The paradigm of personalized medicine is the identification of the appropriate drug for the right patient, using molecular profiles. In Oncology, it is well established that the anticancer drugs are effective in only a small subset of patients. Moreover, many of the new targeted therapies inhibit specific proteins, and they are only effective in tumors that are genetically altered. Consequently, the success of personalized treatment depends on each individual molecular profile, which a priori can be considered as very heterogeneous.
Here, we present a new computational approach (PanDrugsDB) based on the analysis and integration of genomic data (mutations, copy number variations or gene expression levels), with functional data (proteins essentiality) and pharmacological data. This tool is able to identify those molecular alterations that drive tumor progression and could be druggable based on the patient's molecular profile, and propose an individualized therapeutic strategy to guide clinical decision making for cancer patients. We have tested this approach, in publicly available data (ICGC and TCGA cancer genome projects) and patient's tumor genomic data that are analyzed in our institution as part of CNIO Personalized Medicine initiative.
Citation Format: Elena Piñeiro-Yáñez, Miguel Reboiro-Jato, Javier Perales-Patón, Daniel Glez-Peña, Héctor Tejero, Takeshi Shimamura, Julián Carretero, Alfonso Valencia, Manuel Hidalgo, Fátima Al-Shahrour. PanDrugsDB: Identifying druggable genetic dependencies for personalized cancer therapy. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A22.
- ©2015 American Association for Cancer Research.