Optimal targeted therapy for multiple cancers based on contrastive Notch signaling networks.

Abstract

Over decades, cancer understanding has advanced significantly at molecular and cellular levels, leading to various therapies based on intra-/inter-cellular networks. Despite this, cancer still remains a leading cause of death globally. The Notch signaling pathway, a crucial intercellular network in many cancers, has been extensively studied and therapies targeting the Notch pathway also have been well-studied based on inhibiting various stages of Notch activation. Nonetheless, the unclear pathophysiological mechanisms of metastasis, responsible for about 90% of cancer deaths, complicate treatment development. For example, the role of Notch signaling varies between cancers; in non-small cell lung cancer, Notch1 and Notch2 exhibit opposing effects compared to their roles in embryonal brain tumors. This suggests that a single targeted therapy to Notch signaling could produce opposing effects in the metastatic state, necessitating a more careful selection of therapies. To address this, we considered a scenario involving multiple cancers with contrasting Notch signaling pathways. We developed two mathematical models and explored optimal targeted therapies for reducing cancer cells in the metastatic state of two types of cancers with these contrasting pathways. From the in silico tests of existing Notch-targeted therapies and newly suggested therapies in this study, we found that multiple cancers with contrasting Notch networks can be controlled by one common targeted signal network. Furthermore, combination therapies enhancing Notch production may be most effective in early-stage cancer, whereas cleavage therapies may prove more effective in late-stage cancer. We also found that the order of multiple targeted therapies significantly affects treatment effectiveness and should be a key consideration. Our study proposes that optimal treatment should take into account the cancer stage, with careful selection and sequencing of medication therapies.