Martin Seeger, James Longden, Edda Klipp and Rune Linding*
According to the WHO, cancer is the second most common cause of death worldwide. The social and economic damage caused by cancer is high and rising. In Europe, the annual direct medical expenses alone amount to more than €129 billion. This results in an urgent need for new and sustainable therapeutics, which is currently not met by the pharmaceutical industry; only 3.4% of cancer drugs entering Phase I clinical trials get to market. Phosphorylation sites are parts of the core machinery of kinase signaling networks, which are known to be dysfunctional in all types of cancer. Indeed, kinases are the second most common drug target, yet, because these inhibitors block all functions of a protein, they commonly lead to the development of resistance and increased toxicity. To facilitate global and mechanistic modeling of cancer and clinically relevant cell signaling networks, the community will have to develop sophisticated data-driven deep-learning and mechanistic computational models that generate in silico probabilistic predictions of molecular signaling network rearrangements causally implicated in cancer.
deep learning, cancer, causality, signaling, SOPs, networks
Humboldt-Universität zu Berlin, Theoretical Biophysics, Invalidenstr. 42, 10115 Berlin, Rewire Tx, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Humboldt-Universität zu Berlin, Theoretical Biophysics, Invalidenstr. 42, 10115 Berlin, Humboldt-Universität zu Berlin, Theoretical Biophysics, Invalidenstr. 42, 10115 Berlin