Decoding a gene expression program that accompanies the phenotype of sporadic and Basal Cell Nevus Syndrome-associated odontogenic keratocyst

Immunohistochemical evaluation of cyclin D1 and p63 in odontogenic keratocyst and unicystic ameloblastoma

INTRODUCTION

Odontogenic keratocyst (OKC) and unicystic ameloblastoma (UA) are lesions of odontogenic origin. Both lesions are morphologically cysts. However, they are classified as developmental cysts and epithelial odontogenic tumours, respectively. Cyclin D1 (CCD1) dysregulation is associated with oncogenic activity and malignancies, while tumour protein p63 (p63) alterations are associated with tumourigenesis.

AIM

To evaluate and compare the protein expression of CCD1 and p63 in sporadic OKC (OKC-sp), syndromic OKC (OKC-sy), and UA.

MATERIAL AND METHODS

45 cases from the Anatomical Pathology Department, Faculty of Dentistry, University of Chile were analysed and divided into groups: OKC-sp (n=15), OKC-sy (n=15) and UA (n=15), the latter categorised into intraluminal and/or luminal (n=7) and mural (n=8). Immunohistochemical staining for CCD1 and p63 proteins was performed from paraffin-embedded sections. Statistical analysis included the Shapiro-Wilk test, one-way ANOVA with Tukey's multiple comparisons, and Spearman's correlation coefficient (p<0.05).

RESULTS

There was an involvement mainly in women in the mandibular area, and a high frequency of jaw expansion, especially in the mural UA. P63 protein expression was higher than CCD1 in all cystic lesions, particularly in mural UA (p<0.001). No correlation was found between CCD1 and p63 expression.

CONCLUSION

P63 may serve as a valuable marker for evaluating cell proliferative activity in odontogenic cystic lesions, providing insights into the aggressive behaviour of mural UA.

Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.