Desmoplastic Reaction and Tumor Budding in Cervical Squamous Cell Carcinoma are Prognostic Factors for Distant Metastasis: A Retrospective Study

Cancer Manag Res. 2020 Jan 8:12:137-144. doi: 10.2147/CMAR.S231356. eCollection 2020.

Abstract

Purpose: An accurate risk assessment system for disease metastasis or recurrence could improve the cancer management practice in cervical squamous cell carcinoma (CxSCC) patients, which has few definite prognostic predictors. Previous studies have indicated the important utility of stromal features in determining cancer biological behavior; however, it lacks histopathologic or morphologic criteria for its evaluation. Therefore, this present study aimed to comprehensively catalog histopathological features of mesenchymal stroma to determine the prognostic value of these features in CxSCC.

Patients and methods: We histologically and immunohistochemically evaluated the stromal features in the primary tumors of 122 CxSCC patients. The follow-up duration was 41.25 months (range: 3-80.77 months). Multivariate proportional hazard regression models were used to identify the top classifier for distant metastasis-free survival (DMFS) prediction.

Results: Lymph-vascular invasion (LVI), lymph node metastasis (LNM), tumor-node-metastasis (TNM) stage and tumor budding were positively correlated with distant metastasis (P < 0.001, P < 0.001, P < 0.001 and P = 0.012, respectively). Distant metastasis was also associated with the immature desmoplastic reaction (DR) (P = 0.002), high level of cancer-associated fibroblasts (P = 0.003), vasohibin-1 (VASH1)-positive microvessels (P = 0.027), and the VASH1/CD31 ratio (P = 0.004). Multivariate COX proportional hazard regression models revealed that LVI, LNM, and DR were independent predictors of poor DMFS in CxSCC patients.

Conclusion: Primary tumor histologic stromal features, especially DR, may be useful in predicting distant metastasis in patients with CxSCC.

Keywords: desmoplastic reaction; distant metastasis; squamous cell carcinoma; stromal features.