A Physical Mechanism and Global Quantification of Breast Cancer

Chong Yu and Jin Wang

PLOS ONE, 2016, vol. 11, issue 7, 1-20

Abstract: Initiation and progression of cancer depend on many factors. Those on the genetic level are often considered crucial. To gain insight into the physical mechanisms of breast cancer, we construct a gene regulatory network (GRN) which reflects both genetic and environmental aspects of breast cancer. The construction of the GRN is based on available experimental data. Three basins of attraction, representing the normal, premalignant and cancer states respectively, were found on the phenotypic landscape. The progression of breast cancer can be seen as switching transitions between different state basins. We quantified the stabilities and kinetic paths of the three state basins to uncover the biological process of breast cancer formation. The gene expression levels at each state were obtained, which can be tested directly in experiments. Furthermore, by performing global sensitivity analysis on the landscape topography, six key genes (HER2, MDM2, TP53, BRCA1, ATM, CDK2) and four regulations (HER2⊣TP53, CDK2⊣BRCA1, ATM→MDM2, TP53→ATM) were identified as being critical for breast cancer. Interestingly, HER2 and MDM2 are the most popular targets for treating breast cancer. BRCA1 and TP53 are the most important oncogene of breast cancer and tumor suppressor gene, respectively. This further validates the feasibility of our model and the reliability of our prediction results. The regulation ATM→MDM2 has been extensive studied on DNA damage but not on breast cancer. We notice the importance of ATM→MDM2 on breast cancer. Previous studies of breast cancer have often focused on individual genes and the anti-cancer drugs are mainly used to target the individual genes. Our results show that the network-based strategy is more effective on treating breast cancer. The landscape approach serves as a new strategy for analyzing breast cancer on both the genetic and epigenetic levels and can help on designing network based medicine for breast cancer.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0157422

DOI: 10.1371/journal.pone.0157422

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