A computational model of cancer metabolism that predicts genes that are essential for cancer-cell growth is reported in a study published online in Molecular Systems Biology. This study provides an important step towards more selective and more personalized cancer treatments.

Cancer cells cause disease, in part, because of their rapid, uncontrolled growth. But, in order to maintain this abnormal growth, the cells must adapt in special ways. In particular, they must alter their metabolism, i.e. the way they absorb, break down, and process the nutrients they need to live and grow.

By understanding these changes in cancer metabolism, researchers hope to identify drugs and therapies that specifically target and disrupt the growth of cancer cells while minimizing toxic effects on healthy tissues.

In addition, cancer-related changes in metabolism vary between different cancer types, and even between patients, so therapies need to be tailored to specific cancer types or even for each cancer patient.

Tomer Shlomi, Eytan Ruppin and colleagues develop a computer simulation of cancer metabolism and use it to predict genes that are essential for cancer cell growth.

They show that by targeting multiple points within cellular metabolism, potentially potent therapeutic combinations can be identified. Importantly, when specific metabolic genes are already known to be turned off within a particular cancer, the researchers show that they can predict treatments that selectively target that cancer without disrupting the metabolism of healthy tissues, leading to the potential of new cancer therapies with lesser side effects.




Source:www.nature.com/msb/

Article: DOI: 10.1038/msb.2011.35