Citations

Abstract Profound disruption of immune function is an established risk factor for non-Hodgkin lymphoma. We report here a large-scale evaluation of common genetic variants in immune genes and their role in lymphoma. We genotyped 57 single nucleotide polymorphisms (SNP) from 36 candidate immune genes in 1,172 non-Hodgkin lymphoma cases and 982 population-based controls from a US multicenter study. We calculated odds ratios (OR) and 95% confidence intervals (95% CI) for the association between individual SNP and haplotypes with non-Hodgkin lymphoma overall and five well-defined subtypes. A haplotype comprising SNPs in two proinflammatory cytokines, tumor necrosis factor-α and lymphotoxin-α (rs1800629, rs361525, rs1799724, rs909253, and rs2239704), increased non-Hodgkin lymphoma risk overall (OR, 1.31; 95% CI, 1.06-1.63; P = 0.01) and notably for diffuse large B cell (OR, 1.64; 95% CI, 1.23-2.19; P = 0.0007). A functional nonsynonymous SNP in the innate immune gene Fcγ receptor 2A (FCGR2A; rs1801274) was also associated with non-Hodgkin lymphoma; AG and AA genotypes were associated with a 1.26-fold (95% CI, 1.01-1.56) and 1.41-fold (95% CI, 1.10-1.81) increased risk, respectively (Ptrend = 0.006). Among non-Hodgkin lymphoma subtypes, the association with FCGR2A was pronounced for follicular and small lymphocytic lymphomas. In conclusion, common variants in genes influencing proinflammatory and innate immune responses were associated with non-Hodgkin lymphoma risk overall and their effects could vary by subtype. Our results require replication but potentially provide important clues for investigating common genetic variants as susceptibility factors and in disease outcomes, treatment responses, and immunotherapy targets. (Cancer Res 2006; 66(19): 9771-80)

AbstractThe identification and characterization of mouse basophils have historically been hampered by the extreme rarity of this cell type. Virtually no photomicrographs of hematologically stained (eg, Wright-Giemsa) examples of mouse basophils exist in the literature. However, 4 recent studies in the past 2 years have used flow cytometry and a defined set of cell-surface markers to identify and subsequently isolate mouse “basophils,” including the publication of stained cytospin preparations of these cells. Surprisingly, a reevaluation of the data from all 4 of the studies revealed several issues of concern that suggest that the cells under study are not necessarily basophils. Nonetheless, we propose that these studies do provide the foundation for a reevaluation of the defining characteristics of a basophil and/or provide support for the provocative conclusion that a new previously overlooked leukocyte subtype has been identified. The purpose of this commentary is to revisit these previously published studies, highlight the relevant issues, and provide a different perspective in the hope of developing a consensus within the research community as to the true identity of the “basophils” described in these studies.

OBJECTIVE—The degree to which chronic glycemic exposure (CGE) (fasting plasma glucose [FPG], HbA1c [A1C], duration of diabetes, age at onset of diabetes, or combinations of these) is associated with or predicts the severity of microvessel complications is unsettled. Specifically, we test whether combinations of components correlate and predict complications better than individual components. RESEARCH DESIGN AND METHODS—Correlations and predictions of CGE and complications were assessed in the Rochester Diabetic Neuropathy Study, a population-based, cross-sectional, and longitudinal epidemiologic survey of 504 patients with diabetes followed for up to 20 years. RESULTS—In multivariate analysis, A1C and duration of diabetes (and to a lesser degree age at onset of diabetes but not FPG) were the main significant CGE risk covariates for complications. A derived glycemic exposure index (GEi) correlated with and predicted complications better than did individual components. Composite or staged measures of polyneuropathy provided higher correlations and better predictions than did dichotomous measures of whether polyneuropathy was present or not. Generally, the mean GEi was significantly higher with increasing stages of severity of complications. CONCLUSIONS—A combination of A1C, duration of diabetes, and age at onset of diabetes (a mathematical index, GEi) correlates significantly with complications and predicts later complications better than single components of CGE. Serial measures of A1C improved the correlations and predictions. For polyneuropathy, continuous or staged measurements performed better than dichotomous judgments. Even with intensive assessment of CGE and complications over long times, only about one-third of the variability of the severity of complications is explained, emphasizing the role of other putative risk covariates.