[Peritoneal recurrence prediction for colon cancer based on immunoexpression]

Peritoneal relapse as an isolated form of recurrence in colon cancer occurs in 25% of cases during the first two years subsequent to a curative colectomy. Currently, the diagnostic limitations of imaging studies and the absence of predictive scales for peritoneal recurrence warrant "second look" surgery in high-risk patients. The aim of this study is to assess features of some epithelial-mesenchymal transition biomarkers (c-Met, IGF-1R and plexin β1) in order to predict post-surgical peritoneal colonization and develop a mathematical model to predict carcinomatous relapse.

A retrospective study of the histopathological samples of 87 patients diagnosed with colon cancer who underwent radical resection was carried out, using immunohistochemical techniques for c-Met, IGF-1R and plexin β1. The patients were divided into two groups; those who had presented peritoneal recurrence and those who only had risk factors for this kind of relapse. Every stained sample was assessed by the rate of stained cells and immunostaining intensity. A possible association between immunohistochemical findings and peritoneal relapse was evaluated. Statistical analysis of the biomarkers with higher prognostic value allowed a risk mathematical formula to be developed based on coefficients, providing a specific value to each biomarker and patient.

c-Met expression in the primary tumour showed a high statistical trend (p: .074) while IGF-1 (p: .022) and plexin β1 (p: .021) revealed a significative association with peritoneal relapse. However, the multivariate analysis selected c-Met y plexin β1 as useful factors for a predictive mathematical model on peritoneal recurrence with a 75.8% sensitivity and 80.5% specificity in patients with a staining more than 50% for both biomarkers.

c-Met and plexin B1 overexpression is related to an increased risk of peritoneal relapse in cases of colon cancer where a radical resection is feasible. The encouraging outcomes of the proposed mathematical model may prove useful clinically in the identification of candidates for carcinoprophylaxis.