Modelling molecular mechanisms of cancer pathogenesis: Virtual patients, real opportunities

Abstract

A combination of decades of cancer research and trillions of dollars has helped to exponentially increase our understanding of the molecular processes involved in cancer pathogenesis [1, 2] and to develop new cancer drugs. However, despite progress in diagnosing and treating cancer, these diseases remain one of the leading causes of morbidity and mortality worldwide, responsible for millions of deaths each year [3]. Moreover, we are still faced, on average, with low drug response rates, very serious treatment side effects and questionable survival benefits. In Europe alone, cancer kills around 4000 people every day [4] and costs billions per year [5]. Worldwide, the number of new cancer cases is increasing every year [4], at least partly due to rapidly ageing populations [6], with the number of new cancer cases projected to reach 25 million per year by 2030, and cure rates for many common forms of cancer stagnating [4]. Due to the high number of nonresponders to existing drugs and spiralling costs of new cancer drugs-costs have almost doubled in the last decade, resulting in a dramatic decrease in the number of new drugs-an individualised approach to the diagnosis and treatment of cancer patients is desperately required.