Association of human papillomavirus (HPV), p16, p53 and p63 expression with non-bilharzia-associated squamous cell carcinoma of the bladder and algorithm construction for histopathological grading prediction

Abstract

ABSTRACT Objective To investigate the expression of human papillomavirus (HPV), p16, p53, and p63 in non-schistosomiasis-related squamous cell carcinoma of the bladder and to develop an accurate and automated tool to predict histological classification based on clinicopathological features. Methods Twenty-eight patients with primary bladder pure squamous cell carcinoma who underwent cystectomy or transurethral resection of bladder tumor (TURBT) for bladder cancer between January 2011 and July 2017 were evaluated. Clinical data and follow-up information were obtained from medical records. Formalin-fixed, paraffin-embedded surgical specimens were used for immunohistochemical staining for p16, p53, and p63. Human papillomavirus detection was evaluated by PCR. Statistical analysis was performed, and statistical significance was set at p<0.05. Finally, decision trees were built to classify patients’ prognostic features. Leave-one-out cross-validation was used to test the generalizability of the model. Results Neither direct HPV detection nor its indirect marker (p16 protein) was identified in most cases. The absence of p16 was correlated with less aggressive histological grading (p=0.040). The positive p16 staining detection found only in pT1 and pT2 cases in our sample suggests a possible role for this tumor suppressor protein in the initial stages of bladder squamous cell carcinoma. The decision trees constructed described the relationship between clinical features, such as hematuria/dysuria, the level of tumor invasion, HPV status, lymphovascular invasion, gender, age, compromised lymph nodes, and tumor degree differentiation, with high classification accuracy. Conclusion The algorithm classifier approach established decision pathways for semi-automatic tumor histological classification, laying the foundation for tailored semi-automated decision support systems for pathologists.

Keywords

• Algorithms
• Urinary bladder neoplasms
• Human papillomavirus 16
• Human papillomavirus type 53
• Human papillomavirus 63
• Papillomaviridae
• Papillomavirus infections
• Machine learning
• Carcinoma, squamous cell