Abstract
Background
Acute lymphoblastic leukemia/lymphoma (ALL) in adults is a rare malignancy with a poor clinical outcome, and few reported etiologic risk factors.
Methods
We performed an exploratory pooled study of 152 ALL cases and 23096 controls from 16 case–control studies to investigate the role of medical history, lifestyle, family history, and occupational risk factors and risk of ALL. Age- race/ethnicity-, sex-, and study-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression.
Results
An increased risk of ALL was found in those with a family history of a hematological malignancy (OR = 2.6, 95% CI = 1.22 to 5.54) and in leather (OR = 3.91, 95% CI = 1.35 to 11.35) and sewing/embroidery workers (OR = 2.92, 95% CI = 1.00 to 8.49). Consumers of alcohol had an increased risk of B-cell ALL (OR = 2.87, 95% CI = 1.18 to 6.95).
Conclusions
The small number of statistically significant risk factors identified out of the 112 variables examined could be chance findings and will require further replication to assess their role in the etiology of adult ALL.
Acute lymphoblastic leukemia/lymphoma (ALL) is a rare malignancy of B or T cells. However, unlike other hematological malignancies, the majority of ALL diagnoses are made in children under the age of 15 years with only 40% of cases diagnosed in adults 20 years or older in economically developed countries (1–3). Survival rates for childhood ALL have improved dramatically over the last 50 years, and several etiological risk factors have been identified including genetic susceptibility (4,5), high birth weight (6,7), and trisomy 21 (8), with maternal chemical exposures (9,10) and childhood immune response factors also thought to be important determinants of disease risk (11,12). However, the prognosis for adult ALL remains poor, with few reported risk factors except for those related to genetic susceptibility (13,14). To advance our understanding of the etiology of adult ALL, we investigated potential associations with lifestyle, medical history, family history, and occupational risk factors in a pooled analysis of 152 cases and 23096 controls from 16 case–control studies from Europe, North America, and Australia as part of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma (NHL) Subtypes Project. We also sought to identify common risk factors for adult and childhood ALL.
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 ALL and 2) availability of individual-level data for at least several risk factors of interest by December 31, 2011. Contributing studies were approved by local ethics review committees, and all participants provided written informed consent before interview.
Cases were classified according to the World Health Organization classification (15,16) using guidelines from the InterLymph Pathology Working Group (17,18). Most studies had some form of centralized pathology review by at least one expert hematopathologist to confirm the ALL diagnoses. The pathology review procedures for each participating study were reviewed by an interdisciplinary team of pathologists and epidemiologists.
Each study collected data on putative NHL risk factors in a standardized, structured format by in-person or telephone interviews and/or self-reported questionnaires. Risk factors selected for inclusion were the available medical history, lifestyle, family history, and occupational risk factors with data from at least four studies. Details of the data harmonization rules are provided elsewhere in this issue (19).
Risk of ALL associated with each exposure variable was examined using logistic regression models in a basic model for age, race/ethnicity, sex, and study. We estimated odds ratios (ORs) and 95% confidence intervals (95% CIs) for each association. The statistical significance of each relationship 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 and P values greater than or equal to .05 to P values less than .1 identifying suggestive risk factors. We also evaluated associations between exposures and ALL by cell type (B or T cell). To evaluate effect heterogeneity among the 16 studies, we performed a separate logistic regression within each study and then quantified the variability of the coefficients by the H statistic, adapting the definition of Higgins and Thompson to categorical variables (20).
Because controls for most original studies were chosen to frequency-match the age and sex of all NHL cases, rather than just ALL cases, we conducted sensitivity analyses using a subset of controls that were matched by age and sex to the ALL cases. The results of these sensitivity analyses were very similar to the results obtained using the full set of controls (results not shown); thus, we retained the full set of controls for our main analyses to increase statistical power.
This analysis included 152 cases of adult ALL (55 B cell, 48 T cell, and 49 not otherwise specified) and 23096 controls from 16 studies. The median age of the cases at the time of diagnosis was 41 years (range, 18–91 years); 60 cases (39.5%) were women and 132 (87%) were of European decent (Table 1). The median age of the controls was 59 years (range, 16–98 years); 9608 controls (41.6%) were women and 21572 (93.4%) were of European descent. Although the sample size was small, the sex and race/ethnicity distributions were fairly consistent across studies. The age distribution among the cases and controls was skewed, with cases being younger than controls.
