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M.M. Epstein et al.
dicted risk of an AIDS-NHL diagnosis in HIV-positive per- sons,2-4 in some instances as early as five years pre-diagno- sis.5 Several of these markers have also demonstrated an association with NHL risk in immunocompetent people in prospective studies.6-13 Of interest, plasma sCD30 levels were positively associated with NHL risk at 6-10 years9 and even 15-23 years pre-diagnosis.11 Another small nest- ed case-control study reported a significant 2.5-fold increase in NHL risk in women with elevated sol- uble IL-2 receptor-α levels (sIL-2Rα; a marker of T-cell activation and IL-2 upregulation), and marginally signifi- cant increases in NHL risk in women with higher pre-diag- nosis tumor necrosis factor (TNF)-α and soluble TNF- receptor-2 (sTNF-R2) levels.14 These findings collectively suggest that chronic B-cell stimulation has a role in lym- phomagenesis in immunocompetent persons.
Our study aimed to further characterize pre-diagnosis plasma immune marker profiles associated with risk of HIV-unrelated NHL and its major histological subtypes in two large US cohorts. This study represents one of the largest populations with prospectively collected pre-diag- nosis blood samples to investigate the association between numerous immune markers and NHL risk, including those with specific NHL subtypes that are often precluded due to small sample size, and to assess the inde- pendence of biomarker-NHL associations for multiple immune markers.11,12 The long-term follow up of the study population also allowed for examination of the influence of time since blood draw on observed immune marker- NHL associations, including an assessment of potential early markers of lymphomagenesis present ten years or more prior to diagnosis. The choice of immune markers was guided in part by the immune deregulation we sought to characterize and by reported findings in AIDS- or HIV- unrelated NHL. We hypothesized that pre-diagnosis levels of immune markers indicative of B-cell activation or inflammation would be positively associated with risk of developing NHL and major NHL subtypes, and that the use of multi-marker models will enhance characterization of the immune milieu associated with NHL risk and sug- gest subtle differences by histological subtype.
Methods
Study population
The study population comprised Nurses’ Health Study (NHS, all female) and Health Professionals Follow-up Study (HPFS, all male) participants with archived plasma (Online Supplementary Methods).15,16 Cancer diagnoses were identified via routine ques- tionnaires or follow up after death17,18 and confirmed by medical record review or tumor registry linkage.
Participants provided written informed consent at blood collec- tion. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health.
Case and control selection
We included all participants with confirmed incident NHL diag- nosed three months or more after blood draw through 31st December 2010 with no other cancer history. Study pathologists (JCA, SJR) classified NHL histological subtype19 according to World Health Organization20,21 and International Lymphoma Epidemiology (InterLymph) Consortium guidelines.22,23 We ana-
lyzed common B-cell (B-)NHL subtypes individually [diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL)], combined less common B-NHLs (“other B-NHL”) and defined additional categories by cell type (T-NHL, B-NHL). We matched one control per case by sex (cohort), age, race, and blood draw details (Online Supplementary Methods).
Biomarker assessment
Assays were performed at the University of California, Los Angeles (LM, OMM), using multiplexed kits (Fluorokine® MAP, R & D Systems, Minneapolis, MN, USA), a Bio-Plex 200 Luminex instrument and Bio-Plex analysis software (Bio-Rad, Hercules, CA, USA). Blinded laboratory personnel measured sCD30, sIL-2Rα, B- cell activating factor of the TNF family (BAFF, a B-cell stimulatory cytokine), CXCL13, sIL-6Rα, sGP130, sCD14, sTNF-R2, C-reac- tive protein (CRP), IL-6, IL-8, IL-10, and TNF-α concentration according to the manufacturer's directions (Online Supplementary Methods). We set TNF-α, IL-8 and CXCL13 values to missing for samples with >24-hour processing delays (NHS: n=35; HPFS: n=23). Analyte concentrations were natural log-transformed for all analyses. We observed similar measured biomarker concentra- tions for the NHS and HPFS (Online Supplementary Table S1) and pooled the data.
Statistical analysis
We conducted batch calibration to diminish the potential influ- ence of laboratory batch-related variability on biomarker-NHL associations.24 Outlying biomarker values were identified using the Rosner extreme Studentized deviate method25 and omitted from analyses of the marker.
The primary analysis assessed batch effect-corrected, log-trans- formed biomarker values continuously per Standard Deviation (SD) increase in concentration, with SD units calculated for log- transformed values in the pooled controls. We calculated Odds Ratios (OR) and 95% Confidence Intervals (CI) for the association of each biomarker with NHL risk (overall and for DLBCL, FL, CLL/SLL, other B-NHL, all B-NHL and all T-NHL) using uncondi- tional logistic regression. Models adjusted for all matching factors unless small cell counts precluded adjustment for race. We evalu- ated but did not observe confounding by body mass index (BMI) and autoimmune disease history.
We intended a priori to identify multi-marker profiles associated with NHL risk via mutual adjustment of models for biomarkers that were individually associated. We also examined models strat- ified by follow-up interval (0 to <5, 5 to <10, ≥10 years) and assessed heterogeneity by time period using the contrast test.26 The Online Supplementary Methods describe additional analyses designed post hoc.
Results
In total, 601 cases of NHL (345 NHS and 256 HPFS) were identified and individually matched to controls. Three cases were later excluded due to unconfirmed lym- phoma status. The final analysis thus included 598 cases, including 114 DLBCL, 92 FL, 165 CLL/SLL, 132 other B- NHL (4 Burkitt lymphoma, 19 lymphoplasmacytic lym- phoma, 20 mantle cell lymphoma, 44 marginal zone lym- phoma, 20 other B-NHL, and 25 unclassified B-NHL) and 30 T-NHL, and 601 controls. The study population was 96% Caucasian and 58% female. Cases and controls had similar covariable distributions, due in part to the matched design (Table 1).
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