The Updated AJCC/TNM Staging System for Papillary Thyroid Cancer (8th Edition): From the Perspective of Genomic Analysis.

BACKGROUND: Recently, the American Joint Committee on Cancer published the 8th edition of its Cancer Staging Manual with major changes regarding the staging of thyroid cancer, including the raising of the age cutoff from 45 to 55 years. Using the clinical and genetic data of 505 papillary thyroid cancer (PTC) cases, we aimed to compare overall survival (OS) and recurrence-free survival (RFS) with different age cutoff values, and also investigate the efficacy of the new staging system on a genomic level. METHODS: We downloaded gene expression data, somatic mutation profile, copy number alteration data and clinical data of 505 PTC patients from The Cancer Genome Atlas data portal. We used multiple statistical analysis and multiplatform genomic analysis to evaluate the efficacy of the 8th edition. RESULTS: When using 55 years as the cutoff value for analyzing RFS, the Kaplan-Meier plot showed a significant p value but not when using 45 years (p = 0.006 vs. p = 0.493), but both cutoff values were significant when analyzing OS (p = 1.1 × 10-9 with age 55 vs. p = 4.4 × 10-5 with age 45). When looking at stage-dependent survival, both the 7th and 8th edition had significant p values (p = 0.048 vs. p = 3.1 × 10-9 in RFS and p = 5.9 × 10-10 vs. p = 2.2 × 10-10 in OS). Multiplatform genomic analysis showed patients ≥55 years had 103 differently expressed genes when compared with other age groups. Signaling pathway analysis revealed that patients ≥55 years had altered pathways associated with aggressiveness of thyroid cancer. CONCLUSION: In conclusion, this is the first study to show clinical and genetic evidence supporting the altered age cutoff point of 55 years in the AJCC 8th edition for PTC patients.

Predictive gene signatures of nodal metastasis in papillary thyroid carcinoma.

BACKGROUND: Cervical lymph node metastases (LNM) in papillary thyroid carcinomas (PTCs) are common and develop in approximately 30-80% of PTCs. The presence of cervical LNM significantly increases the rate of locoregional recurrence in PTCs. OBJECTIVE: To search for predictive gene signatures for nodal metastasis in PTCs. METHODS: We used unsupervised clustering with unbiased manner to compare molecular profiles between PTCs with nodal metastasis and PTCs without nodal metastasis using mRNA-seq of TCGA data. Using gene ontology (GO) and logistic regression test, we generated 12-predictive genes for nodal metastasis in PTCs. RESULTS: Unsupervised clustering of mRNA-seq (training set, N = 158) revealed that PTCs with nodal metastasis showed different gene expression patterns compared to PTCs without nodal metastasis. We generated 12 predictive genes and these gene signatures showed consistency for predicting nodal metastasis when we applied them to a validation set (N = 80). Based on multivariate analysis, these 12 predictive gene signatures showed more significant odds ratio compared to other variables. CONCLUSIONS: These 12 gene signatures could be used to predict the chance of nodal metastasis in PTCs in preoperative evaluation using fine needle aspiration biopsy (FNAB) so that appropriate plan such as central neck dissection could be made.

Three distinct genomic subtypes of head and neck squamous cell carcinoma associated with clinical outcomes.

OBJECTIVES: Heterogeneity of head and neck squamous cell carcinomas (HNSCCs) results in unpredictable outcomes for patients with similar stages of cancer. Beyond the role of human papilloma virus (HPV), no validated molecular marker of HNSCCs has been established. Thus, clinically relevant molecular subtypes are needed to optimize HNSCC therapy. The purpose of this study was to identify subtypes of HNSCC that have distinct biological characteristics associated with clinical outcomes and to characterize genomic alterations that best reflect the biological and clinical characteristics of each subtype. MATERIALS AND METHODS: We analyzed gene expression profiling data from pan-SCC tissues including cervical SCC, esophageal SCC, lung SCC, and HNSCC (n = 1346) to assess the similarities and differences among SCCs and to identify molecular subtypes of HNSCC associated with prognosis. Subtype-specific gene expression signatures were identified and used to construct predictive models. The association of the subtypes with prognosis was validated in two independent cohorts of patients. RESULTS: Pan-SCC analysis identified three novel subtypes of HNSCC. Subtype 1 had the best prognosis and was similar to cervical SCC, whereas subtype 3 had the worst prognosis and was similar to lung SCC. Subtype 2 had a moderate prognosis. The 600-gene signature associated with the three subtypes significantly predicted prognosis in two independent validation cohorts. These three subtypes also were associated with potential benefit of immunotherapy. CONCLUSION: We identified three clinically relevant HNSCC molecular subtypes. Independent prospective studies to assess the clinical utility of the subtypes and associated gene signature are warranted.