Abstract
Lung cancers are among the most common invasive cancers worldwide and annually lead to high mortality and morbidity. Genomic alterations have been known to control the evolution of hallmarks of cancer in a dynamic way. These molecular alterations combined with epigenomic and post-genomic modifications contribute to formation of these neoplasms. Multiplicity of these changes has made development of personalized therapeutic regimens for these cancers a complex problem. Metabolic reprogramming is one of the main mechanisms in progression of cancers. There have been efforts to model the metabolic reprogramming in cancer using metabolic networks of cancer cells, but there has been no computational framework to model these metabolic transitions in cancer for precision and personalized medicine. We have combined computational, mathematical and experimental methodologies to develop a platform for precision oncology in non-small cell lung cancer (NSCLC) by in silico models of metabolic switches. Our integrative analysis of genomic data from NSCLC has led to discovery of genomic signatures controlling metabolic reprogramming in NSCLC with KRAS mutations. This discovery was proved in vivo and in vitro using drugs blocking different metabolic pathways. We have shown that NSCLC cells and tumors which carry KRAS mutations and have these genomic signatures are addicted to the pentose phosphate pathway (PPP). We have verified and proved the predictive value of these genomic signatures using Patient Derived Xenograft (PDX) tumor models of NSCLC. We are developing a mathematical and computational framework to model these metabolic switches. Our platform is capable of using genomic data from a cell line or tumor to determine the metabolic dependency of them quantitatively and predict the optimized personalized treatments for modulating metabolic pathways aiming to control cancer progression.
Citation Format: Iman Tavassoly, Ravi Iyengar. Metabolic reprogramming in non-small cell lung cancer: a precision oncology approach [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 5554. doi:10.1158/1538-7445.AM2017-5554