Multiplex analysis of cytokines as biomarkers that differentiate benign and malignant thyroid diseases

Abstract

Thyroid cancer incidence is increasing, and its diagnosis can be challenging. Fine needle biopsy, the principal clinical tool to make a tissue diagnosis, leads to inconclusive diagnoses in up to 30% of the cases, leading to surgery. Advances in proteomics are improving abilities to diagnose malignant conditions using small samples of tissue or body fluids. We hypothesized that analysis of serum growth factors would uncover diagnostically informative differences between benign and malignant thyroid conditions. Using xMAP profiling, we evaluated concentrations of 19 cytokines, chemokines, and growth factors. We used sera from 23 patients with cancer (Malignant group), 24 patients with benign nodular thyroid disease (Benign group), and 23 healthy subjects (Normal group). In univariate analysis, five factors (epithelial growth factor, hepatocyte growth factor, Interleukins-5 and -8, and regulated upon activation, normally T-expressed and presumably secreted (RANTES) distinguished subjects with thyroid disease from the Normal group. In multivariate analysis, the set {Interleukin-8, hepatocyte growth factor, monocyte-induced γ interferon, interleukin-12 p40} achieved noteworthy discrimination between Benign and Malignant groups (area under the receiver operating characteristics curve was 0.81 (95% confidence interval: 0.65-0.90)). Multiplex panels of serum biomarkers may be promising tools to diagnose cancer in patients presenting with evidence of nodular thyroid disease.

Figure 1

Dot plots of serum concentrations (in pg/mL) of the 19 factors subjected to statistical analysis. Green plus signs denote reference Normals; blue circles denote Benign samples; red triangles denote Malignant samples.

Figure 2

Empirical ROC curves, constructed from per patient averages of test-set prediction probabilities generated using logistic regression with permutation resampling. Data were resampled 10 000 times into training sets and test sets for logistic regression using panels of 2–4 factors. For each sample, the >2000 prediction probabilities resulting from when it was resampled into test sets were averaged together, and ROC curves were based on these per-patient averages. The four multivariate most informative markers used in the panels are ranked in order as follows: IL-8, HGF, MIG, and IL-12p40; rankings utilize the marker r² of Table 4. IL-5 was the univariately most discriminative marker, and was accordingly used univariately in the analysis.