Abstract A36: Systems biology: Implications in assessing drug-drug interactions in pancreatic cancer

Abstract

Abstract The era of new adjuvant targeted therapies (k-ras, EGFR, VEGF src, p53, etc.) has generated much interest in discovering better approaches for the treatment of cancers; however such approaches have met with more failure than success. The major reason for low response is related to incomplete understanding and validation of the specific molecular targets at the gene level along with complexities of genetic and epigenetic changes in cancer. This is further complicated due to redundancies and cross-talk in signaling pathways that may explain the failure of single-pathway targeted therapies. To overcome this barrier, one must fully understand the molecular interactions among key signaling pathways. This requires innovative approaches to identify genomic events to guide intelligent decision making in the design of novel therapeutic strategies for combination therapies to improve treatment outcome. An increasingly popular and scientifically robust method is to utilize integrated systems biology which combines the use of high throughput technologies with analysis and computer modeling of the potential interconnecting pathways modulated in response to drug treatments. Recently we have found that specific, orally active murine double minute 2 (MDM2) inhibitor MI-219 can synergize with chemotherapeutic drug oxaliplatin that results enhanced anti-tumor effects in pancreatic cancer. However, the mechanism behind this synergy has not been explored in greater detail. In this study we have used integrated genomic expression microarray profiling (IGEMP) coupled with pathway/genetic network modeling (PGNM) to obtain crucial information in the synergistic efficacy of MI-219-oxaliplatin in a genetically complex and by far incurable pancreatic cancer. Global gene analysis of capan-2 cell shows that MI-219 alone induces alterations in 48 genes that is expected of a targeted agent. On the other hand, oxaliplatin treatment results in alterations of more than 700 genes and is in line with the mode of action of cytotoxic drugs. However, the combination of MI-219-oxaliplatin resulted in alteration of >700 genes with emergence of 286 synergy unique genes. Principal component analysis revealed that each drug treatment and time point had individual global gene signatures. Further analysis of the 286 synergy unique genes revealed the presence of several local gene networks of MDM2-p53 pathway including key players such as players CREBBP (i.e., ubiquitously expressed in PC and is involved in the transcriptional coactivation of many different transcription factors including p53, CARF that is responsible for p53 stability along with NF-kB and EGR1 tumor suppressor module, all positively affecting p53 reactivation that in principle would drive cells toward increased apoptosis. Additionally, the analysis revealed a novel role of hepatocyte nuclear factor alpha (HNF4α) in aiding MI-219-oxaliplatin mediated apoptosis suggesting that this technology can also be utilized in identifying potential biomarkers. Most interestingly, all the genetic signatures could be verified and validated at the RNA and protein level. Fortified by our strong evidence we propose the use of this technology for evaluating multiple drug combinations being tested for different cancers in clinical setting. We believe that success in personalized medicine will require advances in our understanding of the true potential and concepts of this technology towards elucidating the role of most influential driver genes in drug-drug interaction that would positively impact treatment outcome. Citation Information: Clin Cancer Res 2010;16(14 Suppl):A36.