BACKGROUND: Reliable predictive biomarkers are required to address the challenge of disease recurrence after thyroid cancer surgery. For this study, the authors assessed the association of cellular-based and serum-based immunologic mediators with thyroid cancer recurrence. METHODS: Leukocyte subset counts and immune regulatory cytokine levels were determined in peripheral blood samples using multiparameter flow cytometry and 51-panel, multiplex enzyme-linked immunosorbent assays, respectively. The functional activity of circulating B-lymphocytes, T-lymphocytes, and natural killer lymphocytes was assessed ex vivo. Differences in mean biomarker levels between defined patient groups and correlations between biomarkers and cancer recurrence were assessed using t tests or Wilcoxon tests and by univariate and multivariate analyses with Cox models. Optimal cutoff values of significantly correlated biomarkers that best predicted disease recurrence after surgery were established by receiver operating characteristics and were validated by using an optimal cutpoint determination algorithm. RESULTS: In total, 35 patients were enrolled (median age, 49.4 year), including 24 women and 15 patients with recurrent disease; and there were 21 individuals in the control group. Patients without recurrence had higher levels of soluble FAS (tumor necrosis receptor superfamily, member 6) ligand (sFASL), transforming growth factor-β, regulatory T cells, and programmed death 1/ programmed death ligand 1-expressing leukocytes. sFASL (hazard ratio, 0.60; 95% confidence interval, 0.38-0.95; P = .031) and interferon-α (hazard ratio, 1.55; 95% confidence interval, 1.03-2.34; P = .038) were associated significantly with disease recurrence. There was a significant difference in progression-free survival between patient groups stratified by an sFASL optimal cutpoint of 15 pg/mL (log-rank P = .0009). CONCLUSIONS: sFASL and IFN-α levels were correlated significantly with thyroid cancer recurrence and may be useful for risk-adapted surveillance strategies in patients with thyroid cancer.
BACKGROUND: Reliable predictive biomarkers are required to address the challenge of disease recurrence after thyroid cancer surgery. For this study, the authors assessed the association of cellular-based and serum-based immunologic mediators with thyroid cancer recurrence. METHODS: Leukocyte subset counts and immune regulatory cytokine levels were determined in peripheral blood samples using multiparameter flow cytometry and 51-panel, multiplex enzyme-linked immunosorbent assays, respectively. The functional activity of circulating B-lymphocytes, T-lymphocytes, and natural killer lymphocytes was assessed ex vivo. Differences in mean biomarker levels between defined patient groups and correlations between biomarkers and cancer recurrence were assessed using t tests or Wilcoxon tests and by univariate and multivariate analyses with Cox models. Optimal cutoff values of significantly correlated biomarkers that best predicted disease recurrence after surgery were established by receiver operating characteristics and were validated by using an optimal cutpoint determination algorithm. RESULTS: In total, 35 patients were enrolled (median age, 49.4 year), including 24 women and 15 patients with recurrent disease; and there were 21 individuals in the control group. Patients without recurrence had higher levels of soluble FAS (tumor necrosis receptor superfamily, member 6) ligand (sFASL), transforming growth factor-β, regulatory T cells, and programmed death 1/ programmed death ligand 1-expressing leukocytes. sFASL (hazard ratio, 0.60; 95% confidence interval, 0.38-0.95; P = .031) and interferon-α (hazard ratio, 1.55; 95% confidence interval, 1.03-2.34; P = .038) were associated significantly with disease recurrence. There was a significant difference in progression-free survival between patient groups stratified by an sFASL optimal cutpoint of 15 pg/mL (log-rank P = .0009). CONCLUSIONS: sFASL and IFN-α levels were correlated significantly with thyroid cancer recurrence and may be useful for risk-adapted surveillance strategies in patients with thyroid cancer.
Authors: Elzbieta Izbicka; Robert T Streeper; Joel E Michalek; Christopher L Louden; Armando Diaz; David R Campos Journal: Cancer Genomics Proteomics Date: 2012-01 Impact factor: 4.069
Authors: N Mitsiades; V Poulaki; G Mastorakos; S T Tseleni-Balafouta; V Kotoula; D A Koutras; M Tsokos Journal: J Clin Endocrinol Metab Date: 1999-08 Impact factor: 5.958
Authors: Ha T T Phan; Pieter L Jager; Jacqueline E van der Wal; Wim J Sluiter; John T M Plukker; Rudi A J O Dierckx; Bruce H R Wolffenbuttel; Thera P Links Journal: Eur J Endocrinol Date: 2008-01 Impact factor: 6.664
Authors: Shahab Uddin; Prashant Bavi; Abdul K Siraj; Maqbool Ahmed; Maha Al-Rasheed; Azhar R Hussain; Mohammed Ahmed; Tarek Amin; Ali Alzahrani; Fouad Al-Dayel; Jehad Abubaker; Rong Bu; Khawla S Al-Kuraya Journal: Endocr Relat Cancer Date: 2010-02-18 Impact factor: 5.678
Authors: Hui Zhao; Jayeeta Roychoudhury; Teresa A Doggett; Rajendra S Apte; Thomas A Ferguson Journal: Invest Ophthalmol Vis Sci Date: 2013-08-07 Impact factor: 4.799
Authors: M E Peter; A Hadji; A E Murmann; S Brockway; W Putzbach; A Pattanayak; P Ceppi Journal: Cell Death Differ Date: 2015-02-06 Impact factor: 15.828