Development of prognostic signatures for intermediate-risk papillary thyroid cancer

Abstract

Background: The incidence of Papillary thyroid carcinoma (PTC), the most common type of thyroid malignancy, has risen rapidly worldwide. PTC usually has an excellent prognosis. However, the rising incidence of PTC, due at least partially to widespread use of neck imaging studies with increased detection of small cancers, has created a clinical issue of overdiagnosis, and consequential overtreatment. We investigated how molecular data can be used to develop a prognostics signature for PTC.

Methods: The Cancer Genome Atlas (TCGA) recently reported on the genomic landscape of a large cohort of PTC cases. In order to decrease unnecessary morbidity associated with over diagnosing PTC patient with good prognosis, we used TCGA data to develop a gene expression signature to distinguish between patients with good and poor prognosis. We selected a set of clinical phenotypes to define an 'extreme poor' prognosis group and an 'extreme good' prognosis group and developed a gene signature that characterized these.

Results: We discovered a gene expression signature that distinguished the extreme good from extreme poor prognosis patients. Next, we applied this signature to the remaining intermediate risk patients, and show that they can be classified in clinically meaningful risk groups, characterized by established prognostic disease phenotypes. Analysis of the genes in the signature shows many known and novel genes involved in PTC prognosis.

Conclusions: This work demonstrates that using a selection of clinical phenotypes and treatment variables, it is possible to develop a statistically useful and biologically meaningful gene signature of PTC prognosis, which may be developed as a biomarker to help prevent overdiagnosis.

Keywords: Gene expression; Papillary thyroid cancer; Prognosis.

Fig. 1

Association of gene expression with prognosis within the TCGA papillary thyroid cancer study. Heatmap showing expression of the top 100 genes that were most differentailly regulated between papillary thyroid cancer patients of good (n = 51) and poor (n = 79) prognosis, tested using significance analysis of microarrays (SAM) analysis. Genes and samples are arranged by linkage distace, using unsupervised hierarchical clustering of average expression across samples and genes, respectively, as illustrated by dendrograms. Good and poor prognosis patients are represented by red and black squares within the sidebar

Fig. 2

Differential expression of genes between normal tissue, good prognosis and poor prognosis patient groups within the TCGA thyroid cancer study. Representative examples of genes that were identified as differentially expressed between good and poor prognosis tumors, and which were also differentially expressed between tumor and normal tissue. These genes displayed step-wise changes of expression between normal tissue, good prognosis tumors and poor prognosis tumors, which may be indicitive of incremented deregulation associated with advancing disease

Fig. 3

Performance of a gene-expression based supervised predictor in classifying prognosis. ROC curve illustratrating the performance of a gene-expression based supervised classifier in correctly predicting the prognostic group (good or poor prognosis) to which each patient belongs, over 10 rounds of cross-validation. The classifier was determined using Prediction of Microarray (PAM) analysis, and was limited to 100 genes, which were differentially expressed between good and poor prognosis patients

Fig. 4

Enrichment of clinicopathological prognositic features of papillary thryroid cancer within intermediate prognosis patients classified as good and poor prognosis by a Prediction of Microarrays (PAM) model. Intermediate prognosis patients (n = 378) were classified as either good (n = 111) or poor prognosis (n = 195) using the PAM model, which was trained using the gene signature of differential expression between extreme good prognosis (n = 51) and extreme poor prognosis (n = 79) patients. Within the poor prognosis group there was a higher percentage of patients with BRAF mutations, nodal involvement, extra-thyroid extension, and the aggressive Tall cell histological subtype, but a lower percentage of patients with NRAS and HRAS mutations and the well-differentiated follicular histological subtype. *** Chi -squared p-value < 0.001