An analysis of trends of incidence and cytohistological correlation of papillary carcinoma of the thyroid gland with evaluation of discordant cases

A comparison of the WHO 2004 and WHO 2017 thyroid tumor classifications

INTRODUCTION

The category of borderline malignancy or unknown malignant potential was added to the WHO's 2017 classification of thyroid tumours. A new histological variety of papillary tumours and Hurthle cell tumours was given as a separate entity. The classification has also adopted the Turin criteria for histological diagnosis of poorly differentiated cancer (PDC).

SETTINGS AND DESIGN

Descriptive study.

METHODS AND MATERIAL

From July 2018 to June 2022, 200 thyroid neoplasm patients at a tertiary care facility in western Maharashtra were participated in the prospective research over a period of 4 years.

STATISTICAL ANALYSIS USED

The descriptive statistics were used to analyse the collected data.

AIM

This study was undertaken to compare the old (2004) and new (2016) WHO classifications and their importance in the treatment of thyroid malignancies.

RESULTS

Out of 200 cases, the age range of 31 to 40 years had the greatest number of cases. The ratio of females to males was 5:1. In our study, according to the WHO 2004 classification, malignant tumours comprised 57.5% of the cases, while benign tumours 42.5% of the cases. When tumours were subcategorized, the most frequent benign tumour was follicular adenoma (43.5%) and malignant tumour was papillary thyroid carcinoma (37%). Malignant tumours made up 47.5% of the cases when the tumours were reclassified using the revised WHO 2017 classification, followed by borderline tumours with 27.5% of the cases and benign tumours with 25% of the cases. The most frequent borderline tumour was NIFTP (Noninvasive follicular thyroid neoplasm with papillary-like nuclear features) (17.5%), the most prevalent malignant tumour was papillary carcinoma (including its variant) (32%), and the most frequent benign tumour was follicular adenoma (27%).

CONCLUSION

We concluded that the inclusion of the Boderline Category in the new WHO classification significantly improved thyroid cancer management. WHO 2017 classification prevents under diagnosis (in the case of benign tumors) and over diagnosis (in the case of malignant tumors).

Design, synthesis, and biological evaluation of aminopyridine derivatives as novel tropomyosin receptor kinase inhibitors

Tropomyosin receptor kinase (TRK) is a successful target for the treatment of various cancers caused by NTRK gene fusions. Herein, based on a rational drug design strategy, we designed and synthesized 35 aminopyrimidine derivatives that were shown to be TRKA inhibitors in the enzyme assay, among which compounds C3, C4, and C6 showed potent inhibitory activities against TRKA with IC₅₀ values of 6.5, 5.0, and 7.0 nM, respectively. In vitro antiproliferative activity study showed that compound C3 significantly inhibited the proliferation of KM-12 cells but had weak inhibitory effect on MCF-7 cells and HUVEC cells. The preliminary druggability evaluation showed that compound C3 exhibited favorable liver microsomal and plasma stabilities and had weak or no inhibitory activity against cytochrome P450 isoforms at 10 µM. Compounds C3, C4, and C6 were also selected for ADME (absorption, distribution, metabolism, and elimination) properties prediction and molecular docking studies. Inhibition experiments showed that compound C3 was not selective for TRK subtypes. All results indicated that compound C3 was a useful candidate for the development of TRK inhibitors.

Somatic Mutation Profiling of Papillary Thyroid Carcinomas by Whole-exome Sequencing and Its Relationship with Clinical Characteristics

The incidence of papillary thyroid carcinomas (PTCs) has increased rapidly during the past several decades. Until now, the mechanisms underlying the tumorigenesis of PTCs have remained largely unknown. Next-generation-sequencing (NGS) provides new ways to investigate the molecular pathogenesis of PTCs. To characterize the somatic alterations associated with PTCs, we performed whole-exome sequencing (WES) of PTCs from 23 Chinese patients. This study revealed somatic mutations in genes with relevant functions for tumorigenesis, such as BRAF, BCR, CREB3L2, DNMT1, IRS2, MSH6, and TP53. We also identified novel somatic gene alterations which may be potentially involved in PTC progression. Gene set enrichment analysis revealed that the cellular response to hormone stimulus, epigenetic modifications, such as protein/histone methylation and protein alkylation, as well as MAPK, PI3K-AKT, and FoxO/mTOR signaling pathways, were significantly altered in the PTCs studied here. Moreover, Protein-Protein Interaction (PPI) network analysis of our mutated gene selection highlighted EP300, KRAS, PTEN, and TP53 as major core genes. The correlation between gene mutations and clinicopathologic features of the PTCs defined by conventional ultrasonography (US) and contrast-enhanced ultrasonography (CEUS) were assessed. These analyses established significant associations between subgroups of mutations and respectively taller-than-wide, calcified, and peak time iso- or hypo-enhanced and metastatic PTCs. In conclusion, our study supplements the genomic landscape of PTCs and identifies new actionable target candidates and clinicopathology-associated mutations. Extension of this study to larger cohorts will help define comprehensive genomic aberrations in PTCs and validate target candidates. These new targets may open methods of individualized treatments adapted to the clinicopathologic specifics of the patients.

Artificial Intelligence in Cytopathology: A Neural Network to Identify Papillary Carcinoma on Thyroid Fine-Needle Aspiration Cytology Smears

Introduction

Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology.

Aim

The aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears.

Subjects and Methods

An ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 - nonpapillary carcinoma and 21 - papillary carcinoma, each photographed at two magnifications ×10 and ×40).

Results

The performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback.

Conclusion

With further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.

Insights into Current Tropomyosin Receptor Kinase (TRK) Inhibitors: Development and Clinical Application

The use of kinase-directed precision medicine has been heavily pursued since the discovery and development of imatinib. Annually, it is estimated that around ∼20 000 new cases of tropomyosin receptor kinase (TRK) cancers are diagnosed, with the majority of cases exhibiting a TRK genomic rearrangement. In this Perspective, we discuss current development and clinical applications for TRK precision medicine by providing the following: (1) the biological background and significance of the TRK kinase family, (2) a compilation of known TRK inhibitors and analysis of their cocrystal structures, (3) an overview of TRK clinical trials, and (4) future perspectives for drug discovery and development of TRK inhibitors.

p63 inhibits CD44+/CD24- cell proliferation and chemoresistance in papillary thyroid carcinoma cells

Thyroid cancer typically has a good prognosis; however, the risks of recurrence and chemoresistance associated with thyroid cancer remain a cause for concern. Papillary thyroid carcinoma (PTC) comprises 80% of all cases of thyroid carcinoma. A previous study reported that cluster of differentiation (CD) 44⁺/CD24⁻ PTC cells may contribute to PTC recurrence and chemoresistance; however, the underlying molecular mechanisms remain elusive. In the present study, CD44⁺/CD24⁻ cells were isolated from the TPC-1 PTC cell line and biological function assays revealed that CD44⁺/CD24⁻ cells were significantly more proliferative and chemoresistant compared with CD44⁻/CD24⁻ cells. Furthermore, the expression level of p63 was demonstrated to be negatively correlated with the expression of CD44 in PTC cells. The role of p63 in CD44⁺/CD24⁻ cell proliferation and chemoresistance was investigated and, the ectopic expression of p63 was observed to significantly inhibit CD44⁺/CD24⁻ cell proliferation and chemoresistance in vitro and in vivo. In conclusion, the present study indicated that CD44⁺/CD24⁻ cells contribute to PTC proliferation and chemoresistance and that the suppression of p63 in CD44⁺/CD24⁻ cells contributes to these effects.