Aim and Objective: The number of anticancer drugs available currently is limited, and
some of them have low treatment response rates. Moreover, developing a new drug for cancer
therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known
cancer treatment compounds can speed up the development time and potentially increase the
response rate of cancer therapy. This study proposes a systems biology method for identifying new
compound candidates for cancer treatment in two separate procedures.
Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene
set enrichment analysis on the expression profile of responses to a compound. Second, survival
analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to
identify gene sets that are associated with cancer survival. A “cancer–functional gene set–
compound” network was constructed, and candidate anticancer compounds were identified. Through
the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172
putative compounds were obtained.
Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets
and then connect it to candidate compounds by using gene expression data from the Connectivity
Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association
with breast cancer prognosis and discussed six new compounds that can increase the expression of
the gene set after the treatment.
Conclusion: Our method can effectively identify compounds with a potential to be “repositioned”
for cancer treatment according to their active mechanisms and their association with patients’