55 (20.1) | 0.72 (0.47 to 1.09) | |
| Recreational sun exposure (h/wk)† | ||||
| Quartile 1 (low) | 2234 (20.6) | 93 (22.2) | 1.00 (referent) | .133 |
| Quartile 2 | 2332 (21.6) | 85 (20.3) | 0.92 (0.69 to 1.27) | |
| Quartile 3 | 2159 (20.0) | 89 (21.3) | 0.98 (0.73 to 1.34) | |
| Quartile 4 (high) | 2983 (27.6) | 103 (24.6) | 0.74 (0.55 to 0.99) | |
| Ever used hair dyes (women only) | ||||
| Never | 856 (13.5) | 15 (6.7) | 1.00 (referent) | .517 |
| Ever | 2412 (38.1) | 53 (23.7) | 1.21 (0.67 to 2.19) | |
| Frequency of hair dye use (women only) | ||||
| Never | 856 (13.5) | 15 (6.7) | 1.00 (referent) | .507 |
| 1–5 times/y | 835 (13.2) | 13 (5.8) | 0.94 (0.43 to 2.03) | |
| 6–11 times/y | 849 (13.4) | 19 (8.5) | 1.12 (0.55 to 2.25) | |
| 12+ times/y | 507 (8.0) | 16 (7.1) | 1.77 (0.86 to 3.65) | |
| Ever, frequency unknown | 221 (3.5) | 5 (2.2) | 1.13 (0.37 to 3.49) | |
| Number of children (women only) | ||||
| 0 | 663 (7.7) | 11 (3.7) | 0.79 (0.33 to 1.86) | |
| 1 | 467 (5.5) | 11 (3.7) | 1.00 (referent) | .820 |
| 2 | 1137 (13.3) | 28 (9.4) | 0.86 (0.42 to 1.75) | |
| 3+ | 1541 (18) | 28 (9.4) | 0.61 (0.29 to 1.26) | |
| Use of oral contraceptives (women only) | ||||
| Never | 1871 (32) | 39 (20.4) | 1.00 (referent) | .805 |
| Ever | 1012 (17.3) | 18 (9.4) | 0.91 (0.45 to 1.85) | |
| Use of hormone replacement therapy (women only) | ||||
| Never | 1411 (31.2) | 25 (15.2) | 1.00 (referent) | .313 |
| Ever | 729 (16.1) | 22 (13.4) | 1.39 (0.74 to 2.62) | |
* Odds ratio (OR) and 95% confidence interval (CI) adjusted for age, sex, race, and study. BMI = body mass index.
† The counts do not always add up to the total number of cases and controls due to data missing by design or report.
‡ For height and weight, quartiles were sex-specific, based on the full control population (height, males: <172.0, 172.0–177.7, 177.8–181.9, ≥182.0cm; females: <159.0, 159.0–162.9, 163.0–167.9, ≥168.0cm; weight, males: <72.6, 72.6–79.9, 80.0–88.9, ≥89.0kg; females: <58.1, 58.1–64.9, 65.0–74.7, ≥74.8kg). For total and recreational sun exposure, study-specific quartiles are available upon request.
§ Smoked longer than 6 mo or more than 100 cigarettes in lifetime.
Observed associations with hay fever, family history, living on a farm, and the specific occupations listed above remained unchanged upon multivariate adjustment, nor was there evidence of effect modification by study design, geographical region, age, or primary GI tract involvement (data not shown). Also, there was no evidence of significant variation in results by study as assessed by the H statistic, except for with family history of hematological malignancies in female relatives.
Discussion
In this unique international pooled case–control study of risk factors for MCL, we observed a previously unrecognized strong inverse association with hay fever and risk of MCL. We also report a novel increased risk of MCL associated with ever having lived on a farm. These findings suggest a role for environmental factors in MCL etiology. We confirmed an approximately doubled risk of MCL associated with a family history of hematological malignancy, and further showed that risks were more evident among male patients and when the relatives were male, providing clues for future investigations of underlying genetic susceptibility for MCL.