Table 1.
Characteristics of studies included in the InterLymph NHL Subtypes Project*
| Controls | Cases | |
|---|---|---|
| No. (%) | No. (%) | |
| Total | 23096 | 152 |
| Study | ||
| New South Wales | 694 (3.0) | 5 (3.3) |
| Mayo Clinic | 1314 (5.7) | 1 (0.7) |
| British Columbia | 845 (3.7) | 6 (3.9) |
| Nebraska (newer) | 533 (2.3) | 1 (0.7) |
| United Kingdom | 1139 (4.9) | 0 (0.0) |
| NCI-SEER | 1055 (4.6) | 0 (0.0) |
| UCSF1 | 2402 (10.4) | 12 (7.9) |
| EpiLymph | 2460 (10.7) | 46 (30.3) |
| Yale | 717 (3.1) | 3 (2.0) |
| SCALE | 3187 (13.8) | 15 (9.9) |
| Los Angeles | 375 (1.6) | 16 (10.5) |
| Italy multicenter | 1771 (7.7) | 12 (7.9) |
| Italy (Aviano-Milan) | 1157 (5.0) | 10 (6.6) |
| Italy (Aviano-Naples) | 504 (2.2) | 0 (0.0) |
| Iowa/Minnesota | 1245 (5.4) | 6 (3.9) |
| Kansas | 948 (4.1) | 1 (0.7) |
| Nebraska (older) | 1432 (6.2) | 3 (2.0) |
| Engela | 722 (3.1) | 8 (5.3) |
| UCSF2 | 457 (2.0) | 7 (4.6) |
| University of Rochester | 139 (0.6) | 0 (0.0) |
| Region | ||
| North America | 11462 (49.6) | 56 (36.8) |
| Northern Europe | 6542 (28.3) | 59 (38.8) |
| Southern Europe | 4398 (19.0) | 32 (21.1) |
| Australia | 694 (3.0) | 5 (3.3) |
| Design | ||
| Population based | 17846 (77.3) | 104 (68.4) |
| Hospital based | 5250 (22.7) | 48 (31.6) |
| Total | 23096 (100.0) | 152 (100.0) |
| Age | ||
| <30 | 1360 (5.9) | 38 (25.0) |
| 30–39 | 2180 (9.4) | 36 (23.7) |
| 40–49 | 3159 (13.7) | 16 (10.5) |
| 50–59 | 4992 (21.6) | 30 (19.7) |
| 60–69 | 6380 (27.6) | 24 (15.8) |
| 70–79 | 4136 (17.9) | 6 (3.9) |
| ≥80 | 873 (3.8) | 2 (1.3) |
| Missing | 16 (0.1) | 0 (0.0) |
| Sex | ||
| Male | 13495 (58.4) | 92 (60.5) |
| Female | 9601 (41.6) | 60 (39.5) |
| Race/ethnicity | ||
| White, non-Hispanic | 21576 (93.4) | 132 (86.8) |
| Black | 351 (1.5) | 1 (0.7) |
| Asian | 321 (1.4) | 7 (4.6) |
| Hispanic | 360 (1.6) | 6 (3.9) |
| Other/unknown/missing | 488 (2.1) | 6 (3.9) |
| SES | ||
| Low | 9335 (40.4) | 51 (33.6) |
| Medium | 6709 (29.0) | 53 (34.9) |
| High | 6642 (28.8) | 48 (31.6) |
| Other/missing | 410 (1.8) | 0 (0.0) |
| NHL classification scheme | ||
| World Health Organization | 13766 (59.6) | 92 (60.5) |
| Working Formulation | 9330 (40.4) | 60 (39.5) |
| ALL cell type | ||
| B cell | 0 (0.0) | 55 (36.2) |
| T cell | 0 (0.0) | 48 (31.6) |
| NOS | 0 (0.0) | 49 (32.2) |
| Missing | 23 096 (100.0) | 0 (0.0) |
* ALL = acute lymphoblastic leukemia/lymphoma; NCI-SEER = National Cancer Institute-Surveillance, Epidemiology, and End Results; NHL = non-Hodgkin lymphoma; NOS = not otherwise specified; SCALE = Scandinavian Lymphoma Etiology Study; SES = socioeconomic status; UCSF = University of California San Francisco.
