title: Predictors of histology, tissue eosinophilia and mast cell infiltration in Hodgkin's lymphoma--a population-based study.
abstract: OBJECTIVE
  Classical Hodgkin's lymphoma (HL) lesions comprise few tumour cells, surrounded by numerous inflammatory cells. Like in other malignancies, the microenvironment is presumed to be clinically important in HL; however, microenvironment predictors remain poorly characterised. The aim of this study was to investigate how selected patient characteristics and genetic factors affect HL phenotype, in particular tissue eosinophilia, mast cell counts and HL histological subtype.
  
  
  METHODS
  In a population-based study, patients with HL were interviewed about potential HL risk factors. Available tumours, n=448, were classified histologically; the number of eosinophils and mast cells were estimated, and eosinophil cationic protein (ECP) and eosinophil protein-x (EPX) gene polymorphisms were determined. Associations were assessed in regression models.
  
  
  RESULTS
  Self-reported history of asthma was predictive of having tumour eosinophilia [≥200 eosinophils/10 high power fields, univariate odds ratio (OR)=2.22, 95% CI 1.06-4.64, P=0.03]. High numbers of eosinophils were predominantly seen in patients carrying the genotype ECP434GG [multivariate relative levels (RLs)=1.84, 95% CI 1.02-3.30, P=0.04]. Lower number of eosinophils was seen in Epstein-Barr virus (EBV)-positive tumours (univariate RL=0.52, 95% CI 0.3-0.9, P=0.02) and in older patients (univariate RL=0.85, 95% CI 0.73-0.99, P=0.03). Well-known factors such as young age, female sex and EBV-negative status predicted nodular sclerosis histology.
  
  
  CONCLUSIONS
  The number of eosinophils in HL tumours is influenced by patient traits such as asthma, ECP genotype and EBV status. EBV status was predictive of histology.
authors.affiliation: Department of Radiology, Oncology and Radiation Science Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden. ingrid.glimelius@onkologi.uu.se
authors.forename: Ingrid
authors.initials: I
authors.lastname: Glimelius
authors.forename: Jenny
authors.initials: J
authors.lastname: Rubin
authors.forename: Klaus
authors.initials: K
authors.lastname: Rostgaard
authors.forename: Rose-Marie
authors.initials: RM
authors.lastname: Amini
authors.forename: Martin
authors.initials: M
authors.lastname: Simonsson
authors.forename: Karina M
authors.initials: KM
authors.lastname: Sorensen
authors.forename: Karin E
authors.initials: KE
authors.lastname: Smedby
authors.forename: Per
authors.initials: P
authors.lastname: Venge
authors.forename: Henrik
authors.initials: H
authors.lastname: Hjalgrim
authors.forename: Daniel
authors.initials: D
authors.lastname: Molin
authors.forename: Gunilla
authors.initials: G
authors.lastname: Enblad
mesh_terms: D001249:Asthma; D002452:Cell Count; D003625:Data Collection; D004804:Eosinophils / Q000473:pathology*; D005260:Female; D005838:Genotype; D004854:Herpesvirus 4, Human; D006689:Hodgkin Disease / Q000453:epidemiology* / Q000473:pathology*; D006801:Humans; D008297:Male; D008407:Mast Cells / Q000473:pathology*; D011237:Predictive Value of Tests; D011379:Prognosis; D012307:Risk Factors