Previous studies investigating the etiology of MCL are rare, although risk of MCL has sometimes been evaluated in the context of studies of all NHL and its subtypes (7,8). In a previous pooled analysis within the InterLymph consortium, we investigated allergic and atopic disorders as possible risk factors for NHL subtypes, and observed a modest inverse association of specific allergies and hay fever with risk of B-cell NHL overall (26), which is in line with some (27,28), but not all (29–31), studies of allergic disorders and NHL. In the previous InterLymph study, risk of MCL was evaluated together with chronic lymphocytic leukemia and small lymphocytic lymphoma, and nonsignificant inverse associations with atopy and allergy were observed for this mixed group. Concern has been expressed that the reported inverse association between atopy and allergy and NHL or B-cell NHL could be explained by reverse causality due to a dysfunctional immune system and impaired antibody response during the preclinical phase of a B-cell malignancy (29,32). To reduce the risk of this potential bias, reports of the occurrence of atopy and allergy within 2 years before diagnosis or interview were disregarded. Also, our findings of an association specifically with hay fever and MCL risk argues against such an explanation. Hay fever is characterized by a hypersensitive immune response where interaction of IgE antibodies and allergens leads to an abundant release of cytokines and chemokines (33). It has been speculated that this heightened response may also act against cancer-specific or cancer-associated antigens and lead to early detection and eradication of tumor cells (34). Further studies are needed to confirm the observed association and to better understand the potential mechanisms in risk of MCL.
The role of a family history of hematological malignancy and risk of MCL has been investigated once previously (20), and included 40% of the cases in the current study. With more than twice as many patients in the present analysis, we demonstrated a similar, approximately doubled risk of MCL in association with self-reported family history of related malignancies, specifically among male relatives. We also extended the previous investigation in showing that male patients were more likely than females to have a positive family history. Surprisingly, although MCL is a disease that occurs predominantly in males, we did not identify gender-specific risks or indications of effect modification by gender for any other exposure. Our analyses also revealed higher risks when the affected relatives were male, although this observation may be explained by the male predominance of most hematopoietic malignancies.
The most likely biological mechanism behind the observed familial link is inherited genetic variation, although shared environmental factors could also theoretically contribute to risk. A few studies have investigated candidate gene variants and risk of NHL including MCL (35–37), and associations with variants in the proinflammatory tumor necrosis factor and the interleukin 10 genes have been reported, but remain unconfirmed. Based on the familial risk of MCL and the lack of consistent results in previous small-scale candidate gene studies, a large-scale investigation of genetic susceptibility for MCL is warranted. With regard to the pattern of a male predominance in family history and MCL risk, our findings indicate that further investigation of the role of genetic susceptibility in MCL should be stratified by sex.
An array of occupational exposures, including most notably organic solvents and pesticides, have been associated with risk of NHL overall and B-cell NHL (38,39). A history of farming was one of the first risk factors identified for NHL (39,40), but no previous studies have investigated such risks specifically for MCL. Although our data support an association between MCL and living on a farm, the results should be interpreted cautiously in light of the lack of a clear association with working on a farm. Some occupational risk factors have been investigated previously in risk of NHL and its subtypes within the consortium (41), but case numbers did not allow for separate evaluation of risk of MCL. In the current investigation, our results suggest that electric and electronics workers, as well as material-handling operator drivers, may have an increased risk of MCL, although the numbers of exposed cases were small. These occupational groups are highly exposed to engine exhausts, organic solvents and polychlorinated biphenyls, and electric and electronic workers are also exposed to electromagnetic fields (42,43). The lack of association in the current study with other occupations characterized by exposure to similar agents, such as painters, welders, textile, and wood workers, suggests that our positive findings could be due to chance and/or bias and should be interpreted with caution.