Results with P values less than .05 and results approaching significance (P ≥ .05 to P < .1), in an analyses adjusted for age, sex, and study, for associations with combined ALL, and B- or T-cell types, are presented in Table 2. All results, including null results with the exception where there were no cases with the exposure, are presented in Supplementary Table 1 (available online). A total of 112 variables were tested in all, though some variables within each risk factor category were correlated and thus did not represent independent exposures (19).
Table 2.
Factors associated with acute lymphoblastic leukemia/lymphoma risk, overall and stratified by cell type*
| Total | B-Cell | T-Cell | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Controls | Cases | Controls | Cases | Controls | Cases | |||||||
| No. (%) | No. (%) | OR (95% CI)† | P | No. (%) | No. (%) | OR (95% CI)† | P | No. (%) | No. (%) | OR (95% CI)† | P | |
| First-degree family history | ||||||||||||
| Any hematologic malignancy | ||||||||||||
| No | 11705 (90.1) | 93 (90.3) | 1.00 (referent) | .027 | 4356 (86.6) | 38 (90.5) | 1.00 (referent) | .061 | 4364 (92.0) | 19 (82.6) | 1.00 (referent) | .175 |
| Yes | 614 (4.7) | 8 (7.8) | 2.60 (1.22 to 5.54) | 236 (4.7) | 4 (9.5) | 3.30 (1.10 to 9.91) | 200 (4.2) | 2 (8.7) | 3.33 (0.73 to 15.11) | |||
| Any hematologic malignancy, male relative | ||||||||||||
| No | 8940 (90.6) | 78 (94.0) | 1.00 (referent) | .204 | 3764 (87.4) | 40 (97.6) | 1.00 (referent) | .618 | 3760 (93.5) | 13 (81.3) | 1.00 (referent) | .222 |
| Yes | 256 (2.6) | 3 (3.6) | 2.38 (0.72 to 7.89) | 107 (2.5) | 1 (2.4) | 1.75 (0.23 to 13.36) | 83 (2.1) | 1 (6.3) | 4.88 (0.60 to 39.90) | |||
| Any hematologic malignancy, female relative | ||||||||||||
| No | 8961 (90.8) | 77 (92.8) | 1.00 (referent) | .057 | 3771 (87.6) | 38 (92.7) | 1.00 (referent) | .025 | 3759 (93.5) | 14 (87.5) | 1.00 (referent) | .487 |
| Yes | 235 (2.4) | 4 (4.8) | 3.30 (1.13 to 9.59) | 100 (2.3) | 3 (7.3) | 5.87 (1.60 to 21.52) | 84 (2.1) | 0 (0.0) | — | |||
| Occupation | ||||||||||||
| Leather worker | ||||||||||||
| No | 8438 (94.9) | 84 (94.4) | 1.00 (referent) | .032 | 3818 (98.5) | 41 (93.3) | 1.00 (referent) | .073 | 6352 (97.8) | 30 (93.8) | 1.00 (referent) | .139 |
| Yes | 146 (1.6) | 4 (4.5) | 3.91 (1.35 to 11.35) | 56 (1.3) | 2 (4.4) | 5.33 (1.17 to 24.21) | 138 (1.9) | 2 (5.7) | 3.81 (0.85 to 17.02) | |||
| Sewer and embroiderer | ||||||||||||
| No | 9631 (94.9) | 88 (94.6) | 1.00 (referent) | .083 | 4278 (97.0) | 40 (88.9) | 1.00 (referent) | .027 | 7012 (97.3) | 35 (100.0) | 1.00 (referent) | .343 |
| Yes | 191 (1.9) | 4 (4.3) | 2.92 (1.00 to 8.49) | 118 (2.7) | 4 (8.9) | 4.38 (1.41 to 13.62) | 183 (2.5) | 0 (0.0) | — | |||
| Lifestyle | ||||||||||||
| History of alcohol consumption | ||||||||||||
