With an understanding of the heterogeneity of breast cancer, patients with luminal or HER2 breast cancer have more specific treatment options other than traditional chemotherapy, the standard therapy for triple-negative breast cancer (TNBC) patients. However, the response to current treatments as well as the prognosis have been clinical challenges. In fact, breast cancer consists of more than subtypes routinely used based on gene expression. In addition, gene expression is highly correlated with response to treatment and prognosis. This suggests that the development of personalized treatment with targeted therapy could improve the outcomes, especially for the TNBC subtype. To address this need, I used two approaches, the development of pathway-guided individualized treatment and an understanding of the interactions of potential genes for targeted therapy. Considering the complexity of gene and pathway interactions, the probability of pathway activation was predicted using pathway signatures generated by comparing gene expression differences between cells overexpressing interested genes and those expressing GFP. This approach was validated in two subtypes of mouse mammary tumors from MMTV-Myc mice, and then further validated in human TNBC patient-derived xenografts (PDXs). The inhibition of tumor growth in mouse mammary tumors and the regression of tumors in PDXs were observed. These proof-of-principle experiments demonstrated the flexibility of pathway-guided personalized treatment. Because this approach needs the combination of different targeted therapies, it is necessary to understand the characteristics of these targetedgenes and therapies, such as gene-gene interactions. To meet this demand, I studied the effects of Stat3 in Myc-driven tumors. Here, MMTV-Myc mice with conditional knockout Stat3 mice was generated. I noted that the deletion of Stat3 in MMTV-Myc mice accelerated the tumorigenesis as well as delayed the tumor growth with an alteration in the frequency of histological subtypes. These tumors also had deficient angiogenesis. Unexpectedly, mice with this genotype had lactation deficiencies and the lethality of pups was found.This model shared some of the same effects of loss of Stat3 in other oncogene-induced tumors and also had distinct effects compared with other models. This suggests that the oncogene drivers determine the roles of Stat3, an oncogene or tumor suppressor, and emphasizes again the importance of understanding the pathways and interactions in the development of treatment.In sum, these studies demonstrate the potential of guiding individualized treatments in preclinical platforms using bioinformatics analyses. Combined with other genomic profiles, this approach could offer more complete assessments before being translated to practice. In addition, this could be further applied in adaptive clinical trials through matching with mouse models.
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