Mathematical modeling of the interactions between cellular programs in response to oncogene inactivation: Incorporation of both cell intrinsic and cell extrinsic (Immune mediated) effects
{"title":"Mathematical modeling of the interactions between cellular programs in response to oncogene inactivation: Incorporation of both cell intrinsic and cell extrinsic (Immune mediated) effects","authors":"Chinyere Nwabugwu, K. Rakhra, D. Felsher, D. Paik","doi":"10.1109/BIBE.2012.6399720","DOIUrl":null,"url":null,"abstract":"Some tumors' addiction to the over-expression of a single oncogene provides a weakness for a molecularly targeted therapy to exploit. A number of targeted therapy drugs (e.g., imatinib, erlotinib, etc.) produce dramatic tumor regression but are often eventually followed by tumor relapse. Understanding the complex interaction of mechanisms behind the tumor's overall response to oncogene inactivation is key to preventing tumor relapse. It is becoming clear that there is a general sequence of cellular responses and that the latter immune-mediated steps are key to eliminating residual disease. What is still unclear is exactly how these mechanisms work together to produce the overall tumor regression response. We have built a novel integrative computational model that includes the various cellular response mechanisms of tumors to oncogene inactivation. This consists of cell intrinsic programs (e.g., apoptosis, proliferation, differentiation, dormancy) and immune-mediated cell extrinsic programs (e.g., senescence, inhibition of angiogenesis). Our model unifies these different response programs in order to allow for predictions of the time-course of events following oncogene inactivation and their impact on tumor burden. Eventually, we will use this model to make and validate predictions of the effect of various therapeutic strategies and interventions (e.g., adjunct chemotherapy, immune system modulation). These predictions will be validated in vivo using conditional expression transgenic mouse models of MYC-induced lymphoma, osteosarcoma, and hepatocellular carcinoma.","PeriodicalId":330164,"journal":{"name":"2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2012.6399720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Some tumors' addiction to the over-expression of a single oncogene provides a weakness for a molecularly targeted therapy to exploit. A number of targeted therapy drugs (e.g., imatinib, erlotinib, etc.) produce dramatic tumor regression but are often eventually followed by tumor relapse. Understanding the complex interaction of mechanisms behind the tumor's overall response to oncogene inactivation is key to preventing tumor relapse. It is becoming clear that there is a general sequence of cellular responses and that the latter immune-mediated steps are key to eliminating residual disease. What is still unclear is exactly how these mechanisms work together to produce the overall tumor regression response. We have built a novel integrative computational model that includes the various cellular response mechanisms of tumors to oncogene inactivation. This consists of cell intrinsic programs (e.g., apoptosis, proliferation, differentiation, dormancy) and immune-mediated cell extrinsic programs (e.g., senescence, inhibition of angiogenesis). Our model unifies these different response programs in order to allow for predictions of the time-course of events following oncogene inactivation and their impact on tumor burden. Eventually, we will use this model to make and validate predictions of the effect of various therapeutic strategies and interventions (e.g., adjunct chemotherapy, immune system modulation). These predictions will be validated in vivo using conditional expression transgenic mouse models of MYC-induced lymphoma, osteosarcoma, and hepatocellular carcinoma.