The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear.A number of studies have showed that the heterogeneous responses to anticancer treatments of patients are partly associated with their specific changes in gene expression and somatic alterations.The emerging large-scale pharmacogenomic data provide us valuable opportunities to improve existing therapies or to guide the early-phase clinical trials of the compounds under development.However,how to identify the underlying combinatorial patterns among the data is still a challenging issue.In this talk,we will report a sparse network-regularized partial least square (SNPLS) method to identify joint modular patterns using gene-expression,drug-response data and a gene network.We demonstrated the effectiveness of SNPLS on a set of simulation data and applied it to a real biological data across 641 cancer cell lines consisting of diverse tissue types of human cancers.We found that the gene-drug modular patterns provide us new insights into the molecular mechanisms of how drugs act and suggest new drug targets for therapy of certain types of cancers.