Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases. Academic Article uri icon

Overview

abstract

  • A new type of signaling network element, called cancer signaling bridges (CSB), has been shown to have the potential for systematic and fast-tracked drug repositioning. On the basis of CSBs, we developed a computational model to derive specific downstream signaling pathways that reveal previously unknown target-disease connections and new mechanisms for specific cancer subtypes. The model enables us to reposition drugs based on available patient gene expression data. We applied this model to repurpose known or shelved drugs for brain, lung, and bone metastases of breast cancer with the hypothesis that cancer subtypes have their own specific signaling mechanisms. To test the hypothesis, we addressed specific CSBs for each metastasis that satisfy (i) CSB proteins are activated by the maximal number of enriched signaling pathways specific to a given metastasis, and (ii) CSB proteins are involved in the most differential expressed coding genes specific to each breast cancer metastasis. The identified signaling networks for the three types of breast cancer metastases contain 31, 15, and 18 proteins and are used to reposition 15, 9, and 2 drug candidates for the brain, lung, and bone metastases. We conducted both in vitro and in vivo preclinical experiments as well as analysis on patient tumor specimens to evaluate the targets and repositioned drugs. Of special note, we found that the Food and Drug Administration-approved drugs, sunitinib and dasatinib, prohibit brain metastases derived from breast cancer, addressing one particularly challenging aspect of this disease.

publication date

  • October 4, 2013

Research

keywords

  • Breast Neoplasms
  • Drug Repositioning
  • Models, Biological

Identity

PubMed Central ID

  • PMC4005386

Scopus Document Identifier

  • 84885979840

Digital Object Identifier (DOI)

  • 10.1158/0008-5472.CAN-12-4617

PubMed ID

  • 24097821

Additional Document Info

volume

  • 73

issue

  • 20