Network Modeling of Drug Resistance and Efficacy in Cancer Therapy

Abstract

The research outlined in this proposal has three objectives: (i) to model biological pathways involved in cancer using data and prior knowledge; (ii) to investigate the mechanism of action for drugs that are used in cancer therapy; and (iii) to predict the efficacies of drug combinations for effective cancer therapy.

Several of the chemotherapeutic drugs available in the market today target the genes belonging to the cell proliferation or cell survival pathways. However, cancer cells manage to evade death despite being treated by these drugs. The ability of cancer cells to resist chemotherapy is called drug resistance or chemoresistance. Design of cancer therapy involves identifying potential key intervention points in the cell signaling pathways and looking for drug cocktails that could be effective in controlling these points. By targeting the molecules that sensitize cancer cells to cell death, we can devise a strategy to overcome drug resistance.

We employ mathematical modeling and simulation to first, demonstrate how drug resistance occurs in cancer cells and second, which drugs or combinations of drugs can sensitize the cells and achieve robust cell killing.

We modeled the biological pathways instrumental in metastatic melanoma, osteosarcoma and glioblastoma as Boolean networks. STAT3, a signal transducer and activator of transcription factor was identified as an important intervention point in cancer cell signaling pathways. We were able to verify that the inhibition of STAT3 is crucial in order to increase sensitivity of the melanoma and osteosarcoma cells to cell death. The Chinese herbal drug, Cryptotanshinone was chosen since it is known to be an effective STAT3 inhibitor. We predicted the efficacies of different drug combinations used in the treatment of the three cancers.