Mutation in Genes Encoding Key Functional Groups Additively Increase Mortality in Patients with BRAFV600E-Mutant Advanced Papillary Thyroid Carcinoma

The prognostic power of gene mutations in thyroid cancer

The introduction and generalization of next-generation sequencing techniques have significantly increased the identification of mutations in thyroid tumors from multiple patient cohorts. The understanding of the association between specific mutations and clinical outcomes is gradually leading to individualizing the care of patients with thyroid cancer. BRAFV600 is the most common mutation seen in thyroid cancer patients and unequivocally predicts malignancy, but when considered in isolation, it is not recommended to be used as an independent prognostic factor. Mutations in RAS are the second most common alterations in thyroid cancer but can be found in benign and malignant lesions. Rearrangements involving receptor tyrosine kinases, primarily RET, are found in a subset of thyroid tumors without mutations in either BRAF or RAS. The assessment of additional mutations is increasingly employed in thyroid cancer prognostication. The coexistence of BRAF with alterations in genes such as PIK3CA, TERT promoter, or TP53 is associated with less favorable outcomes. Similar studies have also shown that additional oncogenic mutations in RAS-mutant thyroid carcinoma, such as those affecting the EIF1AX gene, likely predict a more aggressive clinicopathologic behavior. Overall, emerging evidence suggests that the co-occurrence of specific alterations in defined genes with BRAF or RAS mutations can become prognostic tools and useful predictors of thyroid tumor aggressiveness.

Genomic alterations in thyroid cancer: biological and clinical insights

Tumours can arise from thyroid follicular cells if they acquire driver mutations that constitutively activate the MAPK signalling pathway. In addition, a limited set of additional mutations in key genes drive tumour progression towards more aggressive and less differentiated disease. Unprecedented insights into thyroid tumour biology have come from the breadth of thyroid tumour sequencing data from patients and the wide range of mutation-specific mechanisms identified in experimental models, in combination with the genomic simplicity of thyroid cancers. This knowledge is gradually being translated into refined strategies to stratify, manage and treat patients with thyroid cancer. This Review summarizes the biological underpinnings of the genetic alterations involved in thyroid cancer initiation and progression. We also provide a rationale for and discuss specific examples of how to implement genomic information to inform both recommended and investigational approaches to improve thyroid cancer prognosis, redifferentiation strategies and targeted therapies.

Radioiodine-refractory differentiated thyroid cancer: Molecular mechanisms and therapeutic strategies for radioiodine resistance

Radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) is difficult to treat with radioactive iodine because of the absence of the sodium iodide transporter in the basement membrane of thyroid follicular cells for iodine uptake. This is usually due to the mutation or rearrangement of genes and the aberrant activation of signal pathways, which result in abnormal expression of thyroid-specific genes, leading to resistance of differentiated thyroid cancer cells to radioiodine therapy. Therefore, inhibiting the proliferation and growth of RAIR-DTC with multikinase inhibitors and other drugs or restoring its differentiation and then carrying out radioiodine therapy have become the first-line treatment strategies and main research directions. The drugs that regulate these kinases or signaling pathways have been studied in clinical and preclinical settings. In this review, we summarized the major gene mutations, gene rearrangements and abnormal activation of signaling pathways that led to radioiodine resistance of RAIR-DTC, as well as the medicine that have been tested in clinical and preclinical trials.

Integrated analysis of tumor microenvironment features to establish a diagnostic model for papillary thyroid cancer using bulk and single-cell RNA sequencing technology

BACKGROUND

Characterizing tumor microenvironment using single-cell RNA sequencing has been a promising strategy for cancer diagnosis and treatment. However, a few studies have focused on diagnosing papillary thyroid cancer (PTC) through this technology. Therefore, our study explored tumor microenvironment (TME) features and identified potential biomarkers to establish a diagnostic model for papillary thyroid cancer.

METHODS

The cell types were identified using the markers from the CellMarker database and published research. The CellChat package was conducted to analyze the cell-cell interaction. The SCEVAN package was used to identify malignant thyroid cells. The SCP package was used to perform multiple single-cell downstream analyses, such as GSEA analysis, enrichment analysis, pseudotime trajectory analysis, and differential expression analysis. The diagnostic model of PTC was estimated using the calibration curves, receiver operating characteristic curves, and decision curve analysis. RT-qPCR was performed to validate the expression of candidate genes in human papillary thyroid samples.

RESULTS

Eight cell types were identified in the scRNA-seq dataset by published cell markers. Extensive cell-cell interactions like FN1/ITGB1 existed in PTC tissues. We identified 26 critical genes related to PTC progression. Further, eight subgroups of PTC tumor cells were identified and exhibited high heterogeneity. The MDK/LRP1, MDK/ALK, GAS6/MERTK, and GAS6/AXL were identified as potential ligand-receptor pairs involved in the interactions between fibroblasts/endothelial cells and tumor cells. Eventually, the diagnostic model constructed by TRPC5, TENM1, NELL2, DMD, SLC35F3, and AUTS2 showed a good efficiency for distinguishing the PTC and normal tissues.