| Nondrinker | 3627 (27.7) | 34 (29.6) | 1.00 (referent) | .969 | 1134 (25.2) | 8 (19.5) | 1.00 (referent) | .015 | 1595 (28.1) | 8 (28.6) | 1.00 (referent) | .121 |
| Drinker (at least one drink per month) | 8558 (65.3) | 64 (55.7) | 1.01 (0.63 to 1.61) | 2444 (54.4) | 21 (51.2) | 2.87 (1.18 to 6.95) | 3331 (58.7) | 15 (53.6) | 0.46 (0.18 to 1.19) | |||
| Alcohol consumption status as of ~2 y before diagnosis/interview | ||||||||||||
| Nondrinker | 3627 (27.7) | 34 (29.6) | 1.00 (referent) | .357 | 1134 (25.2) | 8 (19.5) | 1.00 (referent) | .043 | 1595 (28.1) | 8 (28.6) | 1.00 (referent) | .285 |
| Former drinker | 520 (4.0) | 6 (5.2) | 2.39 (0.89 to 6.36) | 437 (9.7) | 5 (12.2) | 5.87 (1.74 to 19.77) | 252 (4.4) | 1 (3.6) | 1.22 (0.12 to 12.35) | |||
| Current drinker | 4147 (31.6) | 28 (24.3) | 0.95 (0.51 to 1.80) | 2006 (44.6) | 16 (39.0) | 2.48 (0.99 to 6.19) | 1744 (30.8) | 8 (28.6) | 0.75 (0.18 to 3.14) | |||
| Drinker, status unknown | 3891 (29.7) | 30 (26.1) | 0.93 (0.50 to 1.74) | 1 (0.0) | 0 (0.0) | — | 1335 (23.5) | 6 (21.4) | 0.29 (0.08 to 1.00) | |||
| Age at first alcohol consumption | ||||||||||||
| Nondrinker | 3627 (27.7) | 34 (29.6) | 1.00 (referent) | .553 | 1134 (25.2) | 8 (19.5) | 1.00 (referent) | .005 | 1595 (28.1) | 8 (28.6) | 1.00 (referent) | .443 |
| <21 y | 2005 (15.3) | 20 (17.4) | 1.38 (0.70 to 2.73) | 1268 (28.2) | 15 (36.6) | 4.36 (1.61 to 11.80) | 1189 (21.0) | 5 (17.9) | 0.48 (0.14 to 1.63) | |||
| ≥21 y | 3445 (26.3) | 17 (14.8) | 0.80 (0.39 to 1.64) | 571 (12.7) | 6 (14.6) | 2.40 (0.77 to 7.52) | 1487 (26.2) | 4 (14.3) | 0.37 (0.10 to 1.34) | |||
| Drinker, age start unknown | 3108 (23.7) | 27 (23.5) | 0.94 (0.47 to 1.89) | 605 (13.5) | 0 (0.0) | — | 655 (11.6) | 6 (21.4) | 0.61 (0.11 to 3.39) | |||
| Duration of alcohol consumption | ||||||||||||
| Nondrinker | 3627 (27.7) | 34 (29.6) | 1.00 (referent) | .462 | 1134 (25.2) | 8 (19.5) | 1.00 (referent) | .010 | 1595 (28.1) | 8 (28.6) | 1.00 (referent) | .428 |
| 1–20 y | 1195 (9.1) | 22 (19.1) | 1.47 (0.73 to 2.96) | 331 (7.4) | 12 (29.3) | 3.56 (1.22 to 10.40) | 375 (6.6) | 1 (3.6) | 0.72 (0.06 to 8.91) | |||
| ≥21 y | 3837 (29.3) | 16 (13.9) | 1.25 (0.59 to 2.68) | 1507 (33.5) | 9 (22.0) | 3.29 (1.05 to 10.36) | 1016 (17.9) | 2 (7.1) | 0.78 (0.10 to 5.88) | |||
| Drinker, duration unknown | 3526 (26.9) | 26 (22.6) | 0.71 (0.37 to 1.35) | 606 (13.5) | 0 (0.0) | — | 1940 (34.2) | 12 (42.9) | 0.39 (0.14 to 1.13) | |||
| Blood transfusion | ||||||||||||
| No | 8448 (74.7) | 95 (91.3) | 1.00 (referent) | .027 | 4551 (79.5) | 45 (97.8) | 1.00 (referent) | .022 | 4461 (82.0) | 23 (95.8) | 1.00 (referent) | .327 |
| Yes | 1616 (14.3) | 4 (3.8) | 0.4 (0.14 to 1.03) | 896 (15.7) | 1 (2.2) | 0.2 (0.02 to 1.29) | 862 (15.9) | 1 (4.2) | 0.4 (0.06 to 3.12) | |||
* CI = confidence interval; OR = odds ratio.
