RCAS1 as a tumour progression marker: an independent negative prognostic factor in gallbladder cancer

Receptor-binding cancer antigen expressed on SiSo cells (RCAS1) induces apoptosis in immune cells bearing the RCAS1 receptor. We sought to determine RCAS1 involvement in the origin and progression of gallbladder cancer, and also implications of RCAS1 for patient survival. RCAS1 expression was examined immunohistochemically in 110 surgically resected gallbladder specimens. The gallbladders represented 20 cases of cholecystitis with no associated pancreaticobiliary maljunction; 23 cases of cholecystitis with pancreaticobiliary maljunction; 14 cases of adenomyomatosis; 7 adenomas; and 46 cancers. High expression of RCAS1 (immunoreactivity in over 25% of cells) was observed in 32 of the 46 cancers (70%), but not in other diseases, including pre-cancerous conditions. RCAS1 immunoreactivity was associated with depth of tumour invasion (P = 0.0180), lymph node metastasis (P = 0.0033), lymphatic involvement (P = 0.0104), venous involvement (P = 0.0224), perineural involvement (P = 0.0351) and stage by the tumour, nodes and metastases (TNM) classification (P = 0.0026). Thus, RCAS1 expression may be a relatively late event in gallbladder carcinogenesis possibly promoting tumour progression. Cox regression multivariate analysis demonstrated RCAS1 positivity to be an independent negative predictor for survival (P = 0.0337; risk ratio, 12.690; 95% confidence interval, 1.216–132.423). High expression of RCAS1 significantly correlated with tumour progression and predicted poor outcome in gallbladder cancer. © 2001 Cancer Research Campaign http://www.bjcancer.co

An exactly solvable, spatial model of mutation accumulation in cancer

One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model