CONCLUSIONS

Our study comprehensively characterized the tumor microenvironment in papillary thyroid cancer. Through combined analysis with bulk RNA-seq, six potential diagnostic biomarkers were identified and validated. The diagnostic model we constructed was a promising tool for PTC diagnosis. Our findings provide new insights into the heterogeneity of thyroid cancer and the theoretical basis for diagnosing thyroid cancer.

Clinical Implications of mTOR Expression in Papillary Thyroid Cancer—A Systematic Review

Papillary thyroid cancer (PTC) comprises approximately 80% of all thyroid malignancies. Although several etiological factors, such as age, gender, and irradiation, are already known to be involved in the development of PTC, the genetics of cancerogenesis remain undetermined. The mTOR pathway regulates several cellular processes that are critical for tumorigenesis. Activated mTOR is involved in the development and progression of PTC. Therefore, we performed a systematic review of papers studying the expression of the mTOR gene and protein and its relationship with PTC risk and clinical outcome. A systematic literature search was performed using PubMed, Embase, and Scopus databases (the search date was 2012–2022). Studies investigating the expression of mTOR in the peripheral blood or tissue of patients with PTC were deemed eligible for inclusion. Seven of the 286 screened studies met the inclusion criteria for mTOR gene expression and four for mTOR protein expression. We also analyzed the data on mTOR protein expression in PTC. We analyzed the association of mTOR expression with papillary thyroid cancer clinicopathological features, such as the TNM stage, BRAF V600E mutation, sex distribution, lymph node and distant metastases, and survival prognosis. Understanding specific factors involved in PTC tumorigenesis provides opportunities for targeted therapies. We also reviewed the possible new targeted therapies and the use of mTOR inhibitors in PTC. This topic requires further research with novel techniques to translate the achieved results to clinical application.

Development of Novel Murine BRAFV600E-Driven Papillary Thyroid Cancer Cell Lines for Modeling of Disease Progression and Preclinical Evaluation of Therapeutics

The Cancer Genome Atlas study in thyroid cancer exposed the genomic landscape of ~500 PTCs and revealed BRAFV600E-mutant tumors as having different prognosis, contrasting indolent cases and those with more invasive disease. Here, we describe the generation and characterization of six novel BRAFV600E-driven papillary thyroid cancer (PTC) cell lines established from a BrafV600E+/-/Pten+/-/TPO-Cre mouse model that spontaneously develop thyroid tumors. The novel cell lines were obtained from animals representing a range of developmental stages and both sexes, with the goal of establishing a heterogeneous panel of PTC cell lines sharing a common driver mutation. These cell lines recapitulate the genetics and diverse histopathological features of BRAFV600E-driven PTC, exhibiting differing degrees of growth, differentiation, and invasive potential that may help define mechanisms of pathogenesis underlying the heterogeneity present in the patient population. We demonstrate that these cell lines can be used for a variety of in vitro applications and can maintain the potential for in vivo transplantation into immunocompetent hosts. We believe that these novel cell lines will provide powerful tools for investigating the molecular basis of thyroid cancer progression and will lead to the development of more personalized diagnostic and treatment strategies for BRAFV600E-driven PTC.

Integrated analysis of fibroblasts molecular features in papillary thyroid cancer combining single-cell and bulk RNA sequencing technology

BACKGROUND

Papillary thyroid cancer (PTC) is the most common pathological type of thyroid cancer with a high incidence globally. Increasing evidence reported that fibroblasts infiltration in cancer was correlated with prognostic outcomes. However, fibroblasts related study in thyroid cancer remains deficient.

METHODS

Single-cell sequencing data of PTC were analyzed by Seurat R package to explore the ecosystem in PTC and identify fibroblasts cluster. The expression profiles and prognostic values of fibroblast related genes were assessed in TCGA dataset. A fibrosis score model was established for prognosis prediction in thyroid cancer patients. Differentially expressed genes and functional enrichment between high and low fibrosis score groups in TCGA dataset were screened. The correlation of immune cells infiltration and fibrosis score in thyroid cancer patients was explored. Expression levels and prognostic values of key fibroblast related factor were validated in clinical tissues another PTC cohort.

RESULTS

Fibroblasts were highly infiltrated in PTC and could interact with other type of cells by single-cell data analysis. 34 fibroblast related terms were differentially expressed in thyroid tumor tissues. COX regression analysis suggested that the constructed fibrosis score model was an independent prognostic predictor for thyroid cancer patients (HR = 5.17, 95%CI 2.31-11.56, P = 6.36E-05). Patients with low fibrosis scores were associated with a significantly better overall survival (OS) than those with high fibrosis scores in TCGA dataset (P = 7.659E-04). Specific immune cells infiltration levels were positively correlated with fibrosis score, including monocytes, M1 macrophages and eosinophils.

CONCLUSION

Our research demonstrated a comprehensive horizon of fibroblasts features in thyroid cancer microenvironment, which may provide potential value for thyroid cancer treatment.