PURPOSE: The microenvironment influences outcome in follicular lymphoma. Our hypothesis was that several immune cell subsets are important for disease outcome and their individual prognostic importance should be demonstrable in the same analysis and in competition with clinical factors. EXPERIMENTAL DESIGN: Seventy follicular lymphoma patients with extreme clinical outcome ("poor" and "good" cases) were selected in a population-based cohort of 197. None of the 37 good-outcome patients died from lymphoma, whereas all the 33 poor-outcome patients succumbed in <or=5 years. Furthermore, the good-outcome patients were followed for a long time and needed no or little treatment. A tissue microarray was constructed from diagnostic, pretreatment biopsies. Cellular subsets were quantified after immunostaining, using computerized image analysis, separating cells inside and outside the follicles (follicular and interfollicular compartments). Flow cytometry data from the same samples were also used. RESULTS: Independently of the Follicular Lymphoma International Prognostic Index, CD4(+) cells were associated with poor outcome and programmed death-1-positive and CD8(+) cells were associated with good outcome. The prognostic values of CD4(+) and programmed death-1-positive cells were accentuated when they were follicular and that of CD8(+) cells were accentuated when they were interfollicular. Follicular FOXP3(+) cells were associated with good outcome and interfollicular CD68(+) cells were associated with poor outcome. Additionally, high CD4/CD8 and CD4 follicular/interfollicular ratios correlated with poor outcome. CONCLUSION: There are many important immune cell subsets in the microenvironment of follicular lymphoma. Each of these is independently associated with outcome. This is the first study showing the effect of the balance of the entire microenvironment, not only of individual subsets.
PURPOSE: The microenvironment influences outcome in follicular lymphoma. Our hypothesis was that several immune cell subsets are important for disease outcome and their individual prognostic importance should be demonstrable in the same analysis and in competition with clinical factors. EXPERIMENTAL DESIGN: Seventy follicular lymphomapatients with extreme clinical outcome ("poor" and "good" cases) were selected in a population-based cohort of 197. None of the 37 good-outcome patients died from lymphoma, whereas all the 33 poor-outcome patients succumbed in <or=5 years. Furthermore, the good-outcome patients were followed for a long time and needed no or little treatment. A tissue microarray was constructed from diagnostic, pretreatment biopsies. Cellular subsets were quantified after immunostaining, using computerized image analysis, separating cells inside and outside the follicles (follicular and interfollicular compartments). Flow cytometry data from the same samples were also used. RESULTS: Independently of the Follicular Lymphoma International Prognostic Index, CD4(+) cells were associated with poor outcome and programmed death-1-positive and CD8(+) cells were associated with good outcome. The prognostic values of CD4(+) and programmed death-1-positive cells were accentuated when they were follicular and that of CD8(+) cells were accentuated when they were interfollicular. Follicular FOXP3(+) cells were associated with good outcome and interfollicular CD68(+) cells were associated with poor outcome. Additionally, high CD4/CD8 and CD4 follicular/interfollicular ratios correlated with poor outcome. CONCLUSION: There are many important immune cell subsets in the microenvironment of follicular lymphoma. Each of these is independently associated with outcome. This is the first study showing the effect of the balance of the entire microenvironment, not only of individual subsets.
Authors: Jason R Westin; Fuliang Chu; Min Zhang; Luis E Fayad; Larry W Kwak; Nathan Fowler; Jorge Romaguera; Fredrick Hagemeister; Michelle Fanale; Felipe Samaniego; Lei Feng; Veerabhadran Baladandayuthapani; Zhiqiang Wang; Wencai Ma; Yanli Gao; Michael Wallace; Luis M Vence; Laszlo Radvanyi; Tariq Muzzafar; Rinat Rotem-Yehudar; R Eric Davis; Sattva S Neelapu Journal: Lancet Oncol Date: 2013-12-11 Impact factor: 41.316
Authors: June H Myklebust; Jonathan M Irish; Joshua Brody; Debra K Czerwinski; Roch Houot; Holbrook E Kohrt; John Timmerman; Jonathan Said; Michael R Green; Jan Delabie; Arne Kolstad; Ash A Alizadeh; Ronald Levy Journal: Blood Date: 2013-01-07 Impact factor: 22.113