Abstract 566: A pathway based drug selection for cancer precision medicine

Abstract

Abstract Background and Purpose: Precision medicine tailors the right therapy to the right patient based on his/her tumor molecular profiles. In clinic and ongoing clinical trials, drug-targeted selection schemes depends more or less on drug targets’ mutations or expression status. However, there is a lack of systematic pathway based method to connect individual genomics information with properly drug target and treatment in precision medicine. The paper aims to integrate various knowledge-bases, including cancer drugs, drug-targets and gene-gene regulatory pathway, to construct a drug target and properly drug selection algorithm for individual patient in precision medicine. Materials and Methods: In the drug selection algorithm, drug targets and properly drugs recommendation are based on the patient specific molecular profile data, including copy number variation, mutation, and gene expression. Our target and drug selections are based on whether these drug targets act as hub genes that either regulate or control the signaling pathways for many other genes in the biological pathways. Only those patients whose molecular profiles do not show targets, the usual chemotherapy treatment will be recommended. This novel algorithm is applied to individual Pancreatic Adenocarcinoma (PAAD) for drug target and drug selection. All of patients’ genomic data obtained from the Cancer Genome Atlas (TCGA). The pathway information is from Pathway Commons and FDA approved cancer drugs and their targets is from DrugBank. The Cancer Cell Line Encyclopedia (CCLE) 46 pancreases cancer cell line are used to validate the algorithm result reliability. Results: Our algorithm identifies targets, such as ERBB2, CDK2, SRC, CDK9, SMAD2, CDK4, HDAC1, PPP1CA, AKR1B1, EGFR, IGF1R, AKT and MEK, for pancreatic adenocarcinoma cancer patients. In which, they cover the clinic first line effective drug targets, such as Gemcitabine for the Akt-mTOR signaling pathway (AKT) and CDK4/6 inhibitor and Erlotinib for HER2 Kinase Family (ERBB2, EGFR) activation inhibitor. In addition, new inhibitors include Src inhibitors (dasatinib, saracatinib and bosutinib), TGF beta inhibitor for target SMAD2 (galunisertib) and IGF-1R/insulin receptor inhibitors (ceritinib, brigatinib) are recommended accordingly and validated in pancreatic cancer cells. Conclusion: This novel algorithm might act as a better source for off-label drug selection and further cell line validations may help in providing a better treatment strategy in precision medicine. Note: This abstract was not presented at the meeting. Citation Format: Varshini Vasudevaraja, Lijun Cheng, Sai Mounika Inavolu, Milan Radovich. A pathway based drug selection for cancer precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 566. doi:10.1158/1538-7445.AM2017-566