Interestingly, risk of MCL was not related to a history of autoimmune disorders or lifestyle factors such as tobacco smoking, alcohol intake, hair dye use, ultraviolet radiation, body mass index, or hormonal factors, in contrast with one or several more common NHL subtypes (44–49). However, associations with specific disorders, such as, for example, ulcerative colitis, cannot be ruled out in view of limited power. We also note that profound immunosuppression (eg, associated with HIV/AIDS or following solid organ transplantation), a well-established risk factor for NHL overall and for specific subtypes such as diffuse large B-cell lymphoma, has not been associated with MCL (14,50). In our study, 7% of individuals with MCL had evidence of primary GI tract disease at diagnosis, in line with a recent US registry analysis showing that 7.8% of individuals presented with MCL in a primary GI site (51). Due to the limited number of patients with primary GI MCL, we could not fully evaluate whether risks for MCL differ by primary site of presentation. A previous Scandinavian study suggested that infection with the spirochete B. burgdorferi increased MCL risk (21). However, those findings have so far not been validated nor refuted by others. Irrespective of the scarce evidence for associations between specific infectious agents and MCL, a role for antigenic drive in the etiology of at least a subset of MCL is supported by findings of a restricted Ig gene repertoire and targeted somatic hypermutation in MCL tumors (15).
The strengths of the current study include its large size and the lack of heterogeneity of the results across participating studies in spite of data pooling across several study centers and geographical regions. The most important limitation is the exposure assessment through self-reports, which may be subject to misclassification. In particular, self-reported family history of malignancies may be subject to differential recall among cases and controls, which could have inflated some estimates (52). However, recall bias would not explain the observed differences in results by sex. Also, some results may have arisen by chance considering the large number of analyses performed.
In summary, in this unique international collaborative effort, we provide novel and important leads for future studies of environmental and genetic risk factors in MCL etiology.
Funding
This pooled analysis was supported by the Intramural Research Program of the National Cancer Institute/National Institutes of Health and National Cancer Institute/National Institutes of Health (R01 CA14690, U01 CA118444, and R01 CA92153-S1). InterLymph annual meetings during 2010–2013 were supported by the Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute/National Institutes of Health (2010–2013); Lymphoma Coalition (2010–2013); National Institutes of Health Office of Rare Diseases Research (2010); National Cancer Institute/National Institutes of Health (R13 CA159842 01) (2011); University of Cagliari, Provincial Administration of Cagliari, Banca di Credito Sardo, and Consorzio Industriale Sardo, Italy (2011); Intramural Research Program of the National Cancer Institute/National Institutes of Health (2012); and Faculté de Médecine de Dijon, Institut de Veille Sanitaire, Registre des hémopathies malignes de Côte d’Or, INSERM, Institut National du Cancer, Université de Bourgogne, Groupe Ouest Est d’Etude des Leucémies et Autres Maladies du Sang (GOELAMS), l’Institut Bergonié, The Lymphoma Study Association (LYSA), Registre Régional des Hémopathies de Basse Normandie, and the City of Dijon, France (2013). Meeting space at the 2013 Annual Meeting of the American Association for Cancer Research (AACR) was provided by the Molecular Epidemiology Group (MEG) of the AACR. Pooling of the occupation data was supported by the National Cancer Institute/National Institutes of Health (R03CA125831). Individual studies were supported by: the Canadian Institutes for Health Research (CIHR), Canadian Cancer Society, and Michael Smith Foundation for Health Research (British Columbia); Intramural Research Program of