We develop an experimental-computational approach that will accurately classify tumors, such that every single tumor can be mapped precisely and unambiguously according to its characteristics, namely the changes in gene structure (mutations) that it harbors. These mutations cause an imbalance in the activity network of the cancer cell. This network, which determines how how cancer cell behaves, represents a web of interactions between molecules in the cell that can turn each other off or on. Those interactions together generate a flow of information by which a tumor cell will make “decisions”, for example whether to die or to live.

Our algorithms allow us to identify unbalanced networks in tumor cells, for each particular patient.They also allow us to propose drugs that will target particular components of the network, in order to restore balance, and correct the processes, preferentially by killing the tumor cell.