IRS1 phosphorylation underlies the non-stochastic probability of cancer cells to persist during EGFR inhibition therapy.

Stochastic transition of cancer cells between drug-sensitive and drug-tolerant persister phenotypes has been proposed to play a key role in non-genetic resistance to therapy. Yet, we show here that cancer cells actually possess a highly stable inherited chance to persist (CTP) during therapy. This CTP is non-stochastic, determined pre-treatment and has a unimodal distribution ranging from 0 to almost 100%. Notably, CTP is drug specific. We found that differential serine/threonine phosphorylation of the insulin receptor substrate 1 (IRS1) protein determines the CTP of lung and of head and neck cancer cells under epidermal growth factor receptor inhibition, both in vitro and in vivo. Indeed, the first-in-class IRS1 inhibitor NT219 was highly synergistic with anti-epidermal growth factor receptor therapy across multiple in vitro and in vivo models. Elucidation of drug-specific mechanisms that determine the degree and stability of cellular CTP may establish a framework for the elimination of cancer persisters, using new rationally designed drug combinations.

Dynamic patterns of information flow in complex networks.

Although networks are extensively used to visualize information flow in biological, social and technological systems, translating topology into dynamic flow continues to challenge us, as similar networks exhibit fundamentally different flow patterns, driven by different interaction mechanisms. To uncover a network's actual flow patterns, here we use a perturbative formalism, analytically tracking the contribution of all nodes/paths to the flow of information, exposing the rules that link structure and dynamic information flow for a broad range of nonlinear systems. We find that the diversity of flow patterns can be mapped into a single universal function, characterizing the interplay between the system's topology and its dynamics, ultimately allowing us to identify the network's main arteries of information flow. Counter-intuitively, our formalism predicts a family of frequently encountered dynamics where the flow of information avoids the hubs, favoring the network's peripheral pathways, a striking disparity between structure and dynamics.