Human Protein Complex-Based Drug Signatures for Personalized Cancer Medicine

Disease signature-based drug repositioning approaches typically first identify a disease signature from gene expression profiles of disease samples to represent a particular disease. Then such a disease signature is connected with the drug-induced gene expression profiles to find potential drugs for the particular disease. In order to obtain reliable disease signatures, the size of disease samples should be large enough, which is not always a single case in practice, especially for personalized medicine. On the other hand, the sample sizes of drug-induced gene expression profiles are generally large. In this study, we propose a new drug repositioning approach (HDgS), in which the drug signature is first identified from drug-induced gene expression profiles, and then connected to the gene expression profiles of disease samples to find the potential drugs for patients. In order to take the dependencies among genes into account, the human protein complexes (HPC) are used to define the drug signature. The proposed HDgS is applied to the drug-induced gene expression profiles in LINCS and several types of cancer samples. The results indicate that the HPC-based drug signature can effectively find drug candidates for patients and that the proposed HDgS can be applied for personalized medicine with even one patient sample.