† Adjusted for age, sex, race/ethnicity, and study.
An increased risk of ALL was observed in those with a family history of a hematological malignancy (OR = 2.6, 95% CI = 1.22 to 5.54). This risk factor also approached significance for B-cell ALL (OR = 3.3, 95% CI = 1.10 to 9.91). An elevated OR also was seen for T-cell ALL, but the risk estimate was imprecise. These are novel findings for adult ALL but are consistent with previous reports of large population and case–control studies of other lymphoid cancers (21,22).
Although based on very small numbers, those who worked in the leather industry (four cases and 146 controls) had an increased risk of ALL (OR = 3.91, 95% CI = 1.35 to 11.35), and this risk was also evident for B-cell ALL (OR = 5.33, 95% CI = 1.17 to 24.2) and approached statistical significance for T-cell ALL (OR = 3.81, 95% CI = 0.85 to 17.0). An elevated risk of ALL also was observed in textile workers in the sewing and embroidery industry (OR = 2.92, 95% CI = 1.00 to 8.49). Despite the small numbers (four cases and 191 controls), this association was observed for B-cell ALL (OR = 4.38, 95% CI = 1.41 to 13.6), but not for T-cell ALL. Previous studies have also reported an increased risk of NHL in workers in textile-related occupations (23–25) and of acute myeloid leukemia in those in shoe or other leather goods industries (26). However, there have been no previous reports linking textile and sewing/embroidery occupations with risk of adult ALL. There are also no reports of parental occupations in the textile or leather industries and risk of childhood ALL.
We also observed an increased risk of B-cell ALL in ever versus never consumers of alcohol (OR = 2.87, 95% CI = 1.18 to 6.95), whether they were former (OR = 5.87, 95% CI = 1.74 to 19.8) or current drinkers (OR = 2.48, 95% CI = 0.99 to 6.19), and in those who started drinking before 21 years (OR = 4.36, 95% CI = 1.61 to 11.8). No evidence of associations were observed between any alcohol variables and T-cell ALL. Based on a meta-analysis of 21 childhood leukemia case–control studies, maternal alcohol consumption during pregnancy was associated with a significantly increased risk of childhood acute myeloid leukemia, but not ALL (27), suggesting potentially different modes of action related to alcohol exposure in the pathogenesis of adult and childhood acute leukemia.
A suggestive inverse association with ALL risk was found for individuals who had ever received a blood transfusion compared with those who were never transfused, though this analysis was based on only four cases (OR = 0.4, 95% CI = 0.14 to 1.03). This finding is consistent with InterLymph reports for other lymphomas, as described elsewhere in this issue, but inconsistent with previous reports of positive associations with blood transfusions (28). If real, the mechanism of action for this protective effect in transfused individuals remains to be elucidated.
Previous childhood ALL studies have reported positive associations with exposures to herbicides (9), benzene (10), and maternal hair dyes (29). We did not find evidence of associations for adult ALL with any farming variables linked to herbicide exposure (Supplementary Table 1, available online). Moreover, no evidence of an association was found in petroleum workers, painters, or engine mechanics as surrogates for benzene exposure or with exposure to hair dyes. Overall, no evidence of associations was found with other lifestyle, medical history, family history, or occupational risk factors (Supplementary Table 1, available online). However, because of the relatively small numbers of cases, this study may be underpowered to detect differences in these other exposures under study.
In summary, we performed exploratory analyses investigating a variety of potential risk factors for ALL. Statistically significant or suggestive increased risks of ALL were observed in those with a family history of hematological malignancy, working in the leather or textile industries, and alcohol consumption, whereas having one or more blood transfusions was protective. Small sample size limited our power to truly test differences between B- and T-cell ALL. Although the results of this pooled analysis indicate some novel risk factors for adult ALL, multiple testing was not adjusted for and larger studies will be needed to further investigate these associations and rule out explanations other than a direct causal relationship, such as reverse causality, and selection or recall bias. Ideally, these studies will include prospective studies to address potential issues of temporality and misclassification as well as the inclusion of inherited variants in multivariate models.
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.
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, 2009SGR1026), 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 (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 (United Kingdom); and Australian National Health and Medical Research Council (ID990920), Cancer Council NSW, and University of Sydney Faculty of Medicine (New South Wales).
Supplementary Material
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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