CBCB scientists Eytan Ruppin and Rotem Katzir publish a paper in the Proceedings of the National Academy of Sciences (PNAS) on the quantitative identification of cancer SDLs in a model-based mechanistic manner

Wed Oct 14, 2015

CBCB faculty Eytan Ruppin and CBCB doctoral student Rotem Katzir, along with collaborators, published a paper titled “Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival” on September 29, 2015 in the journal Proceedings of the National Academy of Sciences (PNAS).

Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly.

In this paper, a network-level computational modeling framework is introduced that quantitatively predicts human SDLs in metabolism. The approach presented can be used to identify SDLs in species and cell types in which “omics” data necessary for data-driven identification are missing. As expected, the predicted SDLs are less frequently active in tumors to avoid lethality. Cancer tumors with more and stronger SDLs have smaller tumor size and lead to increased patient survival. Beyond facilitating the development of novel anticancer therapies, model-based identification of metabolic SDLs can be used to model pathogenic bacteria and provide leads to new antibiotic targets.

“Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival” article: http://www.pnas.org/content/112/39/12217.full