the National Cancer Institute/National Institutes of Health (Iowa/Minnesota); National Cancer Institute/National Institutes of Health (N01-CP-ES-11027) (Kansas); National Cancer Institute/National Institutes of Health (R01 CA50850) (Los Angeles); National Cancer Institute/National Institutes of Health (R01 CA92153 and P50 CA97274), Lymphoma Research Foundation (164738), and the Henry J. Predolin Foundation (Mayo Clinic); Intramural Research Program of the National Cancer Institute/National Institutes of Health and Public Health Service (contracts N01-PC-65064, N01-PC-67008, N01-PC-67009, N01-PC-67010, and N02-PC-71105) (NCI-SEER); National Cancer Institute/National Institutes of Health (R01CA100555 and R03CA132153) and American Institute for Cancer Research (99B083) (Nebraska [newer]); National Cancer Institute/National Institutes of Health (N01-CP-95618) and State of Nebraska Department of Health (LB-506) (Nebraska [older]); National Cancer Institute/National Institutes of Health (R01CA45614, RO1CA154643-01A1, and R01CA104682) (UCSF1); National Cancer Institute/National Institutes of Health (CA143947, CA150037, R01CA087014, R01CA104682, RO1CA122663, and RO1CA154643-01A1) (UCSF2); National Heart Lung and Blood Institute/National Institutes of Health (hematology training grant award T32 HL007152), National Center for Research Resources/National Institutes of Health (UL 1 RR024160), and National Cancer Institute/National Institutes of Health (K23 CA102216 and P50 CA130805) (University of Rochester); National Cancer Institute/National Institutes of Health (CA62006 and CA165923) (Yale); Association pour la Recherche contre le Cancer, Fondation de France, AFSSET, and a donation from Faberge employees (Engela); European Commission (QLK4-CT-2000-00422 and FOOD-CT-2006–023103), Spanish Ministry of Health (CIBERESP, PI11/01810, RCESP C03/09, RTICESP C03/10, and RTIC RD06/0020/0095), Rio Hortega (CM13/00232), Agència de Gestió d’Ajuts Universitaris i de Recerca–Generalitat de Catalunya (Catalonian Government, 2009SGR1465), National Institutes of Health (contract NO1-CO-12400), Italian Ministry of Education, University and Research (PRIN 2007 prot. 2007WEJLZB, PRIN 2009 prot. 20092ZELR2), Italian Association for Cancer Research (IG grant 11855/2011), Federal Office for Radiation Protection (StSch4261 and StSch4420), José Carreras Leukemia Foundation (DJCLS-R04/08), German Federal Ministry for Education and Research (BMBF-01-EO-1303), Health Research Board, Ireland and Cancer Research Ireland, and Czech Republic MH CZ - DRO (MMCI, 00209805) (EpiLymph); National Cancer Institute/National Institutes of Health (CA51086), European Community (Europe Against Cancer Programme), and Italian Alliance Against Cancer (Lega Italiana per la Lotta contro i Tumori) (Italy, multicenter); Italian Association for Cancer Research (IG 10068) (Italy, Aviano-Milan); Italian Association for Cancer Research (Italy, Aviano-Naples); Swedish Cancer Society (2009/659), Stockholm County Council (20110209), Strategic Research Program in Epidemiology at Karolinska Institut, Swedish Cancer Society (02 6661), Danish Cancer Research Foundation, Lundbeck Foundation (R19-A2364), Danish Cancer Society (DP 08-155), National Cancer Institute/National Institutes of Health (5R01 CA69669-02), and Plan Denmark (SCALE); Leukaemia & Lymphoma Research, UK; and Australian National Health and Medical Research Council (ID990920), Cancer Council NSW, and University of Sydney Faculty of Medicine (New South Wales).
We thank the following individuals for their substantial contributions to this project: Aaron D. Norman, Dennis P. Robinson, and Priya Ramar (Mayo Clinic College of Medicine) for their work at the InterLymph Data Coordinating Center in organizing, collating, harmonizing, and documenting of the data from the participating studies in the InterLymph Consortium; Michael Spriggs, Peter Hui, and Bill Wheeler (Information Management Services, Inc) for their programming support; and Noelle Richa Siegfried and Emily Smith (RTI International) for project coordination.
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